Process capability analysis chart with the application of Cpm

Process capability analysis (PCA) is a highly effective means of assessing the process ability of product that meets specifications. The process capability analysis chart (PCAC/C_pk ) evaluates the capabilities of multiprocess products together with nominal-the-best specifications, larger-the-better and smaller-the-better specifications. This study proposes process capability analysis chart (PCAC/C_pm ) to consider process yield and expected process loss. A new generated estimator for C_pm is proposed and the properties of statistical estimator and hypothesis test are discussed. A practical example was given for application.     

Monitoring manufacturing quality for multiple Li-BPIC processes based on capability index C pmk

The multi-process performance analysis chart (MPPAC) based on process capability indices has been developed to analyse the manufacturing performance for multiple processes, which conveys critical information regarding the departure of the process mean from the target value, process variability, capability levels, which provides a guideline of directions for capability improvement. Existing MPPAC researches have plotted the sample estimates of the process indices on the chart. Conclusions were then made on whether processes meet the capability requirement and directions need to be taken for further quality improvement. Such an approach is highly unreliable since the sample point estimate is a random variable with no assessment of the sampling errors. Further, existing MPPAC researches only considered one single sample. Current quality control practice is to estimate process capability using multiple groups of control chart samples rather than one single sample. In this paper, we propose the C _pmk MPPAC combining the accuracy index C _a to access the performance of multiple manufacturing processes. Distributions of the estimated C _pmk and C _a are derived based on multiple control chart samples, and accurate lower confidence bounds are calculated. The lower confidence bounds of the estimated C _pmk and C _a are then employed to the MPPAC to provide reliable capability grouping for those multiple processes. A real-world example is presented to illustrate the applicability of the proposed MPPAC.     

工序能力分析与评价中的几个问题

在工序质量分析与控制中,计算与评价工序能力指数是一项非常重要的工作,也是计算机辅助质量系统的一个重要模块。文章针对目前在工序能力计算与分析中出现的问题,提出了如何合理地进行抽样、样本数据的正态性检验以及对非正态性数据的处理、Ｃｐ的置信区间以及与样本含量的关系,旨在为实际生产过程中质量工程师进行工序能力分析和评价提供指导。     

Process capability indices—an overview of theory and practice

A range of process capability indices is widely used to measure process performance. The simplicity of the formulae for these capability indices is both a strength and a weakness. The underlying assumptions behind capability indices are frequently overlooked. Capability studies usually result in single point estimates which may result in misleading assessments of process performance. Point estimates ignore sampling error, and safer estimates can be obtained by constructing confidence intervals. The construction of confidence intervals is considered in some detail. Testing or measurement variability give rise to additional uncertainty in process capability assessments. Inaccurate assessments of process performance can result if the basic assumptions and sources of uncertainty are overlooked.     

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.     

Multivariate process capability using principal component analysis

Wang and Chen (Qual. Eng. 1998; 11:21–27) have defined process capability indices (PCIs) for multivariate normal processes data using principal component analysis (PCA). Veevers (Statistical Process Monitoring and Optimization. Marcel Dekker: New York, NY, 1999; 241–256) has suggested a multivariate capability index based on the first principal component (PC). In this paper we demonstrate the problem in the definition of PCIs given by Wang and Chen (Qual. Eng. 1998; 11:21–27) and the non‐suitability of PCI given by Veevers (Statistical Process Monitoring and Optimization. Marcel Dekker: New York, NY, 1999; 241–256) through some examples. We also suggest an alternative method for assessing multivariate process capability based on the empirical probability distribution of PCs. This method has been performed on industrial and simulated data. Copyright © 2008 John Wiley & Sons, Ltd.     

Stability Analysis of Transfer Alignment Filter Based on the μ Theory

The performance of the transfer alignment has great impact on inertial navigation systems. As the transfer alignment is generally implemented using a filter to compensate the errors, its accuracy, rapidity and anti-disturbance capability are key properties to evaluate the filtering process. In terms of the superiority in dealing with the noise, H∞ filtering has been used to improve the anti-disturbance capability of the transfer alignment. However, there is still a need to incorporate system uncertainty due to various dynamic conditions. Based on the structural value theory, a robustness stability analysis method has been proposed for the transfer alignment to evaluate the impact of uncertainty on the navigation system. The mathematical derivation has been elaborated in this paper, and the simulation has been carried out to verify the effectiveness of the algorithm.     

Supplier Selection Based on Process Capability and Price Analysis

Supplier selection is an important part of supply chain management. Among the numerous methods that have been proposed, process capability index is considered to be the most effective technique for identifying quality parts. However, supplier selection should be carried out on the basis of quality and cost together. There is no easy tool available to evaluate the price and quality in an integrated manner. In this article, a new approach to supplier selection using capability index and price comparison (CPC) chart is presented. The CPC chart integrates the process capability and price information of multiple suppliers and presents them in a single chart. It provides a simple but effective method to consider quality and price simultaneously in the supplier selection process.     

Multistage parallel algorithm for diffraction tomography

A multistage parallel algorithm with iterative processing is discussed for the processing of information in diffraction tomography. The algorithm is based on matrix partitioning, which results in mostly parallel stages of processing. Each successive stage is designed to minimize the remaining error. The process is iterated until convergence. The major advantages of the multistage algorithm are the reduced computational time from faster convergence as compared with a single-stage iterative algorithm, further reduction of computation time if the stages are implemented mostly in parallel, and better performance in terms of reduced reconstruction error.     

Postfabrication optimization of an autoregressive planar waveguide lattice filter

Narrow-band planar waveguide filters are sensitive to fabrication errors that make it difficult to realize multistage filters successfully and maintain tight center wavelength tolerances unless a tuning technique is available. An analysis algorithm combined with waveguide heaters has been successfully used to demonstrate that fabrication errors can be compensated in an autoregressive (AR) lattice architecture that makes it possible to concatenate conveniently multiple stages and to achieve higher-order filter functions. Compensation of fabrication errors on filter functions for a second- and third-order AR lattice filter is presented, and issues related to the application of the analysis algorithm are discussed. In combination with postfabrication tuning of the coupling ratios, the analysis algorithm will allow complete control of the filter function after fabrication.     

The stochastic control of process capability indices

In manufacturing science, process capability indices play a role analogous to economic indices in government statistics. The existing capability indices are passive devices whose main role is to retroactively monitor process capability. The have been developed under the restrictive assumption of process stability, and the procedures for using them are based on ad hoc rules. Using the normative point of view for decision making, it can be shown that some of the indices are, at best, convoluted special cases of a more general strategy; they can be justified only under special assumptions, and the manner in which they are currently used could lead to incoherent actions. The available process capability indices should therefore be abandoned and replaced by procedures that are normative, and also proactive with respect to both, prediction and control. An approach towards achieving this goal is proposed. Research Sponsored by The National Institute of Standards and Technology Gaithersburg, Maryland 20899-0001 (Under Purchase Order No. 43NANB610868), The U.S. Army Research Office Grant DAAG-55-97-1-0323, and The Air Force Office of Scientific Research Grant AFOSR-F-49620-95-0107     

MINIMIZATION OF SCRAP AND REWORK COSTS BY PROCESS TOLERANCES ALLOCATION

The design of process tolerances plays a vital role in process planning, which is to design a sequence of operations for converting raw materials into the desired shape. The conventional manual approach and previous mathematical models lack the capability of predicting the probabilities of scrap and rework, and the process capability of machinery was not taken into account. As a result, the tolerances allocated may not be achieved economically. This study proposes a cost-effective means for assigning process tolerances to minimize the cost incurred by scrap and rework. Comparison with other methods indicate that the proposed model is ideal for designing process tolerances.     

ANALYSIS OF A MARKOV CHAIN MODEL OF A MULTISTAGE MANUFACTURING SYSTEM WITH INSPECTION, REJECTION, AND REWORK

The authors present an informative application of Markov Chain Analysis to a multistage manufacturing problem. They also point out an error in the literature which has remained undetected for many years.

Edward A. Silver, Department Editor

Absorption analysis is applied to a Markov chain model of a multistage manufacturing process with inspection and reworking.     

Solving a multistage partial inspection problem using genetic algorithms

Traditionally, the multistage inspection problem has been formulated as consisting of a decision schedule where some manufacturing stages receive full inspection and the rest none. Dynamic programming and heuristic methods (like local search) are the most commonly used solution techniques. A highly constrained multistage inspection problem is presented where all stages must receive partial rectifying inspection and it is solved using a real-valued genetic algorithm. This solution technique can handle multiple objectives and quality constraints effectively.     

Improving Process Capability of Manufacturing Process by Application of Statistical Techniques

This article describes the application of statistical techniques in solving a problem of high rejection and rework due to variation in the machining process. The rejections in the shop floor of a company were studied through Pareto analysis for prioritizing the problems due to different sources. The grinding process was found to be the most contributing among all the processes towards rejection and rework. The process capability of this grinding process was evaluated and found to be very low. A step by step method was adopted by the application of several statistical techniques such as design of experiments for solving this problem. As a result of the study, the process capability has improved drastically. This has lead to reduction in the problems due to high rejection and rework in shop floor.     

Optimal process capability analysis for process design

Conventional process capability analysis is used to measure and control the quality level of a production process in real exercises for on-line quality management. There has been a deficiency in this type of management; namely, the defects which occur in the production process are only passively detected and modified afterwards. Additionally, conventional process capability expression has difficulty distinguishing between alternatives for process selection among possible candidates before process realisation. There is, therefore, considerable motivation for developing a process capability expression which can be used to evaluate alternatives at the beginning of the process design, i.e., off-line application. The conventional C_pm expression is built up by measuring mean deviation and process variances for on-line application. However, if C_pm is used for the process capability analysis for process design, an erroneous C_pm value is found and an inappropriate process design is ended. Thus, the proposed process capability expression revised from the conventional C_pm in consideration of the balance between tolerance cost and quality loss has been developed. This development is the main contribution of this research and, with this development, the appropriate mean and tolerance values can be determined simultaneously prior to the real production process so as to maximise the proposed process capability value. The production is then processed with the pre-determined mean and tolerance values in a real production process. The expectation after process realisation is that the produced responses will be the best of all the alternatives in terms of quality and cost, and that the process capability value obtained after the real production process will be close to the proposed process capability value maximised prior to the real production process.     

Application of a Genetic Algorithm on the Assessment of the Capabilities of Cctuator Systems for Laser Mircro Adjustment

Laser forming with actuator systems is a very attractive process to meet the rising demand for accuracy in micro system technology. However, the actuator systems have to be designed specifically for the given adjustment task. A computer assisted design system for actuator systems for laser based micro adjustment has been developed which supports the designer by creating a number of optimized design choices from a starting design. A multiobjective genetic algorithm has been successfully applied to this task. The algorithm for evaluation of adjustment capability of the actuator system uses another single objective genetic algorithm to determine the forming capability of the actuator system. The approach is similar to the inverse kinematics algorithm used in robotics but also considers elastic forming. The irradiated components of the actuator system are treated as robotic links for which the amount of laser based forming are the link parameter. An elastic structural mechanic simulation of the actuator system with beam elements calculates the resulting adjustment movement of the actuator system. The research performed to develop a configuration of this algorithm specifically to the needs of actuator system design evaluation is presented in this paper.     

Mathematical modeling of the heat treatment and combustion of a coal particle. I. Heating stage

Mathematical modeling of the heat treatment and subsequent combustion of a coal particle as a multistage process has been carried out. The basic parameters of the following sequential stages of this process have been calculated by approximate-analytic dependences: heating of particles; their drying; yield of volatiles, their ignition and combustion; and burning out of the coke residues. A detailed parametric analysis of the influence of the physical and regime characteristics of the process on the burning mechanism of a coal particle (with the example of coal from the Shivee-Ovoo deposit in Mongolia) has been performed. The conditions for effective burning of a single coal particle as the main element of the whole process in the furnace have been determined.     

Application of knowledge-based artificial immune system (KBAIS) for computer aided process planning in CIM context

In the present era, several manufacturing philosophies like lean manufacturing, total quality management (TQM), etc., have the goal of providing a quality product at reduced cost. In this research paper the process planning problem of a CIM system has been discussed where minimisation of cost of the finished product is considered as the main objective. For determining the cost of the finished product, scrap cost, forgotten by most of the previous researchers, has been considered along with other costs like raw material cost, processing cost, etc. In the present environment of concurrent engineering, optimisation of process planning is an NP-hard problem. To solve this complex problem a noble search algorithm, known as knowledge-based artificial immune system (KBAIS) has been proposed. The nobility of the proposed algorithm is that the inherent capability of AIS has been gleaned and incorporated with the property of the knowledge base. In this problem, the power of knowledge has been used for three stages in the algorithm: initialisation, selection and hyper-mutation. To demonstrate the efficacy of the proposed KBAIS, a bench mark problem has been considered. Intensive computational experiments have also been performed on randomly generated datasets to reveal the supremacy of the proposed algorithm over other existing heuristics.