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