A Tutorial to Developing Statistical Models for Predicting Disqualification Probability

A Tutorial to Developing Statistical Models for Predicting Disqualification Probability

Ilmari Juutilainen (University of Oulu, Finland), Satu Tamminen (University of Oulu, Finland) and Juha Röning (University of Oulu, Finland)
DOI: 10.4018/978-1-4666-0128-4.ch015
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Different industries utilize statistical prediction models that predict the product properties in process planning, control, and optimization. An important aim is to decrease the number of disqualifications. The model can prevent disqualifications efficiently if the disqualification probability is predicted accurately. This study gives step-by-step instructions for developing, validating, comparing, and visualizing models that predict the disqualification probability with high accuracy. The work summarizes industrially applicable statistical modeling methods that are most suitable for the development of accurate predictors for the disqualification probability. Currently, the information on such statistical methods, e.g. quantile regression, modeling of distribution shape, and joint modeling of mean and deviation, is scattered in the existing literature. The main contribution of this work is that it pulls together this methodology into a unified framework which allows the comparative analysis of probability predictors that are based on the different approaches. The proposed modeling procedure (ProPred) is demonstrated using three manufacturing industry applications. In the case applications, the predictors generated using the ProPred procedure are 10-30% more efficient in avoiding disqualifications by means of process planning and control operations than the baseline predictors.
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As the competition in product quality and demand for high-quality and energy-efficient products grow, manufacturing industries will meet more quality challenges. The quality requirements are becoming stricter and the cycle of new product development is shortening. Many of these quality challenges can be solved in the terms of statistical methodology (Hahn & Doganaksoy, 2008; Montgomery, 2009). The importance of the reduced variation in product properties was initially lifted to everyday practice of process improvement by Taguchi (Taguchi, 1987). Later, several another approaches to reducing variation in product properties has been proposed, e.g. (Steiner & Mackay, 2005). Also, the business process improvement practice Six Sigma pays attention on the reduction of the costs associated with the variation (Pyzdek & Keller, 2009). The objective is to meet the quality requirements with optimized cost. The disqualification rate can be decreased by accurate process control and well-done planning of production, products and processes. As the numerous applications have proved, statistical models that predict the dependence of the conditional distribution of product properties on the source information and process variables are useful tools to improve the planning and process control practices in manufacturing industries. The modeling of tail distribution behavior has become an important tool for risk management (Crouhy, Galai & Mark, 2005).

Rapidly growing amounts of data are measured from the production process and stored into process databases. In addition to the quality assurance measurements on product properties, the process data contain measurements on variables that cause the desired properties and the variation in it. Typically, process data bases include at least the amount and properties of the process input materials, the planned process settings, the target values for the process variables and real-time measurements on the realized process variables. (Miletic, Quinn, Dudzic, Vaculik, & Champagne, 2004).

Designed experiments have a long history on the primary data source of statistical quality improvement and for the estimation of prediction models for the utilization in planning and control (Montgomery, 2009). In comparison to the designed experiments, the advantage of process databases is the larger number and cheaper prize of data points. Thus, the production data provide a very competitive data source for the estimation of statistical models for the needs of control and planning (Harding, Shahbaz, Srinivas & Kusiak, 2006). This chapter is a methodological guide that relies on experience on several applications in which process data is utilized for the development and estimation of prediction models which are then taken into action in the planning and control in different manufacturing industry applications.

This chapter describes a step-by-step approach to the estimation and selection of statistical models that predict accurately the probability of disqualification. The aim is to provide a guide which helps to develop models to be used for predicting and simulating the effect of source information on the risk of disqualification. Models like this can give significant benefits when utilized by integrating them into software that are used in process control and in the planning of products, production and processes (Choudhary, Harding, & Tiwari, 2009). The benefits are realized as the decreased number of disqualifications, decreased variation in the product properties and savings in the consumption of raw materials and energy.

Our claim is that disqualifications related to failing with quality requirements can be decreased in many industries by the following ways:

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Table of Contents
J. Paulo Davim
J. Paulo Davim
Chapter 1
Pranab K. Dan, Tamal Ghosh, Sourav Sengupta
The essential problem in Cellular Manufacturing System (CMS) is to identify the machine cells and subsequent part families with an aim to curtail... Sample PDF
Application of Soft-Computing Methods in Cellular Manufacturing
Chapter 2
M. Kanthababu
Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems.... Sample PDF
Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms
Chapter 3
F. Nagata, T. Yamashiro, N. Kitahara, A. Otsuka, K. Watanabe, Maki K. Habib
Multiple mobile robots with six PSD (Position Sensitive Detector) sensors are designed for experimentally evaluating the performance of two control... Sample PDF
Self Control and Server-Supervisory Control for Multiple Mobile Robots, and its Applicability to Intelligent DNC System
Chapter 4
M. Chandrasekaran, M. Muralidhar, C. Murali Krishna, U.S. Dixit
In offline optimization of machining process with traditional or soft computing techniques, the functional relationship between the tool life and... Sample PDF
Online Machining Optimization with Continuous Learning
Chapter 5
N. A. Fountas, A. A. Krimpenis, N. M. Vaxevanidis
Extracting CNC machining data on- or off-line demands thorough and careful planning. Exploitation of this data can be carried out by statistical... Sample PDF
Computational Techniques in Statistical Analysis and Exploitation of CNC Machining Experimental Data
Chapter 6
V. N. Gaitonde, S. R. Karnik, J. Paulo Davim
The tungsten-copper electrodes are used in the manufacture of die steel and tungsten carbide workpieces due to high thermal and electrical... Sample PDF
Application of Particle Swarm Optimization for Achieving Desired Surface Roughness in Tungsten-Copper Alloy Machining
Chapter 7
Shutong Xie, Zidong Zhang
Machining parameters optimization is one of the most essential and interesting problems in manufacturing world. Efficient optimization of machining... Sample PDF
Models and Optimization Techniques of Machining Parameters in Turning Operations
Chapter 8
A.P. Markopoulos
Simulation of grinding is a topic of great interest due to the wide application of the process in modern industry. Several modeling methods have... Sample PDF
Simulation of Grinding by Means of the Finite Element Method and Artificial Neural Networks
Chapter 9
K. Palanikumar, B. Latha, J. Paulo Davim
Glass fiber reinforced plastic (GFRP) composite materials are continuously displacing the traditional engineering materials and are finding... Sample PDF
Application of Taguchi Method with Grey Fuzzy Logic for the Optimization of Machining Parameters in Machining Composites
Chapter 10
Alakesh Manna
In this chapter, the use of Taguchi method, Fuzzy logic, and Grey relational analysis based on an L16 (45) orthogonal array for optimizing the multi... Sample PDF
Taguchi, Fuzzy Logic and Grey Relational Analysis Based Optimization of ECSM Process during Micro Machining of E-Glass-Fibre-Epoxy Composite
Chapter 11
Tauseef Uddin Siddiqui, Mukul Shukla
This chapter presents a detailed study of abrasive water jet (AWJ) cutting of thin and thick Kevlar fiber-reinforced polymer (FRP) composites used... Sample PDF
Modeling and Optimization of Abrasive Water Jet Cutting of Kevlar Fiber-Reinforced Polymer Composites
Chapter 12
Robertt A. F. Valente, Ricardo J. Alves de Sousa, António Andrade-Campos, Raquel de-Carvalho, Marisa P. Henriques, José I. V. Sena, João F. Caseiro
This contribution aims to provide a comprehensive overview of some research developments in the field of computational mechanics and numerical... Sample PDF
Developments in Finite Element Technology and Optimization Formulations for Sheet Metal Forming
Chapter 13
Luis M. M. Alves, Paulo A. F. Martins
This chapter presents an innovative forming process for joining sheet panels to tubular profiles at room temperature. Finite element analysis and... Sample PDF
Joining Sheets to Tubular Profiles by Tube Forming
Chapter 14
R. Venkata Rao
Weld quality is greatly affected by the operating process parameters in the gas metal arc welding (GMAW) process. The quality of the welded material... Sample PDF
Modeling and Optimization of Gas Metal Arc Welding (GMAW) Process
Chapter 15
Ilmari Juutilainen, Satu Tamminen, Juha Röning
Different industries utilize statistical prediction models that predict the product properties in process planning, control, and optimization. An... Sample PDF
A Tutorial to Developing Statistical Models for Predicting Disqualification Probability
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