ravikanth vangala, dr. moon w. suh · 2016. 3. 1. · ravikanth vangala, dr. moon w. suh...
Post on 24-Sep-2020
6 Views
Preview:
TRANSCRIPT
Literature has been surveyed to identify the essential properties of fiber clusters that can generate variances in processing
Structural relationships connect the factors in a given process stage to that of the next stage
General form of a structural eqn. connecting any two stages:
Ravikanth Vangala, Dr. Moon W. Suh rkvangal@ncsu.edu, moon_suh@ncsu.edu
Department of Textile and Apparel, Technology and Management, College of Textiles, NCSU
Traditionally used static control systems are inflexible to accommodate complex and
dynamic nature of textile production
The results are frequent false alarms and unwarranted process calibrations leading to loss
in production time, materials and profits
These control systems are completely void of structural models and prediction equations
published to date
Hence, there is a need for development of a dynamic quality control system that utilizes the
known structural models linking the process input to the output variables
INTRODUCTION
FUTURE RESEARCH
EXTENSION TO CONTINUOUS TEXTILE WET PROCESSING
The concept of an on-line real-time dynamic control system will be applied to a
continuous textile wet process by considering all relevant input and output factors
SOFTWARE DEVELOPMENT
A software system will be developed which can graphically represent the dynamic
control charts along with dynamic process averages and standard deviations
PURPOSE OF THIS STUDY
To design and develop an effective Dynamic Quality Control System (DQCS)
To survey, analyze and develop structural equations in staple spinning
To develop the concept of FAMSE Algorithm for consolidating multiple structural equations
To apply the concept of Variance Tolerancing and Channeling in staple spinning
To develop a system software package of DQCS
SURVEY, ANALYSIS AND DEVELOPMENT OF STRUCTURAL EQUATIONS
SCHEMATIC DIAGRAM OF A DYNAMIC CONTROL CHART
Control Limits based on Static Process Average
and Variance
Control Limits based on Dynamic Process Average and Variance
CONCEPTS INCORPORATED IN THE SYSTEM DESIGN
Each (i) of the k stages generates
biases (Bi) and variances (σi
2)
Biases and variances keep
accumulating from previous stages
BT must be separated from σT
2 to
obtain the dynamic control limits
Additive Effects of System biases and variances from Mixing/Blending
to Ring-frame in Staple Spinning process
ADDITIVE EFFECTS OF SYSTEM BIASES AND VARIANCES
Schematic Diagram of Inheritance of Variance from Previous Process Stages
ESTIMATION OF PROCESS VARIANCES AT EACH STAGE
Density profiles of sliver, roving and
yarn in 3 successive processing stages
Variance components inherited sum up
with Variance components generated
MAGNIFICATION OF BIASES ON SUBSEQUENT PROCESS STAGES
Structural equations form the link between any two stages of a continuous processing industry
They uncover the particular parameters and magnify the cause of the bias
FEEDBACK / FEEDFORWARD CONTROL MECHANISMS
To eliminate effect of process disturbances and to keep the process within specification limits – a
combination of feedback/ feedforward control mechanism is employed
Control Limits based on total Biases and Variances
generated in the process
Dynamic Process Average and deviation based on Mass Variation in Spinning
A new concept for separation and estimation of random errors associated with raw
materials and yarn structures using structural relationships (Suh et al. – TRJ 2001)
Estimates variance of a textile product characteristic based on structural equations
The output mass variance obtained at a spinning frame can be expressed as:
(since the other terms are constants)
APPLICATION EXAMPLE: ESTIMATING VARIANCE OF MASS VARIATION IN SPINNING
VARIANCE TOLERANCING TECHNIQUE
Variance tolerancing is accomplished by estimating the variance of the “output mass
variance” as a function of the input variances from the previous processes
can be expanded by using a Taylor’s series expansion with a = μ:
The “output mass variance of a yarn” as a function of the input mean () and input
variance () is:
Opening/mixing: Deviation in feed (input) σfeed is derived as:
Carding: Output mass variation ‘V’ in carded web is given by equation:
Drawing: Mass variation in drawframe sliver as per ‘Law of Drafting’:
Spinning: Substituting relative variance ( ) obtained at roving frame in variance equation at drawing, we get: Vα2
Substituting the input mass variance (Vo) value from carding:
As an application example, we have developed a structural equation for mass variation in staple spinning and computed the
expected levels of mass variation for obtaining dynamic control limits
APPLICATION EXAMPLE : MASS VARIATION IN SPINNING
CONTRIBUTION TO THE INDUSTRY
,
,
By monitoring quality using this novel technique of Dynamic Quality Control System,
textile producers and manufacturers (both domestic and international) will be able to:
generate optimal control strategy at each process stage through more accurate
control limits
minimize/eliminate the unnecessary corrective actions (false positives) by
determining the actual root-causes of out-of-control situations
minimize the impact when the out-of-control causes an irreversible damage
reduce quality costs by minimizing loss of production time and materials
increase their competitive position in the globe by producing high quality textile
products at lowest price
top related