Quality Engineering最新文献_第9页

Improved detection of subsurface defects through active thermography and ensembling techniques 通过主动热成像和集成技术改进了对亚表面缺陷的检测

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-02-14 DOI: 10.1080/08982112.2023.2177871

Darío G. Lema, Oscar D. Pedrayes, R. Usamentiaga, D. García

{"title":"Improved detection of subsurface defects through active thermography and ensembling techniques","authors":"Darío G. Lema, Oscar D. Pedrayes, R. Usamentiaga, D. García","doi":"10.1080/08982112.2023.2177871","DOIUrl":"https://doi.org/10.1080/08982112.2023.2177871","url":null,"abstract":"Abstract Quality control and defect detection are major challenges in industrial environments. The localization of these defects is of crucial importance, as they can affect the performance of manufactured products and even the safety of people. Defect detection methods based on visual sensors and image processing are nowadays the most common approaches. To detect subsurface defects it is common to combine active thermography and deep learning. In this paper, PCT (Principal Components Thermography) is employed to enhance the SNR (signal-to-noise ratio), leading to a significant improvement in the results. Recent developments in this field apply deep learning models for semantic segmentation and object detection. However, the quality of the predictions is not enough to ensure that all quality controls are met. In this paper, a combination of semantic segmentation and object detection is proposed to increase the reliability of predictions. To carry out this combination, a working methodology and two new ensembling strategies are proposed. After comparing the results of the proposed combination with the results of using only semantic segmentation methods in a real industrial scenario, where carbon fiber sheets are used, it is found that the proposal improves the segmentation metrics by a 5% to a 24%. Thus the reliability of the predictions is improved.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44706244","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

Data-driven maintenance priority recommendations for civil aircraft engine fleets using reliability-based bivariate cluster analysis 基于可靠性的双变量聚类分析用于民用航空发动机机队的数据驱动维修优先级建议

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-02-02 DOI: 10.1080/08982112.2022.2163179

Hang Zhou, A. Parlikad, A. Brintrup

引用次数: 1

Monitoring processes with multiple dependent production lines using time between events control charts 使用事件间隔时间控制图监控多个相关生产线的过程

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-02-01 DOI: 10.1080/08982112.2023.2169161

Hussam Ahmad, Adel Ahmadi Nadi, Mohammad Amini, B. Sadeghpour Gildeh

引用次数: 0

An approach to non-homogenous phase-type distributions through multiple cut-points 多切点非均匀相位型分布的一种方法

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-13 DOI: 10.1080/08982112.2023.2168202

J. E. Ruiz-Castro, Christian Acal, J. Roldán

{"title":"An approach to non-homogenous phase-type distributions through multiple cut-points","authors":"J. E. Ruiz-Castro, Christian Acal, J. Roldán","doi":"10.1080/08982112.2023.2168202","DOIUrl":"https://doi.org/10.1080/08982112.2023.2168202","url":null,"abstract":"Abstract A new class of distributions based on phase-type distributions is introduced in the current paper to model lifetime data in the field of reliability analysis. This one is the natural extension of the distribution proposed by Acal et al. (One cut-point phase-type distributions in reliability. An application to resistive random access memories. Mathematics 9(21):2734, 2021) for more than one cut-point. Multiple interesting measures such as density function, hazard rate or moments, among others, were worked out both for the continuous and discrete case. Besides, a new EM-algorithm is provided to estimate the parameters by maximum likelihood. The results have been implemented computationally in R and simulation studies reveal that this new distribution reduces the number of parameters to be estimated in the optimization process and, in addition, it improves the fitting accuracy in comparison with the classical phase-type distributions, especially in heavy tailed distributions. An application is presented in the context of resistive memories with a new set of electron devices for nonvolatile memory circuits. In particular, the voltage associated with the resistive switching processes that control the internal behavior of resistive memories has been modeled with this new distribution to shed light on the physical mechanisms behind the operation of these memories.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59458786","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

Simultaneous monitoring of the mean vector and covariance matrix of multivariate multiple linear profiles with a new adaptive Shewhart-type control chart 用一种新的自适应Shewhart型控制图同时监测多元线性剖面的均值向量和协方差矩阵

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-13 DOI: 10.1080/08982112.2022.2164725

H. Sabahno, A. Amiri

{"title":"Simultaneous monitoring of the mean vector and covariance matrix of multivariate multiple linear profiles with a new adaptive Shewhart-type control chart","authors":"H. Sabahno, A. Amiri","doi":"10.1080/08982112.2022.2164725","DOIUrl":"https://doi.org/10.1080/08982112.2022.2164725","url":null,"abstract":"Abstract There has been a growing interest in research regarding monitoring a process through a regression model (called a profile) rather than a simple quality characteristic. This paper proposes a new monitoring scheme to simultaneously monitor the multivariate multiple linear profiles’ parameters. This scheme is based on the Shewhart control chart concept and only has one single (max-type) control chart for monitoring regression coefficients and error’s variation, which uses a new statistic to improve the variability (error’s variance-covariance matrix) shift detection in multivariate profiles. To increase the sensitivity and capability of the proposed scheme, especially in detecting small to moderate shift sizes, we add a variable parameters (VP) adaptive scheme to the developed control chart as well, considering that no adaptive monitoring schemes have so far been developed for monitoring the multivariate multiple linear profiles and neither are there any VP adaptive features for all profile monitoring schemes. Next, we develop a Markov chain model to compute the time to signal and run length performance measures. After that, we perform extensive numerical analyses to first compare the proposed control chart with the best available control charts and then evaluate its performance under different shift scenarios as well as different dimensions. The results show that the new monitoring scheme performs well compared to the best available monitoring schemes, and more importantly, it is more applicable in real practice. Finally, an illustrative example is presented to show the implementation of the proposed scheme in practice.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41972869","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: 2

Leveraging what you know: Versatile space-filling designs 利用你所知道的:多用途的空间填充设计

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-03 DOI: 10.1080/08982112.2022.2161394

Lu Lu, C. Anderson‐Cook

引用次数: 1

Functional data analysis and nonlinear regression models: an information quality perspective 功能数据分析和非线性回归模型:信息质量视角

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-03 DOI: 10.1080/08982112.2022.2153696

R. Kenett, C. Gotwalt

引用次数: 1

A case study: Eliminating nuisance within-part variation in assessing a measurement system 一个案例研究:在评估一个测量系统时消除部分内的有害变化

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-01-02 DOI: 10.1080/08982112.2022.2088295

M. Hamada, B. W. O’Brien

引用次数: 0

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":null,"pages":null},"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