INDEX

Title Page No

Index 1

Abstract 2

1. Introduction

1.1 Sheet Metal Forming 3

1.2 Deformation Process 4

1.3 Cutting Process 6

2. Bending Die 9

2.1 Introduction

2.2 Classification of Die Components

2.3 Die Components 10

2.4 Types of Bending Die 16

2.5 Expert System 20

2.6 Problems in Traditional Process of Die Design 28

3. Expert System Development Procedure, Methodologies, and its Applications 29

4. Literature Review 21

5. Conclusion 27

References

Abstract

1

In sheet metal forming different types of manufacturing processes are used. Bending is

one of most commonly used manufacturing process in metal forming industries. Traditional

methods of bending die design require expertise and are largely manual and tedious. Die

designer requires a high level of knowledge that can only be achieved through years of

practical experience Development of die, design and selection of components and accessories,

selection of die material and die modelling are major activities involve in die design. Hence

the design of bending die is a complex, experience based and time consuming task. So it’s a

major loss for small scale industries and further more existing software’s presently used for

die design helpful for drawing assistance and simple calculations. Therefore an intelligent

system is required for bending die design. Development of such system can prove a landmark

to ease the complexities involved in the activities of bending die design.

A bending die consist of different components like die block, punch, stripper plate,

shank, punch holder plate, bolster plate, back plate, die set and fasteners. And

selection/design of these components is a vital role. Hence objective of this research work is

to develop a knowledge based system for selection of major bending die components. The

proposed system uses production rule based approach of artificial intelligence consisting of

different modules. Development of such system has different steps to follow like knowledge

acquisition, framing of production rules, verification of rules, and selection of knowledge

representation language, identification of hardware, development of knowledge base and

construction of user interface. Here production rules are coded in the Auto LISP language

and user interface is created in visual basic 6 on AutoCAD platform. This arrangement

facilitates interfacing of design process with modelling and can be operated on a PC/AT.

As this system can be operated on AutoCAD software it is a less costly and more

effective in small and medium scale enterprises. The proposed System is enough flexible

hence it can be improved, modified or edited at any of the stage in future.

Chapter 1 Introduction

2

Sheet Metal Forming

Sheet metals are widely used for industrial and consumer parts because of its capacity for

being bent and formed into intricate shapes. Sheet metal parts comprise a large fraction of

automotive, agricultural machinery, and aircraft components as well as consumer appliances.

Successful sheet metal forming operation depends on the selection of a material with

adequate formability, appropriate tooling and design of part, the surface condition of the

sheet material, proper lubricants, and the process conditions such as the speed of the forming

operation, forces to be applied, etc. A numbers of sheet metal forming processes such as

shearing, bending, stretch forming, deep drawing, stretch drawing, press forming,

hydroforming etc. Each process is used for specific purpose and the requisite shape of the

final product [1, 2].

1.1 Sheet forming operations

The sheet metal forming processes can be classified broadly into two areas:

1) Deformation Processes and 2) Cutting processes

The Deformation processes involves partial or complete plastic deformation of the work

material Bending, Twisting, Curling, Deep Drawing, Spinning, Stretch Forming, Necking,

Bulging, Flanging etc. are major deformation Processes.

The cutting process incorporate cutting materials by subjecting it to shear stresses usually

between punch and die may be of any shape, or between the blades of shear. Shearing,

Blanking, Punching, Parting, Lancing, Shaving etc. are major cutting processes [3].

Production of sheet metal components may require a combination of above categorized

forming processes. A brief introduction of major sheet metal forming processes is given

below.

1.2 Deformation process

1.2.1 Shearing

3

Irrespective of the size of the part to be produced, the first step involves cutting the sheet into

appropriate shape by the process called shearing. Shearing is a generic term which includes

stamping, blanking, punching etc. Figure 1.1 shows a schematic diagram of shearing. When a

long strip is cut into narrower widths between rotary blades, it is called slitting. Blanking is

the process where a contoured part is cut between a punch and die in a press. The same

process is also used to remove the unwanted part of a sheet, but then the process is referred to

punching.

Figure 1.1 Shearing [4]

1.2.2 Stretch Forming

It is a method of producing contours in sheet metal. In a pure stretch forming process, the

sheet is completely clamped on its circumference and the shape is developed entirely at the

expense of the sheet thickness. Figure 1.2 presents a schematic set-up of stretch forming

process. The die design for stretch forming is very crucial to avoid defects such as excessive

thinning and tearing of the formed part. The stretch forming process is extensively used for

producing complex contours in aircraft and automotive parts [4].

Figure 1.2.2 Stretch forming processes [4]

1.2.3 Deep Drawing

Deep drawing is a sheet metal forming process in which a sheet metal blank is radially drawn

4

into a forming die by the mechanical action of a punch. It is thus a shape transformation

process with material retention. The process is considered "deep" drawing when the depth of

the drawn part exceeds its diameter. This can be achieved by redrawing the part through a

series of dies [2, 3].

The metal flow during deep drawing is extensive and hence, requires careful administration

to avoid tearing or fracture and wrinkle. Following are a few key issues affecting metal flow

during deep drawing process and each of them should be considered when designing or

troubleshooting sheet metal deep drawing stamping tools.

1.2.4 Bending

Bending is defined as straining of metal around straight axis, during this process the metal on

the inside of the neutral axis is compressed, while the metal on the outside of the neutral axis

is stretched [2].

Figure 1.3 simple bending [1].

Bending is done using Press Brakes. Press Brakes can normally have a capacity of 20 to 200

tons to accommodate stock from 1m to 4.5m (3 feet to 15 feet). Larger and smaller presses

5

are used for diverse specialized applications. Programmable back gages, and multiple die sets

currently available can make bending a very economical process [1].

1.3 Cutting Processes

1.3.1 Blanking

It is a simple cutting operation as shown in figure 1.3.1. The material used is called the stock

and is generally a ferrous or nonferrous strip. During the working stroke the punch goes

through the material, and on the return stroke the material is lifted with the punch and is

removed by the stripper plate. Stop pin is used here as gage for operator. Here in blanking the

part that is removed from the strip is always the work-piece (blank) in a blanking operation.

Subsequent press-working operations may be performed on the blank [3].

Figure 1.3.1 Blanking & Piercing die []

1.3.2 Piercing

This operation consists of simple hole punching. It differs from blanking in that the punching

(or material cut from stock) is the scrap and the strip is the work piece. Piercing is nearly

always accompanied by a blanking operation before, after, or at the same time. Figure 1.3.1

shows the typical blanked and pierced work piece [1].

1.3.3 Lancing

This is a combine bending and cutting operation along a line in the work material. No metal

is cut free during a lancing operation. The punch is designed to cut on two or three sides and

bend along the fourth side [3].

6

Figure 1.3.2 cutting & parting operation [Suchy, 2006]

1.3.4 Cutting off and parting

A cut-off operation separates the work material along a straight line cut. When the operation

separates the work material along a straight line cut in a double line cut, it is known as

parting. Cutting off and parting operations are used to separate the work piece from the scrap

strip. Cutting off and parting usually occurs in the final stages of progressive die [3]. Figure

1.3.2 shows a cutting off operation.

1.3.5 Notching

This operation removes metal from either or both edges of the strip. Notching serves to shape

the outer contour of the work piece in a progressive die or remove excess metal before a

drawing or forming operation in a progressive die. The removal of excess metal allows the

metal to flow or form without interference from excess metal on the sides [3]. Figure 1.3.3

shows a notching operation.

Figure 1.3.3. Notching [3]

7

1.3.6 Shaving

Shaving is a secondary operation, usually following punching, in which the surface of the

previously cut edge is finished smoothly to accurate dimensions. The excess metal is

removed much as a chip is formed with a metal cutting tool. There is very little clearance

(close to zero) between the punch and die, and only thin section of edge is removed from the

edge of the work piece [3, 4]. Below figure shows the shaving operation.

Figure 1.3.4 (a) Shaving (b) Perforating

1.3.7 Perforating

This is a process by which multiple holes which are very small and close together is cut in

flat work material [3].

1.3.8 Trimming

This operation removes the distorted excess metal from drawn shapes and also provides a

smooth edge [3].

8

Chapter 2 Bending Die

2.1 What is bending die?

A bending die is a specialized tool used in manufacturing industries to shape material using a

press. Products made with bending dies range from simple paper clips to complex pieces used

in advanced technology. It is an assembly of number of components, according to the shape

of the part to be produce die type is selected. Figure 2.1 shows the simple bending die and its

components [1].

Figure 2.1 Simple Bending Die [1]

2.2 Classification of die components -

According to the function of the die, all components may be classified into two groups:

a) The technological components directly participate in forming the work piece, and they

have direct contact with a material; examples are the punches, die block, guide rails, form

block, drawing die, stripper, blank holder, etc.

b) The structural components' securely fasten all components to the subset and die set. They

include the punch holder, the die shoe, the shank, the guideposts, the guidepost bushings, the

springs, screws, dowels, etc. [3].

9

2.3 Bending Die components

The main components for Die Tool sets are:

Die block – A die block is a construction component that houses the opening and

receives punches. These die openings may be machined from a solid block of tool

steel or may be made in sections. .The die block is predrilled, tapped, and reamed,

before being fastened to the die shoe. Die holder is thicker than the punch holder to

compensate for weakening effect of slug and blank holes. Common proportions for

small and medium size dies are Punch holder thickness 1.25 inch, Die holder

thickness 1.5 inch. It is made up of OHNS, high-quality steel, hardened and precision

ground to exact size with hardness of 56HRC.

Figure 2.3.1 Die block [3]

Punch plate – It is mounted to the upper shoe in much the same manner as the die

block. It is made from the hardened tool steel; it may consist of single piece of steel or

be sectioned. It holds all punches, pilots, spring pad, and other components of die. It is

separated from die shoe by back up plate. Usually punch plate is attached directly to

the press attachment ram and the die holder to the press attachment. This necessitates

the use of the same press attachment each time the job is run. It will also speed set up

time by eliminating the need for aligning the punches to the die sections. The punch

plate is designed, dimensioned, and manufactured similarly to the die block [4].

10

Figure 2.3.2 Punch plate [3]

Stripper plate - This is used to hold the material down on the Blank/ Pierce Die and

strip the material off the punches. Material of the stripper plate must be ground on

both sides and perfectly square. Stripper plates may be made of cold rolled steel if

they are not to be machined except for holes. When machining must be applied to

clear gages. Plates should be made from machine steel with hardness of HRC 35-38.

Figure show two types of stripper plates used. Figure 2.3.3 shows the two different

types of stripper plates (a) Stationary Stripper plate (b) Spring stripper plate.

Figure 2.3.3 Stripper plate [4]

Punch – punch tooling is made from hardened steel or tungsten carbide. A die is

located on the opposite side of the work piece and supports the material around the

perimeter and helps to localize the bending forces. There is a small amount of

clearance between the punch and the die to prevent the punch from sticking in the die

11

so that less force is needed to make the hole. Depending on the shape of part to be

produce different types of punches are used, like plain punch, pedestal punch, V type

punch, special purposed etc. The main considerations when designing punches are, 1)

they should be design so that they do not buckle. 2) They should be strong enough to

withstand the stripping force. Standard punch material is SKD11. Expected hardness

thru heat treatment is approximately 60HRC

Figure 2.3.4 Punch Mounting [2]

Guide Post - Both die shoes, upper and lower, are aligned via guide pins or guide

posts. These provide for a precise alignment of the two halves during the die

operation. The guide pins are made of ground, carburized, and hardened-tool steel,

and they are firmly embedded in the lower shoe. The upper shoe is equipped with

bushings into which these pin slip-fit. Figure 2.3.5 (C) shows guide post.

Figure 2.3.5 Die set arrangement [3]12

Punch holder - The upper working member of the die set is called the punch holder.

The name is easy to remember because of its relationship with the punches, which are

normally applied above the strip and fastened to the underside of the punch holder. A

punch holder also serves as supporting the rigidity of the top die. It is also a function

of the punch holder to support rigidity of upper dies. In upper die structure which has

springs, we need to adjust the holder thickness according to spring length. If having

difficulty attaching upper die to press machine just by the shank, you may use punch

holder to attach. Punch holder is made up of cast iron or of steel.

Figure 2.3.6 Schematic diagram of Punch holder [4].

Shank – Upper shoe is sometimes provided with shank by which the whole tool is

clamped to the ram of the press. Dies with large in weight are secured to the ram by

clamps or bolts. However, sometimes even large die sets may contain the shank,

which in such a case is used for centring of the tool in the press. It is a pillar-shaped

part; used for attachment of relatively small upper dies (dies used on stamping

machines up to 30t capacity) to the slide of stamping machines. The size of the shank

depends on the mounting dimensions of the press the die is intended for. Standard

shank diameters are 25, 32, 38, and 50mm. Shank length is usually ranging from 50～

65mm. usual choice of materials is SS400 or S50C and its equivalent, FC250 types [2,

3]. Figure 2.3.7(a) shows the relation between shank and punch holder plate.

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Figure 2.3.7(a) Shank Position [6]

Figure 2.3.7 (b) Types of shank [6]

Bolster plate – Bolster plate, sometimes called press table, is positioned on top of the

press bed. It is a heavy plate, ribbed with T slots (to receive T bolts in the assembly of

a die), precision aligned to the frame with dowel pins. Wear occurring on the press

bed is high; the bolster plate is incorporated to take this wear it is attached to the press

bed and die shoe attached to it. Bolster plate thickness varies from 25mm to 75mm.

The material for bolster plate is a good quality steel [2, 3].

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Shank

Punch Holder

Stop Pin – Material when first being guided into the die, must stop somewhere for the

sequence of die operations to begin successfully. Advancing the strip too far may lead

to greater than usual wear and tear of the tooling and its subsequent misalignment and

breakage. Two types of stop pins are use 1.Automatic 2.Fixed.

Figure 2.3.8 stop pin [4]

Cushion Pin – metal pins used in conjunction with a die cushion to transfer pressure

from the cushion to the bottom of a die pad. They are also called as air pins, pressure

pins and transfer pins [7].

Figure 2.3.9 Cushion Pin [8]

15

Stop pin

2.4 Types of bending die

1) V-bending die -

In V-bending, the sheet metal blank is bent between a V-shaped punch and die. The

clearance between punch and die is constant (equal to the thickness of sheet blank). In

V-die bending, it is possible for the material to exhibit negative springback. This

condition is caused by the nature of deformation as the punch completes the bending

operation. Negative springback does not occur in air bending (free bending) because

of the lack of constraints in a V-die. The thickness of the sheet ranges from

approximately 0.5 mm to 25 mm [2].

Figure 2.4.1 V-bending die [3]

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2) U bending die - In U bending, the sheet metal blank is bent between a U shaped

punch and die. Punch of U shape is used for producing the U bend; figure 2.4.2 shows

U shape part that is produce by only U bending die [10].

Figure 2.4.2 U shape Part [10]

Figure 2.4.3 U bending die [2]

3) Wiping bending die –

Wiping die bending, also known as edge bending, it is performed by holding the sheet

between a pad and die then sliding the wiping flange across the face pushing and

bending the sheet metal which protrudes from the pad and die. The flange is driven by

an upper shoe and the die is supported by a lower shoe. A spring between the pad and

upper shoe grabs the metal before the flange hits it and holds the work piece down

during the bending process.

If the flange has a feature associated with it, other than just a straight bend then a

stronger spring will help prevent the metal from being pulled from the area between

the die and pad. This will lead to less deformation when the piece comes out of the

17

1. Stripper plate

2. Punch

3. Punch holder

4. Die Segment

5. Cushion pin

6. Pressure pad plate

7. Stop pin

8. Die shoe

9. Work piece

stamp. In our example below we are only showing a single section of a feature but in

reality there can be flanges formed on any and all sides of the piece at the same time.

This can lead to significant productivity gains. The Bend Angle is controlled by the

stroke of the wiping punch. It’s necessary that the punch has the proper offset for the

thickness of the material to prevent shearing. This method does not allow for over

bending past 90 ° because of the tooling geometry. This also makes it difficult to

work with harder materials which have a high Spring Back. Below figure shows the

wiping die operation.[11]

`

Figure 2.4.4 wiping die [11]

4) Air bending –

This bending method forms material by pressing a punch (also called the upper or top

die) into the material, forcing it into a bottom V-die, which is mounted on the press.

The punch forms the bend so that the distance between the punch and the side wall of

the V is greater than the material thickness (T)

Either a V-shaped or square opening may be used in the bottom die (dies are

frequently referred to as tools or tooling). A set of top and bottom dies are made for

each product or part produced on the press. Because it requires less bend force, air

bending tends to use smaller tools than other methods. Some of the newer bottom

18

tools are adjustable, so, by using a single set of top and bottom tools and varying

press-stroke depth, different profiles and products can be produced. Different

materials and thicknesses can be bent in varying bend angles, adding the advantage of

flexibility to air bending. There are also fewer tool changes, thus, higher productivity

[9]. From the below figure difference between Air bending and V bending die can be

easily identified. With 3-point bending the points of contact are all on the same side of

the material. The angle is determined by the height adjustment in the bottom tool. And

with air bending the points of contact are on both sides of the material. The angle is

determined by the depth of entry of the tool into the die plate.

Figure 2.4.5 (a) 3 – Point bending. (b) Air bending

A disadvantage of air bending is that, because the sheet does not stay in full contact

with the dies, it is not as precise as some other methods, and stroke depth must be

kept very accurate. Variations in the thickness of the material and wear on the tools

can result in defects in parts produced. [9]

Air bending's angle accuracy is approximately ±0.5 deg. Angle accuracy is ensured by

applying a value to the width of the V opening, ranging from 6 T (six times material

thickness) for sheets to 3 mm thick to 12 T for sheets more than 10 mm thick.

Springback depends on material properties, influencing the resulting bend angle. [9]

Depending on material properties, the sheet may be over bended to compensate for

springback. [10]

19

Air bending does not require the bottom tool to have the same radius as the punch.

Bend radius is determined by material elasticity rather than tool shape. [9]

The flexibility and relatively low tonnage required by air bending are helping to make

it a popular choice. Quality problems associated with this method are countered by

angle-measuring systems, clamps and crowning systems adjustable along the x and y

axes, and wear-resistant tools. [9]

2.5 Knowledge Based System/Expert System

2.5.1 Overview

Expert System is the first realisation of research in the field of Artificial Intelligence (AI). In

the form of a software technology and were developed by the AI community in the mid-

1960’s. It uses human knowledge to solve problems that normally would require human

intelligence. The basic idea behind ES is simply that expertise, which is the vast body of task

specific knowledge, is transferred from a human to a computer. This knowledge is then stored

in the computer and users call upon the computer for specific advice as needed. The computer

can make inferences and arrive at a specific conclusion. Then like a human consultant, it

gives advices and explains, if necessary, the logic behind the advice. Professor Feigenbaum

pioneer of expert system technology has defined an expert system as “an intelligent computer

program that uses knowledge and inference procedures to solve problems that are difficult

enough to required significant human expertise for their solution” [12].

The knowledge in ES may be either expertise or knowledge that is generally available from

books, magazines and knowledgeable persons. The term Expert System (ES), Knowledge

Based System (KBS) or Knowledge based expert systems are often used synonymously.

Most people use the term ES simply because it’s shorter, even though there may be no

expertise in their ES, only general knowledge [13]. An Expert System is not called a

program, but a system, because it encompasses several different components such as

knowledge base, inference mechanism, explanation facility etc. All these different

components interact together in simulating the problem solving process by an acknowledged

expert of domain. Internally ES consist of two main components. The knowledge base

contains collection of knowledge with which inference engine draws conclusions.

20

The process of using current facts and knowledge contained in the knowledge base to

establish additional fats or decisions continues as a chain, until a fact specified as a goal is

established. The control mechanism primarily carries out symbolic processing called

inference. There can be a number of different ways in which the knowledge contained in the

rules can be used for inference. Hence, the control mechanism can consist of many different

inference strategies. Thus these two, knowledge base and inference mechanisms form the

main components of an expert system.

2.5.2 Architecture of Expert system

Typical expert system architecture includes three components: an inference engine, a

knowledge base ad a working memory as shown in figure 4.1. Description of ES components

is given in table 1. Declarative descriptions of expert level information, necessary for

problem solving, are stored I the knowledge base. The inference engine solves a problem by

interpreting the domain knowledge stored in the knowledge base. It also records the facts

about the current problem in the working memory. When an expert system starts the process

of inference, it is required to store the facts established for further use. The set of established

facts represents the context, i.e., the present state of the problem being solved. Hence, this

component is often called context or working memory.

Figure 2.5.1 Architecture of Expert System [14]

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Knowledge base

Inference engine

Working memory

User

User interfac

e

The division between the knowledge base and inference engine has two important

advantages. First, if all of the control structure information is kept in the inference engine,

then one can engage the domain expert in a discussion of the knowledge base alone, rather

than of questions of programming and control structures [12]. Second the versatility of the

system is increased. If all of the task specific knowledge has been kept in the knowledge

base, then it is possible to replace the current knowledge base by a new one and obtain a

performance program for a new task [13]. However, the inference engine and the knowledge

base are not completely independent. The knowledge base contents is influenced by the

inference engine, since the rules written for the knowledge base take into account the

inference engine and its built in control strategies.

Table 2.5.1Expert System Components

Expert System

Components

Descriptions

User Interface Code that controls the dialogue between user and system and provides the

possibility of communication between the user and the computer. Through

user interface, user can provide facts, describe the problem and read the

decision and conclusion provided by the system.

Inference Engine It models the reasoning capabilities of human expert. Code at the core of

the system, which derives recommendation from the knowledge base and

problem specific data in working storage.

Knowledge base This contains the information, facts and rules for specific problem

domain; this is where knowledge is recorded.

Working storage It acts as temporary storage that works in conjunction with the inference

engine. It records facts coming from the user and temporary information

gained by using the rules; this information is used by inference engine for

processing.

Domain Expert Individual(s) who currently are experts solving the problems.

Knowledge

Expert

Individual(s) who encodes the expert’s knowledge in declarative form that

can be used by the expert system.

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2.5.3 Knowledge Base

The knowledge base contains the domain-specific knowledge required to solve problems.

Knowledge engineer develops the knowledge base. He conducts a series of interviews with

experts and organizes the knowledge in a form that can be directly used by the system.

Knowledge engineer has to develop an expert system using a development environment or an

expert system development shell.

The knowledge that goes into problem solving in engineering can be broadly classified into

three categories, viz., compiled knowledge, qualitative knowledge and quantitative

knowledge. Knowledge resulting from the experience of experts in a domain, knowledge

gathered from handbooks, old records, standard specifications etc., and forms compiled

knowledge. Qualitative knowledge consists of rules of thumb, approximate theories, causal

models of processes and common sense. Quantitative knowledge deals with techniques based

on mathematical theories, numerical techniques etc. compiled as well as qualitative

knowledge can be further classified into two broad categories, viz., declarative knowledge

and procedural knowledge [15]. Declarative knowledge deals with knowledge on physical

properties of the problem domain, whereas procedural knowledge deals with problem solving

techniques.

2.5.4 Knowledge Representation

For development of an expert system, one should know different knowledge representation

schemes and the possible modes of interaction between them. Knowledge representation is an

important activity in development of expert system for two reasons. First, expert system

shells are designed for a certain type knowledge representation such as rules or logic. Second,

the way in which an expert system represents knowledge affects the development, efficiency,

speed and maintenance of the expert system. To represent knowledge means to convert

knowledge to an applicable form. An collection of techniques is being used to represent

knowledge including, rule based systems, semantic nets, frame systems, scripts, first order

predicate calculus, associative networks, object oriented systems and attribute grammar

systems. A detailed summary for some of the most important features of different knowledge

representation schemes in expert system is given below [12, 16, 17].

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Production Rules -

One of the most popular types of expert system today is the rule based system. It is popular

for number of reasons.

Modular Nature – this makes it easy to encapsulate knowledge and expand the expert

system by incremental development.

Explanation facilities – It is easy to build explanation facilities with rules because the

antecedents of a rule specify exactly what is necessary to activate the rule. by keeping

track of which rules have been fired, an explanation facility can present the chain of

reasoning that led to certain conclusion.

Similarity to human cognitive process – Rules appear to be a natural way of

modelling how human solve problems. The simple IF and Then representation of

rules makes it easy to explain to expert the structure of the knowledge you trying to

elicit from them.

Production rules are simple but powerful forms of knowledge representation providing the

flexibility of combining declarative and procedural representation for using them in a unified

form. A production rule has a set of antecedents and a set of consequents. The antecedents

specify a set of conditions and the consequents a set of actions.

IF < condition or set of conditions >

Then < Action >

The methodology used in rule based systems [18] originated from the production

systems framework proposed by Post [19]. A rule based system consists of three

major parts:

Working memory that holds the facts, the goal and the intermediate results.

Rule memory which holds all the system rules and

Rule interpreter, which decides about rule applicability.

Conditions or premises are evaluated with reference to the data in the working memory; and

if evaluated to be true, actions take place. The contents of the working memory are affected

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by the actions of the rule that has been fired. If there is more than one rule whose premises

are satisfied, then it is up to the rule interpreter to select one. The strategy to select the next

rule to be fired is called conflict resolution. Conflict resolution requires efficient response to

changes in the working memory to maintain a logical reasoning continuity and system

refraction. Important conflict resolution mechanisms are:

a) Refractoriness that prohibits the execution of a role on the same data more than once.

b) Regency that favours the execution of a rule that matches with the most recently

entered data in the working memory and

c) Specificity, which gives precedence to the execution of a rule with the largest number

of premises.

Some systems allow the programmer to establish metarules. Metarules gives the reason about

which additional rules should or should not be considered. Rule based expert systems utilize

both forward and backward chaining. With some simple trick rules they can be made to

represent many, if not all, knowledge representation techniques. Representative rule based

expert systems include the MYCIN expert systems in engineering [20], the DENDRAL [21],

the Meta-DENDRAL [21] AND THE EMYCIN [20]. Rule based systems are the easiest to

implement and they maintain an overall acceptable performance. However, their efficiency is

deteriorating in the presence of high volume knowledge data and the main reason for this is

that the matching process in the inference engine becomes computational intensive as the

amount of knowledge data increases.

Frames (objects) and Semantic Networks -

Objects are very powerful forms of representing facts in expert systems. They are ideally

suited for representation of declarative knowledge, which describes physical entities and

semantic relationships between them [22]. Any engineering activity is centred on an object or

a facility, and detailed information about the object is required to make decisions concerning

it. Different attributes of the artifact may be used at different stages of a problem such as

planning, analysis, detailing, manufacturing/construction etc. hence, it is appropriate to use

objects with attributes encapsulated in order to have a more structured representation of facts

in the context, during execution of the expert system. Also, the rules should be able to

interact with the objects.

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Procedural programs -

Engineering problem solving involves numerical computations, in addition to inference using

knowledge. In a real life expert system, the system has to perform numerical computations

may be very small in some cases and quite large in many cases. Based on the values inferred,

a detailed analysis of the artifact may have to be carried out to evaluate the correctness of the

parameters arrived at. The quantitative knowledge required for such computations can be

represented as functions/programs written in high level programming languages such as

FORTRAN or C. an expert system should be able to call these programs as and when

required during problem solving. Hence, they also form part of the expert system.

Most expert system development shells provide facilities to represent knowledge in the three

forms, viz., rules, frames and functions in procedural languages. The predicate logic form of

knowledge representation is the natural form in prolog, one of the specialized AI languages;

prolog provides predicate logic-based representation with backtracking inference, which is

inadequate for developing large expert systems for practical applications.

In addition to rules, frames and semantic nets, knowledge can be represented by the symbols

of logic, which is the study of the rules of exact reasoning. An important part of reasoning is

inferring conclusion from premises. The application of computer to perform reasoning has

resulted in logic programming and the development of logic based languages such as

PROLOG. Predicate logic provides mechanisms for representation of facts and reasoning

based on syntactic manipulation of logic formulae. It uses predefined formulae are

manipulated purely based on their form or structure. The major disadvantage of this scheme

is that it cannot consider the meaning or semantic content of the formula. In predicate logic,

all deductions are based on logic statements, and inference rules are guaranteed to be correct.

In addition, a logic program will generate all possible inferences that can be drawn from the

facts and rules. Though such predicate logic systems deduce all possible facts, their ability to

carry out a constrained search through the facts and inferences is limited. This is primarily

due to their inability to carry out guided search and also to represent search strategies. As

new facts are generated, the inference rules are applied to assert newer facts. This process

continues leading to combinatorial explosion until a goal state is reached. Only a constrained

assertion of facts can improve the situation, which is difficult in predicate logic. In addition,

predicate logic systems try to apply all the inference rules to all the facts. There is no

26

mechanism to group facts and associate specific inference rules to different groups. Even in a

small real-life AI-based system, there can be number of facts and inference rules. Due to the

above-mentioned limitations, it becomes difficult to apply predicate logic-based knowledge

representation in expert systems [22, 23]. A good knowledge representation scheme should

have the capability to represent real-life situations (objects and relationships among them),

which can be exploited by efficient guided search strategies and which better reflect the way

humans perceive and think.

27

2.6 Problems in Traditional Process of Bending Die Design

To check the manufacturability of component, determine the process plan for sheet metal

parts and to design/selection various die components, die designers have to performed design

tasks such as process planning, selection of type of press, bending sequence, bending force,

speed design/selection of various die components. Designers also have to consider how to

obtain optimal number of operation stages to produce parts having complex shape. To

perform all these tasks it require many years of experience on the part of die designer. A

number of problems in traditional process of die design are summarized as follow.

1. It is tedious, time consuming and error prone.

2. Knowledge gained by die design experts after long years of experience is

often not available to others even within the same company. It creates a

vacuum whenever the expert retires or leaves company.

3. Due to long apprentice period, slow career growth and burden of heavy

workload, young technocrats do not prefer to enter in the challenging field

of die design.

4. Stamping industries are facing acute shortage of experienced die designers

worldwide.

Number of CAD/CAM software’s like UG, CATIA, PRO-E, IDEAS and SOLID EDGE etc.

are developed. These software’s assist die designers in drafting, visualisation and storage and

retrieval of component geometric data. But the limitations of these CAD/CAM software’s are

1. Commercial CAD software only assists in drafting and simple design

calculations and to operate this software skilled die designers are required.

2. Only segments of the die design process are supported.

3. Incomplete, imprecise, or inconsistent information cannot be handled.

4. The various phases of design of bending die are not integrated in single software.

5. Design technical and logical errors are not detected.

6. Costly and hence not affordable by small scale stamping industries.

28

Chapter 3 KBS Development Procedure, Methodologies, and

Applications

To a large extent the development of a knowledge based system will depend

on the resources provided, however like any other project, the development will also

depend on the how the process is organized and managed. The development of a KBS

is represented in figure 3.1, begins with the preparation phase. Under this phase,

determination of a goal and preliminary investigation is done. The second phase is

conceptual and prototyping phase in which problem analysis, system draft, system

prototyping and validation of the prototype is carried out. In the third phase in-house

testing using a real life case study. In the fourth stage complete testing of the system

with real life data by the user. In the fifth stage system is validate and documented.

The last phase is maintenance and evolution where fixing of error and continuous

enhancement of the system by continuously adding new knowledge [24, 25].

Paper or comparison study to show project is

feasible

Expert system quickly put together to

demonstrate arouse enthusiasms

In-house verification of expert system on real

problem by knowledge engineer.

System tested by selected users not knowledge

engineers or experts

Validated and tested, User documentation,

training

Fix bugs Enhance capabilities

Figure 3.1 General stages in the development of ES/KBS.

29

Feasibility Study

Rapid Prototyping

Refined System (Alpha test)

Field Testable (Beta Test)

Commercial quality system

Maintenance and Evaluation

3.2 Expert System Applications

KBS has been applied to virtually every field of knowledge. Some have been designed as

research tools while others fulfil important business and industrial functions. Based on

system reported in the literature, certain broad classes of KBS applications can be distinguish

as shown in table 3.1

Table 3.1 Broad classes of Expert system

Class General area

Configuration Assemble proper components of system in the proper way

Diagnosis Infer underlying problems based on observed evidence

Instruction Intelligent teaching so that a student can ask why, how and what if type

questions just as if a human was teaching

Interpretation Explain observed data

Monitoring Compare observed data to expected data to judge performance

Planning Devise action to yield a desire outcome

Prognosis Predict the outcome of a given situation

Remedy Prescribe and remedies

Control Regulate a process. May require interpretation, diagnosis, and monitoring,

planning, prognosis.

30

3.3 Procedure of KBS/ES for Bending Die Design

The procedure for building KBS modules of die design is schematically shown in figure 3.2

A brief description of each step is given in following paragraphs [26]

31

Knowledge Acquisitions

Expert dies designers, handbooks, monographs, research journals, catalogue and industrial brochures

Framing of Production Rules

IF <condition>THEN <action>

Verification and sequencing of production rule

Cross checked from die design experts arranged in an a structured

Identification of operating system and hardware

Provide high quality run time environment for complete interactive programs with large knowledge bases

Selection of development language

Provide suitable facilities, for the effective representation of knowledge and efficient inferences from it

Construction of KBS shell

Elicit problem specific data, apply and explain the application of the knowledge base.

Select suitable search strategy

Forward chaining and backward chaining

Preparations of user interface

Knowledge acquisition

It is a first step in the development of knowledge based system. It is most time

consuming and laborious job in expert system development. The domain knowledge for

design of drawing die is collected through by on line and off line consultation with

design experts, tool design engineers of different industries, referring research articles,

catalogue and manual of different design and manufacturing industries. The

information obtained from the literature is not always the same as what is currently

being practiced. The information obtained through industrial broacher is a compromise

between the academically fundamental knowledge obtained through literature reviews

and the practical, experience based knowledge obtainable from industrial experts. The

process of knowledge acquisition from die design experts involves presenting a few

typical problems to the experts and letting the expert talk through the solution. During

the verbal analysis, the experts would be questioned to explain why a particular

decision was reached.

Framing of Production Rules

The knowledge collected from the various sources is represented using rules. The most

common method of knowledge representation is ruled based systems.

The syntax of a production rule is

IF < condition >

THEN < action >

The condition of production rule, sometimes called LHS contains one or more

conditions, while the action portion, sometimes called RHS contains one or more

actions.

32

Verification of production rules

The knowledge available for design of drawing die is mostly collected from the die

design expert. The experts use the thumb rules, which they developed during long years

of practice and experience in die design. These rules may differ from industry to

industry. So it is mandatory to come up on a common solution, which could be

accepted by most of die designers working in various industries. The production rules

framed for each module must be crosschecked from die design expert by presenting

them IF condition of the production rule of IF – THEN variety.

Sequencing of production rules

The framed rules are presented in either in an unstructured or a structured manner. But

structured presentation of knowledge in terms of the production rules are simple to refer

and consume less time and if query is fired it take less time to get the result. Also

ambiguity in understanding the knowledge will be less.

Identification of selection operating system and hardware

The lowest level of the hierarchy in the development of expert system is the machine on

which the expert systems run. Suitable hardware elements depending upon memory

requirement, processing speed and needed configuration should be selected. Today,

most of the KBS modules are being developed on a PC/AT because it involves low

cost.

Selection of development language

Early expert systems were written in language interfaces derived from FORTRAN.

Later on, object oriented languages such as KEE, OPS, PROLOG, TURBOPROLOG

and LISP were developed specifically for the AI systems. LISP and PROLOG have

been won wide acceptance for building expert system, the user of LISP and PROLOG

languages encounters difficulties when handling design problems involving graphical

information. For this reason, AutoCAD and AutoLISP have found greater acceptance

for the development of expert system for die design.

33

Construction of KBS shell

Knowledge base is a part of an ES that contains domain knowledge, which may be

expressed in the form of production rules of IF-THEN variety. The inference

mechanism allows manipulating the stored knowledge for solving problems. The rules

and the knowledge base are linked together by an inference mechanism. The user input

information provides guidance to the inference engine as to what IF-Then rules to fire

and what process of information is needed from the knowledge base.

Choice of search strategy

Inference mechanisms are control strategies or search techniques, which search through

the knowledge base to arrive at decisions. The two popular methods of inference are

backward chaining and forward chaining. Backward chaining is a goal driven process,

whereas forward chaining is data driven. Forward chaining is a good technique when

all on most paths from any of much initial or intermediate state converges at once or a

few goal states. Backward chaining is an efficient technique to use when any of many

goal states converging on one or a few initial states.

Preparation of user interface

The expert system modules should be interactive in nature. The purpose of user

interface in the development of each module is twofold: (1) to enables the user to input

the essential sheet metal component data; (2) to displays the optimal decision choices

for the users benefit. The former is accomplished by flashing AutoCAD prompts to the

user at appropriate stages during a consultation to feed data items. Messages or items of

advice are likewise flashed into the computer screen whenever relevant production

rules are fired.

3.4 Need of Expert System in Bending Die Design

34

Because of globalisation and competitions, sheet metal industry faces number of challenges

such as to reduce the time spent on product development, shorter delivery time and low cost

of product. In addition sheet metal industry faces a problem of low recruitment rate and large

turnover of the manpower. The success of the die design and manufacturing is largely

depends upon the skill and the experience of the die designer. But due to the huge quantity of

uncertain information and factors to be considered and selected, die design is affected by

individual subjectivity to a great extent. These often affect the structure of the die itself and

the reliability of sheet parts. Also large number of rules and experimental studies in the area

of die designs are available but it is poorly documented; when experts argue, the bases on

which they argue are largely unspoken. Commercially available CAD/CAM systems are

providing some assistance in drafting and analysis in die design process, but human expertise

is still need to arrive at the final design [12]. As discussed earlier, the task of die design is a

complex, tedious, time consuming and experience based activity. Only engineers having

many years of design experience would own the knowledge for correct design of die. On one

hand it is very minimum potential failures. On the other hand, even for experienced

engineers, negligence would often result in unwanted consequence.

Therefore, there is a need to develop a KBS for die design that can store the past experiences,

eliminate human error and can logically integrate together all relevant knowledge and

experience and to provide an aid to process planners and die designer of sheet metal industry.

Development of KBS will help to achieve shorter product delivery time and low cost also it

can be used as an effective tool for training for new comers in die design and manufacturing

which will reduce the training time.

Chapter 3 Literature Review

35

Ching. Z. et al., (1994) developed the possible application of an expert system in sheet-metal

bending, and to build a prototype of a sheet-metal bending expert system on a PC/AT. This

sheet-metal bending expert system utilizes the qualitative data in a knowledge base and the

quantitative data in a database, together with empirical design data, to aid the user in the

design of sheet-metal bending. The deduced drawing of the sheet-bending dies is

demonstrated using AutoCAD graphic software. They established a prototype expert system

of sheet-metal bending design with preliminary learning capabilities. The system does not

provide process planning and manufacturability of bending.

Duflou, J. et al. find out that bend sequencing and tool selection have long been the main

hurdles for achieving automatic process planning for sheet metal bending. In this process, the

complex shape and position transitions of work pieces make it hard to obtain a collision-free

operation plan. They presented a tool selection methodology to be integrated in the automatic

bend sequencing system. Both the described selection strategy and the related algorithms

have been implemented in an industrial software package. Taking into account not only

feasibility aspects of technology and geometry, but also production planning oriented

guidelines, the method is able to deliver feasible and well-optimised tooling solutions, as

result of a two phase procedure consisting of pre-selection and refined selection.

Lin Z.et al. (1996) established the knowledge base for the selection of sheet metal bending

machines. They considered machine specifications such as pressure capacity and bending

length in the selection in order to reach the best choice according to the need of the decision

maker. Their purpose was to utilize the PRISM method of inductive learning and knowledge

acquisition to construct a product-type rule base, and further complete an expert system for

the selection of sheet metal bending machines, which may assist the user in choosing the

appropriate bending machine. They used the concept of probability of the knowledge

acquisition model of PRISM inductive learning to construct a modular knowledge base. A

step-by-step illustration of how to use the PRISM algorithm in developing the rules of the

knowledge base is presented. They used three machine specifications - pressure capacity,

bending length and stroke as the analytical attributes to construct the rule base of this system.

Ong, S.K. et al. (1997) applied brake forming in the high variety and small batch part

manufacturing of sheet metal components, for the bending of straight bending lines. They

36

described the application of fuzzy set theory, for the normalization and modelling of the set-

up and bend sequencing process for sheet metal bending. A fuzzy-set based methodology is

used to determine the optimal bending sequences for the brake forming of sheet metal

components, taking into account the relative importance of handling and accuracy. A fuzzy-

set based bend and set-up sequencing methodology for sheet metal working has been

presented. The highly experience and heuristic-based conventional set-up planning

procedures in sheet metal bending are aptly modelled in this computer-automated bends and

set-up sequencing system, using the fuzzy set theory.

Inamdar, M. et al. (2000) described springback in air vee bending process is large in the

absence of bottoming. Inconsistency in springback might arise due to inconsistent sheet

thickness and material properties. Among the various intelligent methods for controlling

springback, an artificial neural network (ANN) may be used for real time control by virtue of

their robustness and speed. They described that the development of an ANN based on back

propagation (BP) of error. They established the architecture using an analytical model for

training consisted of 5 input, 10 hidden and two output nodes (punch displacement and

springback angle). The five inputs were angle of bend, punch radius/thickness ratio, die gap,

die entry radius, yield strength to Young's modulus ratio and the strain hardening exponent, n.

Lin Z. et al. (1996) developed an expert system for the selection of sheet metal bending tool

which was one of the earliest manufacturing techniques in the manufacturing industry. The

machine specifications such as pressure capacity, bending length and so on must be

considered in the selection of sheet metal bending tooling in order to reach the optimal choice

based on the needs of the decision maker. They proposed a model using machine learning

from neural networks in an expert system of sheet metal bending tooling. With the three

machine specifications of pressure capacity, bending length and stroke length as the

analytical attributes of the problem, the knowledge acquisition of the neural networks

machine learning model is used to establish a rule base for the system, which equips the

system with better knowledge representation and inference capacity.

37

Inamdar, M. et al. (2000) find out that Springback is a serious problem in the air vee bending

process because of its inconsistency. An on-line tool to control springback is more reliable

than an analytical model which might not be able to control the stroke of the machine in real-

time. They suggested that, one might resort to adaptive control or use an artificial neural

network (ANN) trainer, either using experimental data or analytical predictions (or both), and

use it for real-time control of the machine tool. The inconsistency in springback is then

reduced to within acceptable limits. Adaptive control would need several strokes to complete

the job, but it is envisaged that the job could be completed in a single stroke with the ANN.

Lin, Z. et al. (2001) described that the shearing force in the shearing-cut process for a

shearing-cut and bending progressive die is far greater than the strip bending force. The

equation for torque equilibrium is first established. The heuristic rule is then adopted to locate

the die centre of the shearing-cut and bending progressive die and an offset displacement is

set. They mainly tried to reduce the time spent in adjusting the pressure centre of the

shearing-cut and bending progressive die. Genetic-algorithms (GA) are applied as a solution

tool in the analysis of the optimal strip working sequence possessing a smaller difference

between the right and left torque in the shearing-cut and bending progressive die. The torque

equilibrium model for the shearing-cut and bending progressive die and the concept of rough

tuning first followed by micro-tuning were presented in this paper. GA was also used to

derive the strip working sequence with mini-mum torque difference.

Kim, C. et al. (2002) described a computer-aided bending and piercing operation for

progressive working of a component. The automated design system for process planning and

die design by fuzzy set theory for electric product with intricate piercing and bending

operation. Program was written in AutoLISP on the AutoCAD with a personal computer and

composed of four main modules, which are input and shape treatment, flat pattern layout,

strip layout, and die layout modules. The strip layout and die layout drawings automatically

generated by formularization and quantification of experimental technology will make

minimization of trial and error and reduction of period in developing new products. Results

obtained using the modules enable the manufacturer for progressive working of electric

products to be more efficient in this field, also play an important role in building FMS system

as an integrated CAD/CAM system.

38

Aomura, S. et al. (2002) proposed a method which generates bending sequences of a sheet

metal part handled by a robot. If parts are handled by a robot, the best grasping positions for

each bending and the number of repositions must be indicated in advance. Using the

proposed method, feasible bending sequences with grasping positions are obtained and the

sequences are sorted in the order of the number of repositions. In generating the sequences,

they consider several important features for the sheet metal bending by dividing them into

channels, which is one of the base features. They calculated the error accumulated during

bending operation for each sequence, and selected set-up positions so as to satisfy the

preferential tolerance. The proposed method assists the sheet metal process planner to

confirm if the robot can perform the handling operation.

Rico, J.C. et al. (2003) developed the system which described a method to obtain valid

bending sequences automatically according to the possible tool–part collisions and

tolerances. A method for solving the problem of bend sequencing in sheet metal

manufacturing is presented. The algorithm developed divides the part into basic shapes

(channels and spirals) and determines the partial sequences associated with them. The

complete bending sequences associated with the complete part were obtained from the

combination of these partial sequences. Finally, the sequence associated with the lower

process time was selected as the optimal solution. An approach to the bending time

calculation was followed to order the valid sequences and determine the best solution. In

order to reduce the calculation time to identify valid sequences, a method based on the part

division in basic shapes (channels and spirals) was proposed.

Pathak, K. et al. (2005) described that the sheet metal bending is an important form of sheet

metal forming process, widely used in various industrial applications like aircraft,

automobiles, household items, power industries, etc. They predict the responses of the sheet

metal bending process using artificial neural network. Sheet thickness and die radius were the

input, and stresses, strains, springback, loads, etc were the output for the neural network. The

trained neural network was tested for five new patterns. They observed that the neural

network gives quite close predictions of sheet forming responses. Such predictions help to

reduce large computational time going into computer simulations. It can be handled even by

novice in finite element analysis.

39

Sousa, L.C. et al (2006) presented an optimization method applied to the design of V and U

bending sheet metal processes. They coupled the numerical simulation of sheet metal forming

processes with an evolutionary genetic algorithm searching the optimal design parameters of

the process. They considered an inverse approach so that the final geometry of the bended

blank should closely follow a prescribed one. They presented applications to demonstrate the

applicability of the proposed method considering several relevant parameters including punch

and die radii, punch displacement and blank-holder force. The genetic algorithm is a

developed FORTRAN code and the finite element analysis is carried out using the

commercial code ABAQUS. They developed interaction between the two codes has been

written using PERL and PYTHON programming languages.

Bozdemir, M et al. (2008) defined the springback angle with minimum error using the best

reliable ANN training algorithm. Training and test data were obtained from experimental

studies. Materials, bending angle and r/t have been used as the input layer , springback angle

has been used as the output layer. For testing data, Root Mean Squared-Error (RMSE), the

fraction of variance (R2) and Mean Absolute Percentage Error (MAPE) were found to be

0.003, 0.9999 and 0.0831% respectively. With all these results they believed that the ANN

can be used for prediction of analysis of springback as an appropriate method in V bending.

Kazan, R. et al. (2009) described that the wipe-bending is one of processes the most

frequently used in the sheet metal product industry. Furthermore, the springback of sheet

metal, which is defined as elastic recovery of the part during unloading, should be taken into

consideration so as to produce bent sheet metal parts within acceptable tolerance limits.

Springback is affected by the factors such as sheet thickness, tooling geometry, lubrication

conditions, and material properties and processing parameters. They developed the prediction

model of springback in wipe-bending process using artificial neural network (ANN)

approach. Here, several numerical simulations using finite element method (FEM) were

performed to obtain the teaching data of neural network. Optimized results were not obtained

by them.

Kontolatis, N. et al. (2010) applied sheet metal bending processes in a multitude of

mechanical parts. The process involves optimizing the sequence of designated bends taking

into account the total processing and handling time, avoiding collisions of the sheet metal

with tools and machine and respecting the dimensional accuracy constraints of the part. They

40

replaced expert knowledge by stochastic search using a classic genetic algorithm. They

introduced dimensional accuracy issues by determining machine stopper positions and

employed interference detection libraries in connection to the search nature of the approach

enabled coping with the full 3D problem instead of quasi 2D problems dealt with in literature.

Their system is not capable to provide solution for sprinback in present approach.

Baseri, H. et al. (2011) described that spring-back is one of the most sensitive features of

sheet metal forming processes, which is due to the elastic recovery during unloading and

leads to some geometric changes in the product. Three parameters which are most influential

on spring-back in V-die bending process are sheet thickness, sheet orientation and punch tip

radius. They proposed a new fuzzy learning back-propagation (FLBP) algorithm to predict

the spring-back using the data generated based on experimental observations. The

performance of the model in training and testing is compared with those of the constant

learning rate back-propagation (CLBP) and the variable learning rate back-propagation

(VLBP) algorithms. Then the best model with the minimum mean absolute error (MAE) is

selected to predict the spring-back. They indicated that the proposed FLBP algorithm has best

performance in prediction of the spring-back with respect to the other algorithms.

Table 7.1 Salient features of major research work reviewed.

Sr. No

Researcher System Details Remarks

1 Uzsoy, et. al., (1991)

Rule based KBC implemented using turbo prolog.

A simple sheet part with total of eight features; four holes with two holes radius of 2 units, two hoes of radius 1 unit and four bends.

2 Ching, Z. et.al. (1994)

Autolisp integrated into AutoCAD

System does not provide process planning and manufacturability of bending.

3 Ching, Z. et. al. (1996)

Sheet metal bendingmachine selection model

data base for present m/c required to bemade

4 Ong, S. etal. (1997)

Fuzzy set theory for sheet metal bend sequencing

Does not provide optimal bending sequences only provides feasiblesolution

5 Ching, Z. etal.(1998)

Selection of bending toolsand bending sequence

good for customized problem only

6 Gupta, etal., (1998)

Automated process planning for robotic sheet metal bending press brake

Separate illustrations are given for each of the modules for different operations;Presently sheet with 23 bends can be

41

addressed for its process planning & other sequences.

7 Inamdar, M.et al.(2000)

Artificial Neural Network for measuring Sprinback inv-Bending

More the Data for initial training themore accurate will the result

8 Ching, Z.(2001)

Torque equilibrium and optimal strip workingsequence for bending progressive die

two stage model generated for customized problem

9 Shigeru, A.et al.(2002)

Sheet metal Bending Sequence and robot grasping positions are determined by Graphicalmethod and geometricalbending features

Only in 55% cases model provides accurate result and around 27 % moderate result.

10 Ruffini, R.(2002)

Neural network for springback minimization

Initial data is required for training of ANN model so, not suitable for initial use where precise o/p is required

11 Kim, C. etal. (2002)

CAPP process planning ofBending and Piercing

System required commercial CAD/CAM software

12 Markus, A.et al.(2003)

Constraint based ProcessPlanning

Still input of experienced domain expert is required

13 Rico, J.C. etal. (2003)

Automatic bending Sequence for Parallel bends

Model is based on Part-tool collision and tolerance constraint with lower process time optimisation

14 Ehrismann,R etal.(2004)

Expert system for BendingSequence

system is not able provide optimizedsolution

15 Pathak, K.et al.(2005)

Neural Network for finding sheet metal bending processparameters

to reduce the error % of results in b/wFEM and NNT high no. of cases are required for training

16 Sousa, L.C.et al.(2006)

Numerical simulation coupled with Genetic Algorithms used to optimized V & U bendingprocesses

Time required for numerical simulation is High around 36 h.

17 Bozdemir,M. et al.(2008)

ANN for Springback inV-Bending

Good results are obtained for customized problems

18 Kazan, R. etal. (2009)

ANN for springback inwipe bending

optimized resultsare not obtained

19 Kontolatis,N. et al.(2010)

Optimization of BendingProcess planning parameters

System is not capable to providesolution for sprinback in present approach

20 Baseri, H. etal. (2011)

Spring Back Modelling byfuzzy learning model in V Bending

Experimental data is used for learningmodel

42

Chapter 8 Conclusion

The conventional process of design of bending die is highly complex, error prone, manual

and time consuming. There are commercially available CAD software are also not capable to

ease burden of experienced die designers and process planners for quick design of stamping

tools. With the advancement in artificial intelligence (AI), researchers have started work in

the development of expert systems for design of bending die. Expert system eliminates

human errors and logically integrates together all relevant knowledge and experts’

experience. Some researchers applied research efforts towards the development of expert

system for design of bending die but most of the systems are prototypes, dedicated to specific

geometry of parts, and covering a subset of process planning and design functions.

Therefore, there is stern need to develop an expert system to ease the complexity in bending

die design process, reduce the time taken and finally display all design data and drawings in

CAD editor. This type of system will certainly provide a great help to the process planners

and die designers working in sheet metal forming industries. The developed expert system

must have low cost of implementation so that it can be easily affordable by small and

medium scale stamping industries, especially in developing countries.

43

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