P E R G A M O N Computers & Industrial Engineering 37 (1999) 89-92

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INTENSITY TYPE STATE VARIABLES IN THE INTEGRATION OF PLANNING AND CONTROLLING MANUFACTURING PROCESSES

Tiber Tdth, Ferenc Erd~lyi and Farzad Rayegani

Institute of Information Science, University of Miskolc, Hungary

ABSTRACT Nowadays rapid changes of market conditions require more efficient integration of Process Planning and Production Controlling in manufacturing industry. In computer integrated systems the classical models of manufacturing operation planning are not adequate for flexible operation management. This paper shows a new conception to use intensity type machining state variables (e.g. Q [cmVmin]) which expresses both machining and management aspects at the same time. The theoretical background of the conception and some application examples will also be detailed. © 1999 Elsevier Science Ltd. All rights reserved. KEYWORDS Integration, optimisation, intensity, manufacturing processes, CAPP, PPS, CAM.

INTRODUCTION

In order to increase their performance, enterprises are required to have capability to meet rapid and stochastic changes of market. Profit oriented production management requires high utilization of machining resources, keeping inventory stock at low level and fulfilling demand of supplying deadlines. The classical mieroeconomical conception is based on a relative stability of market and the paradigm of "mass production". In mass production operation management responsible for productivity and efficiency of production processes demands reliable, feasible and optimum cutting conditions of machining operations from process planning. Of late years production-to-order has become more frequent in manufacturing industry driving back production-to-stock. Operation management has to face the requirement to follow a new production paradigm.

The new production paradigm is "Lean Production" that replaces the static environment of mass production with the dynamic environment of flexible manufacturing. One of the most important tools for satisfying the new demands is the new results of information and communication technology realised in CIM environment. Advanced CIM architectures use Intranet type computer networking, common engineering database and CAxx technology for planning and controlling manufacturing processes. However, the problem is that the models, methods and goals of CIM components (e.g. Process Planning, Production Planning and Scheduling, Production Control, Quality Management) are frequently different.

The most significant difference is that Process Planning takes into consideration the potential capabilities of machines and connects the quasi-optimum solvability of the machining tasks to these machines. A typical consequence of this approach is the overload of machines of the highest technical level, i.e. the rise of "bottlenecks". On the other hand, the up-to-date approach of Production Control emphasises neither the optimisation of individual operations nor process optimisation for a part or an assembly as a whole but it requires searching for a quasi- optimum weighted among fulfilment of the orders related to a given time interval of production (e.g. for a shift).

PROBLEMS OF INTEGRATION

An integrated Process Planning and Production Management System which is capable to adapt to uncertainty and, at

0360-8352/99 - see front matter © 1999 Elsevier Science Ltd. All rights reserved. PII: S0360-8352(99)00029-7

90 P r o c e e d i n g s o f t he 2 4 t h I n t e r n a t i o n a l C o n f e r e n c e on C o m p u t e r s a n d I n d u s t r i a l E n g i n e e r i n g

the same time to optimise technology processes on the base of up-to-date objective functions, can apply two fundamental methods. O n e of them is the application of alternative technology and production plans, the o t h e r is using adaptive reengineering methods for production control. The most important state variables used by Production Control for scheduling and dispatching manufacturing jobs at shop-floor level are machining times and costs of operations. Simultaneously, machining time and cost are typical objective functions at process planning level. The most frequently used objective functions are as follows (c.f.: R a v i g n a n i et al., 1977):

operation element time: t o = t, + t . + t = + + t, ° n v . f ~ , v . f . T '

operation element cost: Co = c . t + c w . t . . ! + + --~ . C r + c w • t .

In the expressions: t : setting time, t . : machining time, t o : auxiliary time, T : total setting time, n: lot size, A:

geometric parameter of cutting, v: cutting speed, f ' feed rate, t c : time of tool edge replace, 7'." tool life, t : time of

rapid movements, c, : setting cost per time unit, c w : the cost related to time unit of the workplace, C r : the total

cost of one edge of the tool used.

Determination of optimum cutting conditions (v*, f*) where operation element time or cost is a minimum is a classic task of process planning solved by many authors in the literature. However, utilizability of the optimum cutting conditions on the base of the above mentioned general objective functions is strongly limited. The reasons of this fact are as follows:

• There are a lot of constraints which determine the boundaries of the permissible domain of cutting conditions.

• The tool life (T) is a fairly complicated function of cutting conditions and in some cases only a stochastic model is utilisable.

• In the case of non-stationary cutting process the model may become very complicated.

• The local optimum cutting conditions computed separately for any other operation elements are not robust but very sensible to changes.

• An optimum parameter-set for complex operations and/or operation groups consisting of numerous operation elements/suboperations can also be determined theoretically but the complexity of computations are not proportionate with the usefulness of the results obtained.

The outputs of CAPP (process plans, tooling charts, NC part programs) transform static information of the product model into the basically dynamic world of manufacturing. At the same time, the methodology of model-based technology planning uses a special abstract world of manufacturing. The plans are usually optimised to a certain extent and their details are connected to one another in a m o s a i c - l i k e way. In order to realise the global optimisation for the manufacturing of a given part, its technology plan must be corrected using the real time information of production control. A NEW STATE VARIABLE FOR PRODUCTION PLANNING AND CONTROLLING

In order to avoid the aforementioned drawbacks, a new conception is suggested: it is worth introducing a state variable Q that measures the material removal rate: Q = v . f . d [cmVmin] where v: cutting speed Ira/m/n]; feed-rate

[mm]; d: dept of cut [mm] (Detzky et al., 1988). A great advantage of this state variable is the fact that both machining time and operation element cost can easily be expressed by means of it. For a given operation element we have:

=r,,,. ,:= C, where: ''. t. -d' c..y. Q R' LC,/c.+toj

In this expressions V : the volume to be removed, d: depth of cut, K,r ,p ,q: empirical quasi - constants for tool life

equation, ~" : the cost function related to the unit of cost per minute and the material volume to be removed, C :

Proceedings of the 24th International Conference on Computers and Industrial Engineering 91

total machining cost, m: the well-known quasi-cous~mt in the generalized Taylor tool life equation, usually 0.1 < m < 0.5 according to experimental sources. Investigating the morphological characteristics of the function r(Q, R) we f'md the following. The level lines ( r = const .) in the form of R = R(Q, r) are:

Q R(Q,T) = (Qr_l),, ~ ; (Q~ > I).

The absolute value ofthe gradient vector is: G= ~r=I(:-'~)"+(Dr~"'\DR)

We search for the extremum of G(Q,r) if r = r0 = const. The solution can be obtained in the form of R = m,Q (see: Fig.l) which is the equation of the valley line. Similarly we can f'md the optimum line in the form of R = m,Q from the equation: R = MIN(•r / DQ). TYPICAL DECISION MAKING IN THE SPACE OF STATE ( r, Q, R )

The conventional approach of technology process planning assumes that the operation elements can be optimised independently while the upper system level relations are passed by the properties of constraints and objective functions to the lower one. In full knowledge of the technology parameter values, the planning system will determine the manufacturing times which are the most important data necessary to production planning and scheduling. The connection between the capacity of available manufacturing resources and the resource demand of manufacturing of the ordered products is established by the manufacturing times.

From the point of view of production planning and scheduling, knowledge of all the time components is needed. However, the most important thing is to know the machining times because they are the time components dominantly changing from task to task. Setting times and auxiliary times depending on the types of the current operation elements (the types of tools are also included) can easily be estimated by using additive and/or proportional time components.

The direct task of operation element planning then can be characterised by the following algorithm: (I) Planning the type of operation element; (2) Tool selection; (3) Determination of the objective function and the constraints (fixing

the depth of cut in a heuristic way is also included); (4) Optimisation (Computing Qo~,Ro~ ); (5) Planning the tool motions; (6) Time computations; (7) Post-processing (determining v and./).

If the optimisation is to be carried-out in the space of state ( Q, R, r ) the points having a chance to be optimum are bounded by the following characteristic constraints:

Q.,. =d,f . .v . , , Q.~ =d,f.~v.~. R.~ =R(/.,.,d,), R... =R(f.~,d,), Q.• <Q(d,,f,v)<_Q.~,

where d, is the depth of cut previously luted.

The indirect task appears in the course of solving the problem of "bottleneck" arising at the workshop level or cell level in the flexible small lot manufacturing (Soml6, 1980). A "bottleneck" can cause exceeding the deadline in manufacturing which can have serious negative consequences in the case of a non-linear (penalty - type) cost function. In this case, PPS may prescribe that intensity of stock-removal using which the scheduled deadlines can be fulfilled. Namely, increasing the intensity Q can result in decreasing the workshop costs, despite growing the costs of the workplace of "bottleneck".

OPERATION MANAGEMENT STRATEGIES

There can be the following management strategies to determine the possible maximum rate of stock removal (Scheer 1994, T6th and Erd~lyi 1995):

• total utilizati(m of the machine tool (bottleneck)

• the tool life required by production planning or production control (Tool Management System = TMS)

92 Proceedings o f the 24th International Conference on Computers and Industrial Engineering

• fulfilment of the requirements of Total Quality Management (TQM)

• slrict complying with the deadline of a given operations-series (Just-in-time). i I / I

' ~ , " ~ ~ / ~ ~

/

/

/ J

/ f

Qrnax

50 100 150 200 250 300 350 400 0 [em3.'m~)

250

a: 1 5 0 ~

O I~f~nJnl

Fig.l: The space of state ( r,Q,R ) Fig.2: Constraints and domains in ( r,Q,R )

According to this conception there are a lot of opportunities to integrate the process planning and production control activities in flexible manufacturing. Some of them are the as follows: (I) Process planning suggests several

alternative Q, T~,t, data for any operations/suboperations/operation elements (Alternative Process Planning); (2) Production planning transfers the selected value of the rate of stock-removal to the operation element planning level of CAPP (Distributed Process Planning); (3) Shop floor control may demand either decreased or increased rates of stock-removal on the base of f'me programming or the current status of workshop. For example, this requirement can be fulfilled by a secondary post-processing in case of Open CNC-machines.

CONCLUSIONS

Utilization of intensity type data (e.g.: rate of stock-removal) is very advantageous both from the point of view of optimum process plans and from the aspect of CAPP-PPS-CAM integration. They are as follows:

• Technological intensity expresses process planning and production control aspects at the same time.

• On the (Q, R ) plane both robust planning and indirect corrections (required by tool life, bottleneck, priority changing etc.) can easily be executed in a demonstrative way, for instance by means of a computer program.

• The CAPP-PPS-CAM integration based upon technological intensity is especially suitable for analysing and evaluating those sensibility features which appear in modem Computer Aided Manufacturing in the course of weighted handling of the following goals: time - cost- quality - quantity -profit.

REFERENCES

Detzky, I., Fridrik, L. and T6th, T. (1988). On a New Approach to Computerized Optimization of Cutting Conditions. Proceedings of the 2nd World Basque Congress, Advanced Technology and Manufacturing Conference, Bilbao. Vol.1. pp.129-141. Ravignani, G.L., Tipnis, V.A., Friedman, M.Y. (1977). Cutting Rate - Tool Life Functions (R-R-F). General Theory and Applications. Annals of the CIRP, Vol.25/1. pp.295-301.

Scheer, A.W. (1994). Computer Integrated Manufacturing. Towards the Factory of the Future. Springer Verlag, Berlin. Soml6, J. (1980). On a New Override Principle for Adaptively Controlled (AC) Machine Tools. Manufacont '80, IFAC, Budapest, pp. 101-106. T6th T. and Erdtlyi, F. (1995). The Inner Structure of Computer Aided Process Planning Having Regard to Concurrent Engineering. 2nd International Workshop on Learning in IMS. CIRP, Budapest, pp. 142-161.