Optimal concurrent product design and process planning based on the requirements of individual customers in one-of-a-kind production

One-of-a-kind production is a new manufacturing paradigm for producing customised products based on the requirements of individual customers while maintaining the quality and efficiency of mass production. This research addresses the issues in optimal concurrent product design and process planning based on the requirements of individual customers. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in a generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimisation are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study is introduced to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products of an industrial company to demonstrate the effectiveness of the developed method.     

Combining iterative heuristic optimization and uncertainty analysis methods for robust parameter design

A number of investigators have pointed out that products and processes lack quality because of performance inconsistency, which is often due to uncontrollable parameters in the manufacturing process or product usage. Robust design methods are aimed at finding product/process designs that are less sensitive to parameter variation. Robust design of computer simulations requires a large number of runs, which are very time consuming. A novel methodology for robust design is presented in this article. It integrates an iterative heuristic optimization method with uncertainty analysis to achieve effective variability reductions, exploring a large parameter domain with an accessible number of simulations. To demonstrate the effectiveness of this methodology, the robust design of a 0.15 μm CMOS device is shown.     

Optimal process parameter determination for computer‐aided manufacturing

Producing high‐quality products at low cost is one of the key factors to survival for manufacturing sectors in today's intense global competition environment. One way to gain competitiveness is to integrate product design and process planning into one activity. This study attempts to determine optimal process parameters for a manufacturing process under given design parameters. The process parameters to be determined in this study include process means and process tolerances for particular manufacturing process sequences. The problem is formulated in constrained non‐linear optimization, considering both quality‐ and manufacturing‐related costs. The proposed application evaluates alternative product designs and process sequences so that the best associated process parameters can be determined during the early stages of design and planning. This makes the link between CAD and CAM systems more useful and effective. As a result, optimal integration of product design and process planning with minimal production costs and maximal product quality is possible. Copyright © 1999 John Wiley & Sons, Ltd.     

Investigation on the effect of process parameters in atmospheric pressure plasma treatment on carbon fiber reinforced polymer surfaces for bonding

Carbon Fiber Reinforced Polymer (CFRP) is used extensively in aerospace applications. Acceptance of bonded CFRP structures, mainly for aerospace applications, requires a robust surface preparation method with improved process controls to ensure high bond quality. Consistent repeatability is a factor lacking from many surface preparation processes. Atmospheric pressure plasma surface treatment is one of the robust surface preparation processes that have drawn wide attention in recent years. This process is capable of being applied in a production clean room environment that would minimize the risk of contamination and reduce cost. In plasma surface treatment the process parameters are easily controlled, documented providing a repeatable process with a high level of consistency. In this paper, the process parameters for atmospheric pressure plasma surface treatment and their effect on bonding for Out-Of-Autoclave (OOA) CFRP composite panels were fully investigated. A mechanized machine with sensory feedback to plasma treat surfaces was developed to change the process parameters for application on larger panels. By the aid of Design of Experiment (DOE) methodology critical process parameters were identified and a mathematical regression model was developed. The mathematical regression model was used to quantify the effect of process parameters on the bonding strength and the model was optimized to find the optimum settings.     

Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

A shadow mask, the primary component of a cathode ray tube (CRT), is used to prevent the outer edges of electron beams from hitting incorrect phosphor dots. It is fabricated by means of a photo-etching process consisting of a few hundred/thousand process parameters. A primary concern in the management of the process is to determine the optimal process parameter settings necessary to sustain the desired levels of product quality. The characteristics of the process, including a large number of process parameters and collinear observed data, make it difficult to accomplish the primary concern. To cope with the difficulties, a two-phase approach is employed that entails the identification of a few critical process parameters, followed by determination of the optimal parameter settings. The former is obtained through the operator's domain knowledge and the NNPLS-based prediction model built between process parameters and quality defects. The latter is obtained by solving an optimization problem using a genetic algorithm (GA). A comparative study shows that the proposed approach improves product quality greatly in the shadow-mask manufacturing process.     

A RFID-based recursive process mining system for quality assurance in the garment industry

With the increasing concern about product quality, attention has shifted to the monitoring of production processes to be assured of good quality. Achieving good quality is a challenging task in the garment industry due to the great complexity of garment products. This paper presents an intelligent system, using fuzzy association rule mining with a recursive process mining algorithm, to find the relationships between production process parameters and product quality. The goal is to derive a set of decision rules for fuzzy logic that will determine the quantitative values of the process parameters. Learnt process parameters used in production form new inputs of the initial step of the mining algorithm so that new sets of rules can be obtained recursively. Radio frequency identification technology is deployed to increase the efficiency of the system. With the recursive characteristics of the system, process parameters can be continually refined for the purpose of achieving quality assurance. A case study is described in which the system is applied in a garment manufacturing company. After a six-month pilot run of the system, the numbers of critical defects, major defects and minor defects were reduced by 7, 20 and 24%, respectively while production time and rework cost improved by 26 and 30%, respectively. Results demonstrate the practical viability of the system to provide decision support for garment manufacturers who may not be able to determine the appropriate process settings for achieving the desired product quality.     

Robust Parameter Design for Profile Quality Control

In certain manufacturing processes, product quality is characterized by spatial profiles, and such profiles are expected to meet specific shape requirements. As profile shapes are affected by process conditions, properly adjusted process variables are expected to help improve profile quality. This work aims to achieve desired shapes of profiles that are sensitive to the variation of noise factors through optimizing settings of controllable factors. A hierarchical model is first built to characterize the spatial correlation of measurement points on a profile and link quality metrics with process variables. The process is then optimized using the robust parameter design technique. The performance of the proposed method is studied using a motivating example from nanomanufacturing. Copyright © 2015 John Wiley & Sons, Ltd.     

Partial least square-based model predictive control for large-scale manufacturing processes

The Model Predictive Control (MPC) method has been widely adopted as a useful tool to keep quality on target in manufacturing processes. However, the conventional MPC methods are inadequate for large-scale manufacturing processes particularly in the presence of disturbances. The goal of this paper is to propose a Partial Least Square (PLS)-based MPC methodology to accommodate the characteristics of a large-scale manufacturing process. The detailed objectives are: (i) to identify a reliable prediction model that handles the large-scale "short and fat" data; (ii) to design an effective control model that both maximizes the required quality and minimizes the labor costs associated with changing the process parameters; and (iii) to develop an efficient optimization algorithm that reduces the computational burden of the large-scale optimization. The case study and experimental results demonstrate that the presented MPC methodology provides the set of optimal process parameters for quality improvement. In particular, the quality deviations are reduced by 99.4%, the labor costs by 84.2%, and the computational time by 98.8%. As a result, the proposed MPC method will save on both costs and time in achieving the desired quality for a large-scale manufacturing process.     

Optimal initial production control period based on a quality growth model

The initial production phase of new products or the initial installation phase of new manufacturing facilities is often unstable because of inexperienced workers and many defective products. An initial production process control, in which the defects in design, production technologies and products are fully fixed and removed, is switched to a normal process control whenever it is ready for actual mass production. This paper discusses a method of deciding the optimal initial production control period, based on a quality growth model. It is determined by the number of products with the minimum expected total quality control cost. Finally a penalty cost due to unattainable loss to the quality goal is introduced in the quality control cost: the realized stabilization level of the initial production process control is lower than the original quality objective. Numerical illustrations of the optimal policy are also presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Six sigma-based approach to optimize deep drawing operation variables

The six sigma approach has been increasingly adopted worldwide in the manufacturing sector in order to enhance the productivity and quality performance and to make the process robust to quality variations. This paper deals with one such application of six sigma methodology to improve the yield of deep drawing operations. The deep drawing operation has found extensive application in producing automotive components and many household items. The main issue of concern of the deep drawn products involves different critical process parameters and governing responses, which influences the yield of the operation. The effects of these parameters are analysed by the DMAIC (Define, Measurement, Analyse, Improve, Control)-based six sigma approach. A multiple response optimization model is formulated using the fuzzy-rule-based system. The functional relationship between the process variables and the responses is established, and thereafter their optimum setting is explored with the aid of response surface methodology (RSM). Rigorous experimentations have been carried out, and it is observed that the process capability of processes is enhanced significantly, after the successful deployment of the six sigma methodology.     

Multiresponse optimization of a multistage manufacturing process using a patient rule induction method

Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage affects the stage that follows, and the process often has multiple response variables. In this paper, we suggest a new procedure for optimizing a multistage process with multiple response variables. Our method searches for an optimal setting of input variables directly from operational data according to a patient rule induction method (PRIM) to maximize a desirability function, to which multiple response variables are converted. The proposed method is explained by a step-by-step procedure using a steel manufacturing process as an example. The results of the steel manufacturing process optimization show that the proposed method finds the optimal settings of input variables and outperforms the other PRIM-based methods.     

A Novel Approach to Simultaneous Robust Design of Product Parameters and Tolerances Using Quality Loss and Multivariate ANOVA Concepts

Design and development of high quality products are of utmost importance to any production plant. Product design consists of parameter design and tolerance design, which affect the product performances and the manufacturing costs, respectively. Most products involve more than one quality feature. Design and development of such products raise multi‐response surface problems in which it is necessary to determine the optimal values of parameters and the tolerances for all responses simultaneously. In this research, an approach for simultaneous robust parameter and tolerance design is proposed to deal with multi‐response problems. The proposed method employs quality loss concept and one‐way multivariate analysis of variance. Two simulation studies are performed to validate the applicability of the proposed method. Research findings show that the proposed method performs better in quality improvement as well as in cost reduction than the existing methods. The variances of the responses are also lower than those of the other methods, that is, the proposed method results in a more robust approach to product design. Copyright © 2016 John Wiley & Sons, Ltd.     

Production policies under deteriorating process conditions

This paper examines a single-stage production system that manufactures multiple products under deteriorating equipment conditions. The machine condition worsens with production, and improves with maintenance. The condition of the process can be in any one of several discrete states, and transitions from state to state follow a semi-Markov process. In many production environments, the quality or yield of output depends heavily on the condition of the production process. The problem considers the trade-offs between manufacturing products that have a higher profit, a longer processing time, and therefore, a higher deterioration probability versus products that have a smaller profit, shorter processing time with a lower process deterioration probability. The firm needs to determine the optimal production choice in each state in a way that maximizes the long-run expected average reward per unit time.

The paper makes three sets of contributions. First, it introduces the concept of critical ratios for the firm's manufacturing decision at each state regarding whether to switch from one product to another. Second, through the use of critical ratios, the main result shows that the optimal production choice for each state can be determined independently of the actions taken in other states, despite the complex interconnections between the production decisions and state transitions. Third, the paper provides generalizations that illustrate the depth, scope and richness of the proposed solution technique by extending the model in the number of machine states, to settings where maintenance is performed in intermediate states, and to settings where transition probabilities are influenced by both mean and variance of processing times.     

Deriving control parameter settings from process models to control capsule fillers integrated into continuous manufacturing

The aim of this work was to investigate the mean fill weight control of a continuous capsule-filling process, whether it is possible to derive controller settings from an appendant process model. To that end, a system composed out of fully automated capsule filler and an online gravimetric scale was used to control the filled weight. This setup allows to examine challenges associated with continuous manufacturing processes, such as variations in the amount of active pharmaceutical ingredient (API) in the mixture due to fluctuations of the feeders or due to altered excipient batch qualities. Two types of controllers were investigated: a feedback control and a combination of feedback and feedforward control. Although both of those are common in the industry, determining the optimal parameter settings remains an issue. In this study, we developed a method to derive the control parameters based on process models in order to obtain optimal control for each filled product. Determined via rapid automated process development (RAPD), this method is an effective and fast way of determining control parameters. The method allowed us to optimize the weight control for three pharmaceutical excipients. By conducting experiments, we verified the feasibility of the proposed method and studied the dynamics of the controlled system. Our work provides important basic data on how capsule filler can be implemented into continuous manufacturing systems.     

Quality optimization (multi‐characteristics) through Taguchi's technique and utility concept

The work of Taguchi for determining the optimal settings of controllable factors through off‐line experiments focuses on products with a single quality characteristic or response. However, most products have several quality characteristics or responses of interest. Taguchi's technique in itself optimizes a single response or performance characteristic yielding a set of process parameters. This particular setting may not give desired results for other characteristics of the product. In such cases, a need arises to obtain a single setting (optimal setting) of the process parameters, which can be used to produce the products with optimum or near optimum quality characteristics as a whole. Multi‐characteristic response optimization may be the solution of the above problem. In the present paper a case study on V‐processed castings of Al–7%Si alloy, utilizing a simplified multi‐criterion methodology based on Taguchi's approach and utility concept, is discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

Stochastic analysis and robust optimization for a deck lid inner panel stamping

FE-simulation and optimization are widely used in the stamping process to improve design quality and shorten development cycle. However, the current simulation and optimization may lead to non-robust results due to not considering the variation of material and process parameters. In this study, a novel stochastic analysis and robust optimization approach is proposed to improve the stamping robustness, where the uncertainties are involved to reflect manufacturing reality. A meta-model based stochastic analysis method is developed, where FE-simulation, uniform design and response surface methodology (RSM) are used to construct meta-model, based on which Monte-Carlo simulation is performed to predict the influence of input parameters variation on the final product quality. By applying the stochastic analysis, uniform design and RSM, the mean and the standard deviation (SD) of product quality are calculated as functions of the controllable process parameters. The robust optimization model composed of mean and SD is constructed and solved, the result of which is compared with the deterministic one to show its advantages. It is demonstrated that the product quality variations are reduced significantly, and quality targets (reject rate) are achieved under the robust optimal solution. The developed approach offers rapid and reliable results for engineers to deal with potential stamping problems during the early phase of product and tooling design, saving more time and resources.     

Aggregate production process design in global manufacturing using a real options approach

A main source of competitive advantage is derived from the cost efficiency offered by firms’ manufacturing and logistics operations. Consequently, firms typically globalise their operations whereby they may exploit the comparative advantages—defined as production functions—of the nations in which they are present. Production process design thus arises as a significant issue. The research presented in this paper targets two fundamental questions attached to production process design that multinational companies face, namely: (i) should plants that are located in different countries but producing similar products use similar production processes?; and (ii) given that the firm's policy is to use similar production processes, how should the production processes be designed? Among others, the paper shows, by way of a numerical illustration of a binational manufacturing network, that the option of choosing freely upon production process design for the respective facilities in certain cases adds little to firm value. In fact, the value of this option tends to zero as the volatility rate increases when the exchange rate is modelled as a geometric Brownian motion without drift rate, implying that firms should employ similar production processes throughout their manufacturing networks. That is, a market value approach stands up for the so-called copy-exactly approach to production process design in these settings. We furthermore show the effects of economies of scale on the optimal production process design.     

An Economic Approach to the Management of High‐Quality Processes

Modern manufacturing developments have forced researchers to investigate alternative quality control techniques for high‐quality processes. The cumulative count of conforming (CCC) control chart is a powerful alternative approach for monitoring high‐quality processes for which traditional control charts are inadequate. This study develops a mathematical model for the economic design of the CCC control chart and presents an application of the proposed model. On the basis of the results of the application, the economic and classical CCC control chart designs of the CCC control chart are compared. The optimal design parameters for different defective fractions are tabulated, and a sensitivity analysis of the model is presented for the CCC control chart user to determine the optimal economic design parameters and minimum hourly costs for one production run according to different defective fractions, cost, time, and process parameters. Copyright © 2012 John Wiley & Sons, Ltd.     

Product Robust Design and Process Robust Design: Are They the Same? (No.)

Taguchi's ideas of robust parameter design motivated the development of the dual-response approach, where both the mean and the variance of the quality response are modeled in terms of the design parameters and noise factors. These are then used to identify optimal settings that achieve the dual objective of optimizing the signal (the mean) and minimizing variation. While much research has been published recently with regard to how to solve the dual-response problem (DRP), relatively little attention has been given to the unique characteristics of process robust design, like the existence of systematic variation or intercorrelations among the process “controllable” variables. These properties indeed put process robust design in a category of its own (separate from product robust design). In this paper, we first expound these unique properties and develop a general formulation of the DRP as it applies to process robust design. We then report on an implementation to an industrial process in a high-tech corporation.     

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives

Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field.