Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in ...Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in various design environments. In order to obtain solutions that are not only "multi-objectively" optimal, but also reliable and robust, a probabilistic optimization method was presented by integrating six sigma philosophy and multi-objective genetic algorithm. With this method, multi-objective genetic algorithm was adopted to obtain the global Pareto solutions, and six sigma method was used to improve the reliability and robustness of those optimal solutions. Two engineering design problems were provided as examples to illustrate the proposed method.展开更多
Using Response Surface Methodology (RSM), an optimizing model of concurrent parameter and tolerance design is proposed where response mean equals its target in the target being best. The optimizing function of the mod...Using Response Surface Methodology (RSM), an optimizing model of concurrent parameter and tolerance design is proposed where response mean equals its target in the target being best. The optimizing function of the model is the sum of quality loss and tolerance cost subjecting to the variance confidence region of which six sigma capability can be assured. An example is illustrated in order to compare the differences between the developed model and the parameter design with minimum variance. The results show that the proposed method not only achieves robustness, but also greatly reduces cost. The objectives of high quality and low cost of product and process can be achieved simultaneously by the application of six sigma concurrent parameter and tolerance design.展开更多
Design for six sigma(DFSS) is a powerful approach of designing products,processes,and services with the objective of meeting the needs of customers in a cost-effective manner.DFSS activities are classified into four m...Design for six sigma(DFSS) is a powerful approach of designing products,processes,and services with the objective of meeting the needs of customers in a cost-effective manner.DFSS activities are classified into four major phases viz.identify,design,optimize,and validate(IDOV).And an adaptive design for six sigma(ADFSS) incorporating the traits of artificial intelligence and statistical techniques is presented.In the identify phase of the ADFSS,fuzzy relation measures between customer attributes(CAs) and engineering characteristics(ECs) as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers(FLCs).These two measures are then used to establish the cumulative impact factor for ECs.In the next phase(i.e.design phase),a transfer function is developed with the aid of robust multiple nonlinear regression analysis.Furthermore,this transfer function is optimized with the simulated annealing(SA) algorithm in the optimize phase.In the validate phase,t-test is conducted for the validation of the design resulted in earlier phase.Finally,a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.展开更多
The International Software Benchmarking Standards Group (ISBSG) provides to researchers and practitioners a repository of software projects’ data that has been used to date mostly for benchmarking and project estimat...The International Software Benchmarking Standards Group (ISBSG) provides to researchers and practitioners a repository of software projects’ data that has been used to date mostly for benchmarking and project estimation purposes, but rarely for software defects analysis. Sigma, in statistics, measures how far a process deviates from its goal. Six Sigma focuses on reducing variations within processes, because such variations may lead to an inconsistency in achieving projects’ specifications which represent “defects”, which mean not meeting customers’ satisfaction. Six Sigma provides two methodologies to solve organizations’ problems: “Define-Measure-Analyze-Improve-Control” process cycle (DMAIC) and Design of Six Sigma (DFSS). The DMAIC focuses on improving the existed processes, while the DFSS focuses on redesigning the existing processes and developing new processes. This paper presents an approach to provide an analysis of ISBSG repository based on Six Sigma measurements. It investigates the use of the ISBSG data repository with some of the related Six Sigma measurement aspects, including Sigma defect measurement and software defect estimation. This study presents the dataset preparation consisting of two levels of data preparations, and then analyzed the quality-related data fields in the ISBSG MS-Excel data extract (Release 12 - 2013). It also presents an analysis of the extracted dataset of software projects. This study has found that the ISBSG MS-Excel data extract has a high ratio of missing data within the data fields of “Total Number of Defects” variable, which represents a serious challenge when the ISBSG dataset is being used for software defect estimation.展开更多
Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure,...Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and controlmethodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar（Khyber Pakhtunkhwa, Pakistan）. The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.展开更多
Achieving Six-Sigma process capability starts with l istening to the Voice of the Customers, and it becomes a reality by combining th e People Power and the Process Power of the organisation. This paper presents a Six...Achieving Six-Sigma process capability starts with l istening to the Voice of the Customers, and it becomes a reality by combining th e People Power and the Process Power of the organisation. This paper presents a Six-Sigma implementation case study carried out in a magnet manufacturing compa ny, which produces bearing magnets to be used in energy meters. If the thickness of the produced bearing magnets is between 2.35 mm and 2.50 mm, they will be ac cepted by the customers. All the time the company could not produce the bearing magnets within the specified thickness range, as their process distribution was flat with 2.20 mm as lower control limit and 2.60 mm as upper control limit. This resulted in a huge loss in the form of non-conformities, loss of time and goodwill. The process capability of the company then was around 0.40. Organisat ion restructuring was carried out to reap the benefit of the People Power of the organisation. Statistically designed experiments (Taguchi Method based Design o f Experiments), Online quality control tools (Statistical Process Control To ols) were effectively used to complete the DMAIC (Define, Measure, Analyse, Impr ove and Control) cycle to reap the benefit of the Process Power of the organisat ion. Presently the company enjoys a process capability of 1.75, a way towards Si x-Sigma Process Capability.展开更多
基金The National Natural Science Foundation of China(No. 50475020)
文摘Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in various design environments. In order to obtain solutions that are not only "multi-objectively" optimal, but also reliable and robust, a probabilistic optimization method was presented by integrating six sigma philosophy and multi-objective genetic algorithm. With this method, multi-objective genetic algorithm was adopted to obtain the global Pareto solutions, and six sigma method was used to improve the reliability and robustness of those optimal solutions. Two engineering design problems were provided as examples to illustrate the proposed method.
基金the National Natural Science Foundation of China (No:70572044)New Central Elitist(No:04-0240)
文摘Using Response Surface Methodology (RSM), an optimizing model of concurrent parameter and tolerance design is proposed where response mean equals its target in the target being best. The optimizing function of the model is the sum of quality loss and tolerance cost subjecting to the variance confidence region of which six sigma capability can be assured. An example is illustrated in order to compare the differences between the developed model and the parameter design with minimum variance. The results show that the proposed method not only achieves robustness, but also greatly reduces cost. The objectives of high quality and low cost of product and process can be achieved simultaneously by the application of six sigma concurrent parameter and tolerance design.
基金Shanghai Leading Academic Discipline Project,China(No.B602)
文摘Design for six sigma(DFSS) is a powerful approach of designing products,processes,and services with the objective of meeting the needs of customers in a cost-effective manner.DFSS activities are classified into four major phases viz.identify,design,optimize,and validate(IDOV).And an adaptive design for six sigma(ADFSS) incorporating the traits of artificial intelligence and statistical techniques is presented.In the identify phase of the ADFSS,fuzzy relation measures between customer attributes(CAs) and engineering characteristics(ECs) as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers(FLCs).These two measures are then used to establish the cumulative impact factor for ECs.In the next phase(i.e.design phase),a transfer function is developed with the aid of robust multiple nonlinear regression analysis.Furthermore,this transfer function is optimized with the simulated annealing(SA) algorithm in the optimize phase.In the validate phase,t-test is conducted for the validation of the design resulted in earlier phase.Finally,a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.
文摘The International Software Benchmarking Standards Group (ISBSG) provides to researchers and practitioners a repository of software projects’ data that has been used to date mostly for benchmarking and project estimation purposes, but rarely for software defects analysis. Sigma, in statistics, measures how far a process deviates from its goal. Six Sigma focuses on reducing variations within processes, because such variations may lead to an inconsistency in achieving projects’ specifications which represent “defects”, which mean not meeting customers’ satisfaction. Six Sigma provides two methodologies to solve organizations’ problems: “Define-Measure-Analyze-Improve-Control” process cycle (DMAIC) and Design of Six Sigma (DFSS). The DMAIC focuses on improving the existed processes, while the DFSS focuses on redesigning the existing processes and developing new processes. This paper presents an approach to provide an analysis of ISBSG repository based on Six Sigma measurements. It investigates the use of the ISBSG data repository with some of the related Six Sigma measurement aspects, including Sigma defect measurement and software defect estimation. This study presents the dataset preparation consisting of two levels of data preparations, and then analyzed the quality-related data fields in the ISBSG MS-Excel data extract (Release 12 - 2013). It also presents an analysis of the extracted dataset of software projects. This study has found that the ISBSG MS-Excel data extract has a high ratio of missing data within the data fields of “Total Number of Defects” variable, which represents a serious challenge when the ISBSG dataset is being used for software defect estimation.
文摘Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and controlmethodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar（Khyber Pakhtunkhwa, Pakistan）. The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.
文摘Achieving Six-Sigma process capability starts with l istening to the Voice of the Customers, and it becomes a reality by combining th e People Power and the Process Power of the organisation. This paper presents a Six-Sigma implementation case study carried out in a magnet manufacturing compa ny, which produces bearing magnets to be used in energy meters. If the thickness of the produced bearing magnets is between 2.35 mm and 2.50 mm, they will be ac cepted by the customers. All the time the company could not produce the bearing magnets within the specified thickness range, as their process distribution was flat with 2.20 mm as lower control limit and 2.60 mm as upper control limit. This resulted in a huge loss in the form of non-conformities, loss of time and goodwill. The process capability of the company then was around 0.40. Organisat ion restructuring was carried out to reap the benefit of the People Power of the organisation. Statistically designed experiments (Taguchi Method based Design o f Experiments), Online quality control tools (Statistical Process Control To ols) were effectively used to complete the DMAIC (Define, Measure, Analyse, Impr ove and Control) cycle to reap the benefit of the Process Power of the organisat ion. Presently the company enjoys a process capability of 1.75, a way towards Si x-Sigma Process Capability.
