Quality Engineering最新文献_第7页

Testing capability index C pk with its application in automobile engine manufacturing industry 测试能力指标cpk及其在汽车发动机制造业中的应用

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-02 DOI: 10.1080/08982112.2022.2087042

H. Iranmanesh, A. Parchami, M. Jabbari Nooghabi

引用次数: 1

Correction 校正

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-02 DOI: 10.1080/08982112.2023.2176110

{"title":"Correction","authors":"","doi":"10.1080/08982112.2023.2176110","DOIUrl":"https://doi.org/10.1080/08982112.2023.2176110","url":null,"abstract":"An error was noted in R programming that caused a modest overestimation of variance component values associated with the oven and batch (block) random effects. This led to posterior predictive distributions for that were slightly too dispersed for the diameter and strength responses. This error came from inadvertently treating the posterior Stan scale parameter variables, SO_1, SO_2, SB_1, SBx2_1, SB_2, and SBx2_2 (shown in the Appendix), as variance (for normal) and variance-related (for student t) parameters, rather than as standard deviation (for normal) and standard-deviation-related (for student t) parameters. (Stan represents the scalevariation parameter for the normal distribution as standard deviation parameter, not a variance parameter. A similar result holds proportionately for the student t distribution in Stan.). The author has corrected this programing error and recomputed Figures 3, 4, 6, and 7 and Table 3. These are shown below. The other figures and tables were not affected. As a high-level validation, a separate, parallel calculation was done using Stan’s generated quantities feature to sample the posterior random effects directly from Stan. This produced results very similar to the original approach (but not the same due to Monte Carlo random variation).","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"i - iii"},"PeriodicalIF":2.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42001085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative study for parameter estimation of the Weibull distribution in a small sample size: An application to spring fatigue failure data 小样本威布尔分布参数估计的比较研究——应用于弹簧疲劳失效数据

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-21 DOI: 10.1080/08982112.2022.2158745

Xiaoyu Yang, Liyang Xie, Yifeng Yang, Bingfeng Zhao, Yuan Li

引用次数: 1

Design construction and model selection for small mixture-process variable experiments with high-dimensional model terms 具有高维模型项的小混合过程变量实验的设计、构造和模型选择

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-15 DOI: 10.1080/08982112.2022.2135444

K. Chatterjee, Chang-Yun Lin

引用次数: 0

Optimizing the quality control of multivariate processes under an improved Mahalanobis–Taguchi system 在改进的Mahalanobis–Taguchi系统下优化多变量过程的质量控制

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-13 DOI: 10.1080/08982112.2022.2146511

Yefang Sun, Ijaz Younis, Yueyi Zhang, Huiying Zhou

{"title":"Optimizing the quality control of multivariate processes under an improved Mahalanobis–Taguchi system","authors":"Yefang Sun, Ijaz Younis, Yueyi Zhang, Huiying Zhou","doi":"10.1080/08982112.2022.2146511","DOIUrl":"https://doi.org/10.1080/08982112.2022.2146511","url":null,"abstract":"Abstract Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"413 - 429"},"PeriodicalIF":2.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48762848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical process monitoring for vector autoregressive time series based on location-scale CUSUM method 基于位置尺度CUSUM方法的向量自回归时间序列统计过程监测

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-13 DOI: 10.1080/08982112.2022.2156295

Sangjo Lee, Sangyeol Lee

引用次数: 1

Case study on applying sequential analyses in operational testing 在运行测试中应用顺序分析的案例研究

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-12 DOI: 10.1080/08982112.2022.2146510

Monica Ahrens, Rebecca M. Medlin, Keyla Pagán-Rivera, John W. Dennis

引用次数: 0

An online approach for robust parameter design with incremental Gaussian process 一种基于增量高斯过程的鲁棒参数在线设计方法

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-08 DOI: 10.1080/08982112.2022.2147844

X. Zhou, Yunlong Gao, Ting Jiang, Zebiao Feng

{"title":"An online approach for robust parameter design with incremental Gaussian process","authors":"X. Zhou, Yunlong Gao, Ting Jiang, Zebiao Feng","doi":"10.1080/08982112.2022.2147844","DOIUrl":"https://doi.org/10.1080/08982112.2022.2147844","url":null,"abstract":"Abstract Robust parameter design (RPD), an important method for quality improvement, can effectively mitigate the negative impact of fluctuations on product quality. Traditional RPD adopts offline design, that is, the optimal level of parameter combination is fixed by one-time modeling throughout the production process. This strategy is obviously unreasonable. Online RPD breaks through the limitation of traditional offline design, which can update the optimal setting by utilizing the new sample when the current optimal setting of controllable factors is not suitable. However, there are still some problems in the current version of online RPD, such as poor data fitting ability of response surface model and low efficiency of parameter design. In this paper, a new online RPD method based on Gaussian process (GP) is proposed. The GP model is used to construct the response surface, which has the capacity of dealing with high-dimensional nonlinear data. But traditional GP method adopts batch learning, it cannot update the model online with new samples. So this paper proposes an incremental Gaussian process model (IGP), which can update the response surface in real-time. In the proposed IGP based online robust parameter design method (IGP-RPD), an effective optimization strategy is used to find the optimal setting of controllable factors, and a reasonable selection criterion is used to determine the noise factor setting for the next stage. The optimal setting of the controllable factor in the previous stage and the currently observed noise factor are used as input, and the corresponding quality characteristic is taken as the output. The input and output form a new sample to update the response surface model. In this way, the RPD process can be redone continuously until the desirable optimal setting of the controllable factor is found. Three cases are used to verify the IGP-RPD method and compare it with the existing methods. The experiments manifest that the IGP-RPD method has better performance in both accuracy and efficiency.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"430 - 443"},"PeriodicalIF":2.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45134333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Letter to the quality engineering editor 致质量工程编辑的信

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-08 DOI: 10.1080/08982112.2022.2154164

Stefan H. Steiner

{"title":"Letter to the quality engineering editor","authors":"Stefan H. Steiner","doi":"10.1080/08982112.2022.2154164","DOIUrl":"https://doi.org/10.1080/08982112.2022.2154164","url":null,"abstract":"Comment on the Gauge R&R Literature Review by Soares et al. (2022) I read with interest the above paper that was a systematic literature review whose stated aim was “to assess the state of the art in Gauge R&R [measurement assessment] studies.” Given that goal, I was disappointed that the review paper failed to even mention any of the following proposed improvements to the traditional design and analysis of gauge R&R measurement assessment studies. This includes, so called, augmented assessment plans, utilizing baseline data, and selecting parts for the measurement study using leveraging from the baseline (Browne 2009a,b; Browne et al. 2009a,b, 2010; Stevens et al. 2010, 2013, 2015). These ideas provide much more efficient measurement assessment studies with no increase in cost. As such, they should be considered as desirable improvements to the traditional Gauge R&R studies. To make a simple comparison, consider the traditional Gauge R&R study where 10 parts are selected at random from the production process and measured 6 times each (here we assume an automated measurement system, so we don’t consider operators). If we also assume we have a large baseline of once measured parts (commonly freely available since these data are often collected for some other reason), an alternate measurement assessment plan is to use leveraging and select 10 parts with either large or small values from the baseline and measure them each 6 times. Comparing these two plans (that require the same number of total measurements in the assessment study) we see that the leveraged plan (that takes into account the baseline values for the selected parts) is much more efficient than the traditional gauge R&R plan. The reduction in standard deviation of an estimator for the ratio of the measurement variation to the overall variation is between about 10–45%, with larger reductions when the actual measurement variation is larger.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"546 - 546"},"PeriodicalIF":2.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43090551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability-based optimization of imperfect preventive maintenance with Bayesian estimation 基于贝叶斯估计的不完全预防性维修可靠性优化

IF 2 4区工程技术

Quality Engineering Pub Date : 2022-12-05 DOI: 10.1080/08982112.2022.2150085

Selma Gürler, D. Göksülük, Deniz Türsel Eliiyi

引用次数: 1