Pub Date : 2018-09-13DOI: 10.1504/IJQET.2018.10015967
R. Shokrizadeh, Mohammad Dolatabadi, Yaqub Yaqubinejad
T2 control chart with variable dimension is useful when there is a set of p1 variables that are easy to monitor or whose measurement is cheap, against a set of p2 variables, p = p1 + p2, that are difficult and/or expensive to monitor. However, the information that these p2 variables provide is important to detect quickly the process quality shift. Therefore, there are cases when controlling the whole set of p variables may be difficult or expensive, but controlling sometimes p1 variables, and only when the process seems to have a problem controlling the full set of p variables, may be a cheaper option on average and very efficient. However, its not effective if the shift size is small. For obtaining good performance in detecting such shifts, we propose the application of the variable sampling interval technique to the variable dimensional T2 (VDT2) control chart, resulting in the VSIVDT2 control chart.
{"title":"Hotelling's T2 control chart with adaptive sampling interval and variable dimensional","authors":"R. Shokrizadeh, Mohammad Dolatabadi, Yaqub Yaqubinejad","doi":"10.1504/IJQET.2018.10015967","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10015967","url":null,"abstract":"T2 control chart with variable dimension is useful when there is a set of p1 variables that are easy to monitor or whose measurement is cheap, against a set of p2 variables, p = p1 + p2, that are difficult and/or expensive to monitor. However, the information that these p2 variables provide is important to detect quickly the process quality shift. Therefore, there are cases when controlling the whole set of p variables may be difficult or expensive, but controlling sometimes p1 variables, and only when the process seems to have a problem controlling the full set of p variables, may be a cheaper option on average and very efficient. However, its not effective if the shift size is small. For obtaining good performance in detecting such shifts, we propose the application of the variable sampling interval technique to the variable dimensional T2 (VDT2) control chart, resulting in the VSIVDT2 control chart.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49033155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-13DOI: 10.1504/ijqet.2018.10015969
M. Barki, F. Asma
In modern metrological applications, 3D laser sensors are used to measure surface parameters. However, the performance of 3D laser sensor is influenced by its position and orientation angles on the target, which causes uncertainties in the measured parameters. In this paper, the influence of the position and orientation of a 3D laser plane sensor is studied by determining the measurement uncertainties on the parameters of a flat surface. Relative position and two orientation angles between the sensor and the surface are taken as the main characteristics of the sensor and are considered separately. In order to determine the uncertainties in the measurements, a set of measurements are performed on a reference part by using a coordinate measuring machine (CMM) equipped with a laser plane sensor.
{"title":"Uncertainties estimation in identification of digital planar surface parameters using a 3D laser sensor","authors":"M. Barki, F. Asma","doi":"10.1504/ijqet.2018.10015969","DOIUrl":"https://doi.org/10.1504/ijqet.2018.10015969","url":null,"abstract":"In modern metrological applications, 3D laser sensors are used to measure surface parameters. However, the performance of 3D laser sensor is influenced by its position and orientation angles on the target, which causes uncertainties in the measured parameters. In this paper, the influence of the position and orientation of a 3D laser plane sensor is studied by determining the measurement uncertainties on the parameters of a flat surface. Relative position and two orientation angles between the sensor and the surface are taken as the main characteristics of the sensor and are considered separately. In order to determine the uncertainties in the measurements, a set of measurements are performed on a reference part by using a coordinate measuring machine (CMM) equipped with a laser plane sensor.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48834162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1504/IJQET.2018.10018453
A. Badiru
The resurgence and advancement of artificial intelligence (AI) has spurred new research interests in applying or re-applying proven techniques from the 1990s to challenges in business and industry. The area of quality engineering and technology is particularly amenable to the application of AI techniques. Quality engineering programs are predicted on recurring quality projects that are made up of specific activities. The proper management of such activities is a precursor for the success of any quality program. This research paper explores the application of the proven technique of artificial neural network to activity networking in the management of quality programs.
{"title":"Quality insights: artificial neural network and taxonomical analysis of activity networks in quality engineering","authors":"A. Badiru","doi":"10.1504/IJQET.2018.10018453","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10018453","url":null,"abstract":"The resurgence and advancement of artificial intelligence (AI) has spurred new research interests in applying or re-applying proven techniques from the 1990s to challenges in business and industry. The area of quality engineering and technology is particularly amenable to the application of AI techniques. Quality engineering programs are predicted on recurring quality projects that are made up of specific activities. The proper management of such activities is a precursor for the success of any quality program. This research paper explores the application of the proven technique of artificial neural network to activity networking in the management of quality programs.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1504/IJQET.2018.10018467
M. Shojaie-Navokh, M. Fallahnezhad, Y. Zaremehrjerdi
Recent researches have shown that control charts with adaptive sampling schemes are faster than the static control charts in detecting process shifts. In this article, based on the Markov chain concepts, adaptive sampling schemes of U control chart are designed for monitoring the average number of non-conformities per unit in the process. Also, the economic statistical design of U control chart with two cost functions has been developed. These economic models considered features such as the cost of false alarms, the cost of detecting assignable causes and repairing, the cost of producing defective items when the process is in-control and out-of-control states and the cost of sampling and inspection. At the end, numerical examples for comparison and demonstrating the applicability of purposed schemes are solved. The results show the significant improvement of the adaptive sampling schemes as compared to the classic scheme based upon the statistical and cost criteria.
{"title":"Evaluating statistically constrained economic design for monitoring the average number of non-conformities with adaptive sampling schemes","authors":"M. Shojaie-Navokh, M. Fallahnezhad, Y. Zaremehrjerdi","doi":"10.1504/IJQET.2018.10018467","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10018467","url":null,"abstract":"Recent researches have shown that control charts with adaptive sampling schemes are faster than the static control charts in detecting process shifts. In this article, based on the Markov chain concepts, adaptive sampling schemes of U control chart are designed for monitoring the average number of non-conformities per unit in the process. Also, the economic statistical design of U control chart with two cost functions has been developed. These economic models considered features such as the cost of false alarms, the cost of detecting assignable causes and repairing, the cost of producing defective items when the process is in-control and out-of-control states and the cost of sampling and inspection. At the end, numerical examples for comparison and demonstrating the applicability of purposed schemes are solved. The results show the significant improvement of the adaptive sampling schemes as compared to the classic scheme based upon the statistical and cost criteria.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1504/IJQET.2018.10018460
Eishiro Higo, M. Pandey
A complex engineering system like a nuclear power reactor consists of a large variety and number of engineering components. As a part of a component aging management program, the diagnostic inspections of various component populations are performed to detect the onset of any unanticipated degradation. A prudent selection of the inspection sample size is necessary to optimise inspection cost. Sample size selection is typically based on the traditional statistical hypothesis test, which tends to result in a fairly large sample size. This paper presents an alternate approach to the sample size determination (SSD) problem based on the concept of the value of information (VoI). The paper provides a comparative analysis of the VoI and hypothesis-testing approaches through illustrative examples. The VoI approach is shown to provide a more meaningful way to minimise the cost of inspection as a function of component-replacement cost and losses arising from a failure. The characteristics and advantages of the VoI approach are analysed.
{"title":"Determination of sample size to support diagnostic inspection of components","authors":"Eishiro Higo, M. Pandey","doi":"10.1504/IJQET.2018.10018460","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10018460","url":null,"abstract":"A complex engineering system like a nuclear power reactor consists of a large variety and number of engineering components. As a part of a component aging management program, the diagnostic inspections of various component populations are performed to detect the onset of any unanticipated degradation. A prudent selection of the inspection sample size is necessary to optimise inspection cost. Sample size selection is typically based on the traditional statistical hypothesis test, which tends to result in a fairly large sample size. This paper presents an alternate approach to the sample size determination (SSD) problem based on the concept of the value of information (VoI). The paper provides a comparative analysis of the VoI and hypothesis-testing approaches through illustrative examples. The VoI approach is shown to provide a more meaningful way to minimise the cost of inspection as a function of component-replacement cost and losses arising from a failure. The characteristics and advantages of the VoI approach are analysed.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1504/IJQET.2018.10018468
Jishnu Gohel, D. Sarkar, H. Raghavendra
This research work is an attempt to improve the quality of concrete by implementing statistical process control tools like CUSUM with V-mask and exponentially weighted moving average (EWMA) control charts for ready-mixed concrete (RMC) plants in the Gandhinagar and Ahmedabad regions of India. These tools are used for sensitising the qualitative analysis of concrete in terms of the 28 days cube compressive strength. It has been observed that the RMC plant authorities were using an over-conservative mix design for the grades of concrete produced. This practice resulted in the usage of more cement than that stipulated in the mix design. The usage of higher cement content is wastage of resource. Also, this practice results in adverse side effects like production of more heat of hydration which creates shrinkage cracks. The application of these tools for strength monitoring would result in cost savings of about INR 4.8 million to 7.2 million per annum.
{"title":"Statistical process control and strength monitoring of ready-mixed concrete through the application of CUSUM and EWMA control charts: a case study of Ahmedabad and Gandhinagar RMC plants","authors":"Jishnu Gohel, D. Sarkar, H. Raghavendra","doi":"10.1504/IJQET.2018.10018468","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10018468","url":null,"abstract":"This research work is an attempt to improve the quality of concrete by implementing statistical process control tools like CUSUM with V-mask and exponentially weighted moving average (EWMA) control charts for ready-mixed concrete (RMC) plants in the Gandhinagar and Ahmedabad regions of India. These tools are used for sensitising the qualitative analysis of concrete in terms of the 28 days cube compressive strength. It has been observed that the RMC plant authorities were using an over-conservative mix design for the grades of concrete produced. This practice resulted in the usage of more cement than that stipulated in the mix design. The usage of higher cement content is wastage of resource. Also, this practice results in adverse side effects like production of more heat of hydration which creates shrinkage cracks. The application of these tools for strength monitoring would result in cost savings of about INR 4.8 million to 7.2 million per annum.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1504/IJQET.2018.10018463
mahsa laari, Reza Kamranrad, Farnoosh Bagheri
Piecewise regression is one of the linear regression types which control variables could break. In this paper, we propose a new mathematical programming-based approach to optimise the piecewise regression model by simultaneous considering variables. To this aim, the break point and the regression model parameters so determined that the absolute error of prediction is minimised. Note that existing methods are designed only based on independent variables and simultaneous effects of variable are not considered in piecewise regression. Accordingly, in this paper, a new approach is developed to optimise the piecewise regression model by simultaneous considering variables. Results show that proposed method has better performance than the existing methods for fitting data with correlated control variables.
{"title":"A mathematical programming-based approach in piecewise regression optimisation by simultaneous considering variables","authors":"mahsa laari, Reza Kamranrad, Farnoosh Bagheri","doi":"10.1504/IJQET.2018.10018463","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10018463","url":null,"abstract":"Piecewise regression is one of the linear regression types which control variables could break. In this paper, we propose a new mathematical programming-based approach to optimise the piecewise regression model by simultaneous considering variables. To this aim, the break point and the regression model parameters so determined that the absolute error of prediction is minimised. Note that existing methods are designed only based on independent variables and simultaneous effects of variable are not considered in piecewise regression. Accordingly, in this paper, a new approach is developed to optimise the piecewise regression model by simultaneous considering variables. Results show that proposed method has better performance than the existing methods for fitting data with correlated control variables.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1504/IJQET.2018.10018459
Hamed Niknafs, Morteza Faridkhah, C. Kazemi
The study of reliability is an important part of engineering design process which forms the basis of analysis and judgement on future performance of the product. Since the future could not be predicted with an absolute certainty, the nature of reliability would lead us to probability theory and uncertainty modelling. The quantitative calculation of this parameter for mechanical systems within different steps of production requires an analytical and systematic approach which has been focused in this paper. The proposed approach has been applied for calculating reliability of a clutch system as a case study. The system reliability in this work is determined based on the block diagram method as a function of individual component reliabilities which are calculated by statistical analysis of life test results. Using Weibull model, reliability of a typical clutch system has been formulated based on durability bench test and results have been interpreted to estimate field reliability.
{"title":"Analytical approach to product reliability estimation: a case study of automotive clutch system","authors":"Hamed Niknafs, Morteza Faridkhah, C. Kazemi","doi":"10.1504/IJQET.2018.10018459","DOIUrl":"https://doi.org/10.1504/IJQET.2018.10018459","url":null,"abstract":"The study of reliability is an important part of engineering design process which forms the basis of analysis and judgement on future performance of the product. Since the future could not be predicted with an absolute certainty, the nature of reliability would lead us to probability theory and uncertainty modelling. The quantitative calculation of this parameter for mechanical systems within different steps of production requires an analytical and systematic approach which has been focused in this paper. The proposed approach has been applied for calculating reliability of a clutch system as a case study. The system reliability in this work is determined based on the block diagram method as a function of individual component reliabilities which are calculated by statistical analysis of life test results. Using Weibull model, reliability of a typical clutch system has been formulated based on durability bench test and results have been interpreted to estimate field reliability.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-23DOI: 10.1504/IJQET.2017.10009772
D. Prajapati
Statistical process control is an industry-based methodology for measuring and controlling quality during the manufacturing process. The case study presented in this paper deals with the determination of process capability ratio (PCR), process capability index (Cpk) and level of correlation for a 'drive shaft run outs' of the manufacturing industry. Data have been taken from the manufacturing process and the -X and R control charts have been plotted to find whether the processes are with-in or out of statistical control. Process capability analysis has been done to find the effectiveness of the processes. It is found that the 36.02% of drive shaft run outs is beyond the control, which is really a higher rejection level and major concern for the industry. The samples of 'drive shaft run outs' are taken from a manufacturing industry located in India and estimated the level of correlation among the observations. The level of correlation among the observations of the 'drive shaft run outs' has been computed and matched with the suggested optimal schemes of Shewhart -X chart for sample size (n) of four. The presented case study shows the level of correlation of 0.50.
{"title":"Determination of correlation level and process capability of a manufacturing process using control charts","authors":"D. Prajapati","doi":"10.1504/IJQET.2017.10009772","DOIUrl":"https://doi.org/10.1504/IJQET.2017.10009772","url":null,"abstract":"Statistical process control is an industry-based methodology for measuring and controlling quality during the manufacturing process. The case study presented in this paper deals with the determination of process capability ratio (PCR), process capability index (Cpk) and level of correlation for a 'drive shaft run outs' of the manufacturing industry. Data have been taken from the manufacturing process and the -X and R control charts have been plotted to find whether the processes are with-in or out of statistical control. Process capability analysis has been done to find the effectiveness of the processes. It is found that the 36.02% of drive shaft run outs is beyond the control, which is really a higher rejection level and major concern for the industry. The samples of 'drive shaft run outs' are taken from a manufacturing industry located in India and estimated the level of correlation among the observations. The level of correlation among the observations of the 'drive shaft run outs' has been computed and matched with the suggested optimal schemes of Shewhart -X chart for sample size (n) of four. The presented case study shows the level of correlation of 0.50.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-23DOI: 10.1504/IJQET.2017.10009787
Joseph P. Bellucci, K. Bauer
Robust parameter design is used to identify a system's control settings that offer a compromise between obtaining desired mean responses and minimising the variability about those responses. Two popular combined-array strategies - the response surface model (RSM) approach and the emulator approach - are limited when applied to simulations. In the former case, the mean and variance models can be inadequate due to the high level of nonlinearity within many simulations. In the latter case, precise mean and variance approximations are developed at the expense of extensive Monte Carlo sampling. This paper extends the RSM approach to include nonlinear metamodels, namely kriging and radial basis function neural networks. The mean and variance of second-order Taylor series approximations of these metamodels are generated via the multivariate delta method and subsequent optimisation problems employing these approximations are solved. Results show that improved mean and variance prediction models, relative to the RSM approach, can be attained at a fraction of the emulator approach's cost.
{"title":"A Taylor series approach to the robust parameter design of computer simulations using kriging and radial basis function neural networks","authors":"Joseph P. Bellucci, K. Bauer","doi":"10.1504/IJQET.2017.10009787","DOIUrl":"https://doi.org/10.1504/IJQET.2017.10009787","url":null,"abstract":"Robust parameter design is used to identify a system's control settings that offer a compromise between obtaining desired mean responses and minimising the variability about those responses. Two popular combined-array strategies - the response surface model (RSM) approach and the emulator approach - are limited when applied to simulations. In the former case, the mean and variance models can be inadequate due to the high level of nonlinearity within many simulations. In the latter case, precise mean and variance approximations are developed at the expense of extensive Monte Carlo sampling. This paper extends the RSM approach to include nonlinear metamodels, namely kriging and radial basis function neural networks. The mean and variance of second-order Taylor series approximations of these metamodels are generated via the multivariate delta method and subsequent optimisation problems employing these approximations are solved. Results show that improved mean and variance prediction models, relative to the RSM approach, can be attained at a fraction of the emulator approach's cost.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43609842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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