Fuzzy TOPSIS for Multiresponse Quality Problems in Wafer Fabrication Processes

The quality characteristics in the wafer fabrication process are diverse, variable, and fuzzy in nature. How to effectively deal with multiresponse quality problems in the wafer fabrication process is a challenging task. In this study, the fuzzy technique for order preference by similarity to an ideal solution (TOPSIS), one of the fuzzy multiattribute decision-analysis (MADA) methods, is proposed to investigate the fuzzy multiresponse quality problem in integrated-circuit (IC) wafer fabrication process. The fuzzy TOPSIS is one of the effective fuzzy MADA methods for dealing with decision-making problems under uncertain environments. First, a fuzzy TOPSIS methodology is developed by considering the ambiguity between quality characteristics. Then, a detailed procedure for the developed fuzzy TOPSIS approach is presented to show how the fuzzy wafer fabrication quality problems can be solved. Real-world data is collected from an IC semiconductor company and the developed fuzzy TOPSIS approach is applied to find an optimal combination of parameters. Results of this study show that the developed approach provides a satisfactory solution to the wafer fabrication multiresponse problem. This developed approach can be also applied to other industries for investigating multiple quality characteristics problems

Decision-maker Trade-offs In Multiple Response Surface Optimization

The focus of this dissertation is on improving decision-maker trade-offs and the development of a new constrained methodology for multiple response surface optimization. There are three key components of the research: development of the necessary conditions and assumptions associated with constrained multiple response surface optimization methodologies; development of a new constrained multiple response surface methodology; and demonstration of the new method. The necessary conditions for and assumptions associated with constrained multiple response surface optimization methods were identified and found to be less restrictive than requirements previously described in the literature. The conditions and assumptions required for a constrained method to find the most preferred non-dominated solution are to generate non-dominated solutions and to generate solutions consistent with decision-maker preferences among the response objectives. Additionally, if a Lagrangian constrained method is used, the preservation of convexity is required in order to be able to generate all non-dominated solutions. The conditions required for constrained methods are significantly fewer than those required for combined methods. Most of the existing constrained methodologies do not incorporate any provision for a decision-maker to explicitly determine the relative importance of the multiple objectives. Research into the larger area of multi-criteria decision-making identified the interactive surrogate worth trade-off algorithm as a potential methodology that would provide that capability in multiple response surface optimization problems. The ISWT algorithm uses an ε-constraint formulation to guarantee a non-dominated solution, and then interacts with the decision-maker after each iteration to determine the preference of the decision-maker in trading-off the value of the primary response for an increase in value of a secondary response. The current research modified the ISWT algorithm to develop a new constrained multiple response surface methodology that explicitly accounts for decision-maker preferences. The new Modified ISWT (MISWT) method maintains the essence of the original method while taking advantage of the specific properties of multiple response surface problems to simplify the application of the method. The MISWT is an accessible computer-based implementation of the ISWT. Five test problems from the multiple response surface optimization literature were used to demonstrate the new methodology. It was shown that this methodology can handle a variety of types and numbers of responses and independent variables. Furthermore, it was demonstrated that the methodology can be successful using a priori information from the decision-maker about bounds or targets or can use the extreme values obtained from the region of operability. In all cases, the methodology explicitly considered decision-maker preferences and provided non-dominated solutions. The contribution of this method is the removal of implicit assumptions and includes the decision-maker in explicit trade-offs among multiple objectives or responses

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies

Non-conventional machining of Al/Sic metal matrix composite

In recent years, aluminum alloy based metal matrix composites (MMC) are gaining importance in several aerospace and automobile applications. Aluminum has been used as matrix material owing to its excellent mechanical properties coupled with good formability. Addition of SiCp as reinforcement in aluminium system improves mechanical properties of the composite. In the present investigation, Al-SiCp composite was prepared by powder metallurgy route. Powder metallurgy homogeneously distributes the reinforcement in the matrix with no interfacial chemical reaction and high localized residual porosity. SiC particles containing different weight fractions (10 and 15 wt. %) and mesh size (300 and 400) is used as reinforcement .Though AlSiC possess superior mechanical properties, the high abrasiveness of the SiC particles hinders its machining process and thus by limiting its effective use in wide areas. Rapid tool wear with poor performance even with advanced expensive tools categories it as a difficult-to-cut material. Non-conventional processes such as electrical discharge machining (EDM) could be one of the best suited method to machine such composites. Four machining parameters such as discharge current (Ip), pulse duration (Ton), duty cycle (),flushing pressure (Fp) and two material properties weight fraction of SiCp and mesh size, and four responses like material removal rate (MRR), tool wear rate (TWR), circularity and surface roughness (Ra) are considered in this study. Taguchi method is adopted to design the experimental plan for finding out the optimal setting. However, Taguchi method is well suited for single response optimization problem. In order to simultaneously optimize multiple responses, a hybrid approach combining principal component analysis (PCA) and fuzzy inference system is coupled with Taguchi method for the optimization of multiple responses. The influence of each parameter on the responses is established using analysis of variances (ANOVA) at 5% level of significance. It is found that discharge current, pulse duration, duty cycle and wt% of SiC contribute significantly, where flushing pressure and mesh size of SiCp contribute least to the multiple performance characteristic index

An intelligent approach for multi-response optimization: a case study of non-traditional machining process

The present work proposes an intelligent approach to solve multi-response optimization problem in electrical discharge machining of AISI D2 using response surface methodology (RSM) combined with optimization techniques. Four process parameters (factors) such as discharge current (Ip), pulse-on-time (Ton), duty factor (τ) and flushing pressure (Fp) and four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component are considered in this study. A Box-Behnken RSM design is used to collect experimental data and develop empirical models relating input parameters and responses. Genetic algorithm (GA), an efficient search technique, is used to obtain the optimal setting for desired responses. It is to be noted that there is no single optimal setting which will produce best performance satisfying all the responses. In industries, to solve such problems, managers frequently depend on their past experience and judgement. Human intervention causes uncertainties present in the decision making process gleaned into solution methodology resulting in inferior solutions. Fuzzy inference system has been a viable option to address multiple response problems considering uncertainties and impreciseness caused during judgement process and experimental data collection. However, choosing right kind of membership functions and development of fuzzy rule base happen to be cumbersome job for the managers. To address this issue, a methodology based on combined neuro-fuzzy system and particle swarm optimization (PSO) is adopted to optimize multiple responses simultaneously. To avoid the conflicting nature of responses, they are first converted to signal-to-noise (S/N) ratio and then normalized. The proposed neuro-fuzzy approach is used to convert the responses into a single equivalent response known as Multi-response Performance Characteristic Index (MPCI). The effect of parameters on MPCI values has been studied in detail and a process model has been developed. Finally, optimal parameter setting is obtained by particle swarm optimization technique. The optimal setting so generated that satisfy all the responses may not be the best one due to aggregation of responses into a single response during neuro-fuzzy stage. In this direction, a multi-objective optimization based on non-dominated sorting genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters. The proposed optimal settings are validated using thermal-modeling of finite element analysis

Redução do espaço de busca em problemas de Otimização via Simulação utilizando Análise Envoltória de Dados e Arranjos Ortogonais de Taguchi.

O desenvolvimento de diversas metaheurísticas possibilitaram o uso da otimização em ambientes de simulação a eventos discretos. No entanto, este campo de pesquisa ainda é pouco utilizado, principalmente, em função do tempo necessário para a convergência desses algoritmos. Nesse sentido, a otimização via simulação é influenciada pela complexidade do modelo de simulação, pelo número de variáveis de decisão e por seus limites de variação. Neste contexto, este trabalho propõe um método capaz de identificar os melhores limites de variação, para cada variável de decisão, em um problema de otimização via simulação, proporcionando uma redução do tempo computacional, ao mesmo tempo em que permite alcançar soluções de elevada qualidade (soluções ótimas ou estatisticamente iguais a ela). Para isso, o método proposto combina a simulação a eventos discretos, arranjos ortogonais de Taguchi e a análise de supereficiência desenvolvida no modelo DEA BCC. Neste método, o espaço de busca do problema de otimização via simulação é representado por meio de um arranjo ortogonal de Taguchi. Para gerar as saídas do modelo DEA BCC, executou-se a simulação do arranjo ortogonal (cenários) e posteriormente a análise de supereficiência. Com base nestes resultados, os cenários são ordenados, sendo adotados como novos limites do problema de otimização os valores das variáveis dos dois cenários de maior supereficiência. Para validar o método proposto, foram utilizados quinze objetos de estudo. Os casos representam problemas complexos de empresas de manufatura e da área hospitalar. Dessa forma, sua eficácia pode ser verificada, uma vez que permitiu reduções médias de 97% no espaço de busca, e de 42% no tempo computacional necessário para se obter uma solução. Além disso, para quatro dos casos estudados, foi realizada a comparação entre o resultado ótimo obtido com a simulação de toda região de solução, e o resultado da otimização realizada no espaço de busca reduzido. Pode-se concluir, com um nível de 95% de confiança, que as respostas obtidas foram estatisticamente iguais

Dynamic optimisation for energy efficiency of injection moulding process

Low carbon economy has emerged as an important task in China since the energy intensity and carbon intensity reduction targets were clearly prescribed in its recent Twelfth Five-Year Plan during 2011-2015. While the largest enterprises have undertaken initial initiative to reduce their energy consumption, small and medium-sized enterprises (SMEs) will need to share the responsibilities in meeting the nation’s targets. However, there is no established structure for helping SMEs to reduce their efficiency gap and hence the implementation of energy-saving measures in SMEs still remains patchy. Addressing this issue, this thesis seeks to understand the critical barriers faced by SMEs and aims to develop proprietary methodologies that can facilitate manufacturing SMEs to close their efficiency gap. Process parameters optimisation is perceived to be an effective “no-cost” strategy which can be conducted by SMEs to realise energy efficiency improvement. A unique design of experiment (DOE) introduced by Dorian Shainin offers a simplistic framework to study process optimisation, but its application is not widespread and being criticised over its working principles. In order to address the inherent limitations of the Shainin’s method, it was integrated with the multivariate statistical methods and the signal-response system in the empirical study. The nature of the research aim also requires a theoretical approach to evaluate the economic performance of the energy efficiency investment. Hence, a spreadsheet-based decision support system (file SERP.xlsm) was created via dynamic programming (DP) method. The main contributions of this thesis can be subdivided into empirical level and theoretical level. At the empirical level, a technically feasible yet economically viable approach called “multi-response dynamic Shainin DOE” was developed. An empirical study on the injection moulding process was presented to examine the validity of this novel integrated methodology. The emphasis has been on the integration of multivariate techniques and signal-response analysis. The former successfully identified the critical factors to energy consumption and moulded parts’ impact performance regardless of the great fluctuation in the impact response. The latter enables the end-user to achieve different performance output based on the particular intent. At the theoretical level, the “DP-based spreadsheet solution” provides a convenient platform to help the rationally-behaved decision makers evaluate the energy efficiency investments. A simple hypothetical case study on the injection moulding industry was illustrated how the decision-making process for equipment replacement can evolve over time. To sum up, both proprietary methodologies enhance the dynamicity in the optimisation process to support injection moulding industry in closing their efficiency gap. The study at the empirical level was limited by the absence of real industrial case study where it is important to justify the practicality of the proposed methodology. Regarding the theoretical level, the dataset for initial validation on the spreadsheet solution was not available. Finally, it is important to continue the future work on the research limitations in order to increase the cogency of the proprietary methodologies for common use in the industry

Dynamic optimisation for energy efficiency of injection moulding process

Low carbon economy has emerged as an important task in China since the energy intensity and carbon intensity reduction targets were clearly prescribed in its recent Twelfth Five-Year Plan during 2011-2015. While the largest enterprises have undertaken initial initiative to reduce their energy consumption, small and medium-sized enterprises (SMEs) will need to share the responsibilities in meeting the nation’s targets. However, there is no established structure for helping SMEs to reduce their efficiency gap and hence the implementation of energy-saving measures in SMEs still remains patchy. Addressing this issue, this thesis seeks to understand the critical barriers faced by SMEs and aims to develop proprietary methodologies that can facilitate manufacturing SMEs to close their efficiency gap. Process parameters optimisation is perceived to be an effective “no-cost” strategy which can be conducted by SMEs to realise energy efficiency improvement. A unique design of experiment (DOE) introduced by Dorian Shainin offers a simplistic framework to study process optimisation, but its application is not widespread and being criticised over its working principles. In order to address the inherent limitations of the Shainin’s method, it was integrated with the multivariate statistical methods and the signal-response system in the empirical study. The nature of the research aim also requires a theoretical approach to evaluate the economic performance of the energy efficiency investment. Hence, a spreadsheet-based decision support system (file SERP.xlsm) was created via dynamic programming (DP) method. The main contributions of this thesis can be subdivided into empirical level and theoretical level. At the empirical level, a technically feasible yet economically viable approach called “multi-response dynamic Shainin DOE” was developed. An empirical study on the injection moulding process was presented to examine the validity of this novel integrated methodology. The emphasis has been on the integration of multivariate techniques and signal-response analysis. The former successfully identified the critical factors to energy consumption and moulded parts’ impact performance regardless of the great fluctuation in the impact response. The latter enables the end-user to achieve different performance output based on the particular intent. At the theoretical level, the “DP-based spreadsheet solution” provides a convenient platform to help the rationally-behaved decision makers evaluate the energy efficiency investments. A simple hypothetical case study on the injection moulding industry was illustrated how the decision-making process for equipment replacement can evolve over time. To sum up, both proprietary methodologies enhance the dynamicity in the optimisation process to support injection moulding industry in closing their efficiency gap. The study at the empirical level was limited by the absence of real industrial case study where it is important to justify the practicality of the proposed methodology. Regarding the theoretical level, the dataset for initial validation on the spreadsheet solution was not available. Finally, it is important to continue the future work on the research limitations in order to increase the cogency of the proprietary methodologies for common use in the industry