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Advances in Data Analytics for Manufacturing Quality Assurance
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Advances in Data Analytics for Manufacturing Quality Assurance

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 9592

Special Issue Editors

Dr. Qing Li

E-Mail Website
Guest Editor
Department of Industrial & Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA
Interests: statistical quality assurance; statistics, data analytics, and machine learning in advanced manufacturing; non-destructive evaluation, healthcare, and other engineering and natural science applications
Special Issues, Collections and Topics in MDPI journals
Dr. Bing Si

E-Mail Website
Guest Editor
Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, USA
Interests: statistical machine learning; data fusion; precision medicine; population health
Dr. Renjie Hu

E-Mail Website
Guest Editor
Department of Information and Logistics Technology, University of Houston, Houston, TX, USA
Interests: machine learning; multi-modal data and missing data in healthcare; medical image analysis; anomaly detection in semiconductor industry; traffic and car crash predictions

Special Issue Information

Dear Colleagues,

Data analytics, statistics, and machine learning play crucial roles in advanced manufacturing. In this Special Issue, we are looking for high-quality research papers relevant to data analytics for manufacturing quality assurance, including process monitoring, anomaly/defect detection, variation quantification, system/process optimization, and reliability analysis. Articles that establish new methodologies in these topics or provide interesting and innovative applications are immensely welcome. Reviews will also be considered, mainly those that may provide commentaries that lead to open perspectives of new methodologies and applications.

Submissions must be rigorous, clear, well-written in professional English, and accessible and appealing to a broad audience. There is no restriction on the length of the papers, nor the use of color figures and diagrams. However, if thought to be adequate, electronic files with software or computer codes, full details of calculations, and full descriptions of experimental procedures, lengthy datasets, or detailed proof that may be judged to be too lengthy to be inserted in the body of the paper may be added in the Appendix and/or supplementary materials.

Dr. Qing Li
Dr. Bing Si
Dr. Renjie Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process monitoring/prognosis
  • anomaly/defect detection
  • uncertainty quantification
  • system/process optimization
  • reliability analysis
  • statistics
  • machine learning/deep learning
  • data fusion
  • measurements
  • quality evaluation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

20 pages, 4600 KiB  
Article
A Novel Methodology for Performance Evaluation in Advanced Quality Control
by Ethel García, Rita Peñabaena-Niebles, Winston S. Percybrooks and Kevin Palomino
Mathematics 2025, 13(2), 259; https://doi.org/10.3390/math13020259 - 14 Jan 2025
Viewed by 910
Abstract
Current global conditions and challenges in industrial manufacturing, marked by dynamism, competition, and the need for responsible resource management, have increased the demand for sustainable manufacturing practices. The integration of Industry 4.0 and the recent development of Industry 5.0 have added dynamism, which [...] Read more.
Current global conditions and challenges in industrial manufacturing, marked by dynamism, competition, and the need for responsible resource management, have increased the demand for sustainable manufacturing practices. The integration of Industry 4.0 and the recent development of Industry 5.0 have added dynamism, which has generated profound implications for quality control and process monitoring, focusing mainly on recognising control patterns within the manufacturing environment. This study introduces a novel methodology for evaluating the performance of pattern classification models used in advanced quality control. Our approach incorporates robust performance metrics, early detection, window size, network hyperparameters, and concurrent patterns within a simulated monitoring environment. Unlike previous research, our evaluation methodology addresses the sensitivity of classification models to various factors, emphasising the critical balance between early detection and minimising false alarms. The findings reveal that window size significantly impacts the model’s sensitivity to pattern changes, highlighting that measuring early detection alone is impractical in real-world applications. Furthermore, optimal hyperparameter selection enhances the model’s practical applicability. Full article
(This article belongs to the Special Issue Advances in Data Analytics for Manufacturing Quality Assurance)
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15 pages, 1681 KiB  
Article
T2-LSTM-Based AI System for Early Detection of Motor Failure in Chemical Plants
by Chien-Chih Wang
Mathematics 2024, 12(17), 2652; https://doi.org/10.3390/math12172652 - 26 Aug 2024
Viewed by 1638
Abstract
In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple current monitoring, often need to be faster and more accurate. [...] Read more.
In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple current monitoring, often need to be faster and more accurate. The rapid development of artificial intelligence provides powerful tools for early fault detection and maintenance. In this study, the Hotelling T2 index is used to calculate the root mean square values of the normal motor’s x, y, and z axes. A long short-term memory (LSTM) model creates a trend model for the Hotelling T2 index, determining an early warning threshold. Current anomaly detection follows the ISO 10816-1 standard, while future anomaly prediction uses the T2-LSTM trend model. Validated at a chemical plant in Southern Taiwan, the method shows 98% agreement between the predicted and actual anomalies over three months, demonstrating its effectiveness. The T2-LSTM model significantly improves the accuracy of motor fault detection, potentially reducing economic losses and improving safety in the chemical industry. Future research will focus on reducing false alarms and integrating more sensor data. Full article
(This article belongs to the Special Issue Advances in Data Analytics for Manufacturing Quality Assurance)
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17 pages, 1335 KiB  
Article
Predictive Maintenance with Linguistic Text Mining
by Alberto Postiglione and Mario Monteleone
Mathematics 2024, 12(7), 1089; https://doi.org/10.3390/math12071089 - 4 Apr 2024
Cited by 3 | Viewed by 2075
Abstract
The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data [...] Read more.
The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency. Full article
(This article belongs to the Special Issue Advances in Data Analytics for Manufacturing Quality Assurance)
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34 pages, 2838 KiB  
Article
Measurement Studies Utilizing Similarity Evaluation between 3D Surface Topography Measurements
by Lijie Liu, Beiwen Li, Hantang Qin and Qing Li
Mathematics 2024, 12(5), 669; https://doi.org/10.3390/math12050669 - 24 Feb 2024
Viewed by 1386
Abstract
In the realm of quality assurance, the significance of statistical measurement studies cannot be overstated, particularly when it comes to quantifying the diverse sources of variation in measurement processes. However, the complexity intensifies when addressing 3D topography data. This research introduces an intuitive [...] Read more.
In the realm of quality assurance, the significance of statistical measurement studies cannot be overstated, particularly when it comes to quantifying the diverse sources of variation in measurement processes. However, the complexity intensifies when addressing 3D topography data. This research introduces an intuitive similarity-based framework tailored for conducting measurement studies on 3D topography data, aiming to precisely quantify distinct sources of variation through the astute application of similarity evaluation techniques. In the proposed framework, we investigate the mean and variance of the similarity between 3D surface topography measurements to reveal the uniformity of the surface topography measurements and statistical reproducibility of the similarity evaluation procedure, respectively. The efficacy of our framework is vividly demonstrated through its application to measurements derived from additive-fabricated specimens. We considered four metal specimens with 20 segmented windows in total. The topography measurements were obtained by three operators using two scanning systems. We find that the repeatability variation of the topography measurements and the reproducibility variation in the measurements induced by operators are relatively smaller compared with the variation in the measurements induced by optical scanners. We also notice that the variation in the surface geometry of different surfaces is much larger in magnitude compared with the repeatability variation in the topography measurements. Our findings are consistent with the physical intuition and previous research. The ensuing experimental studies yield compelling evidence, affirming that our devised methods are adept at providing profound insights into the multifaceted sources of variation inherent in processes utilizing 3D surface topography data. This innovative framework not only showcases its applicability but also underlines its potential to significantly contribute to the field of quality assurance. By offering a systematic approach to measuring and comprehending variation in 3D topography data, it stands poised to become an indispensable tool in diverse quality assurance contexts. Full article
(This article belongs to the Special Issue Advances in Data Analytics for Manufacturing Quality Assurance)
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16 pages, 1402 KiB  
Article
Dynamic Acceptance Sampling Strategy Based on Product Quality Performance Using Examples from IC Test Factory
by Chien-Chih Wang and Yu-Shan Chang
Mathematics 2023, 11(13), 2872; https://doi.org/10.3390/math11132872 - 27 Jun 2023
Cited by 1 | Viewed by 2824
Abstract
Acceptance sampling plans are divided into attributes and variables, which are used to evaluate the mechanism for determining lot quality. Traditional attribute sampling plans usually choose the Acceptable Quality Level (AQL) for each stage based on experience but need practical guidelines to follow. [...] Read more.
Acceptance sampling plans are divided into attributes and variables, which are used to evaluate the mechanism for determining lot quality. Traditional attribute sampling plans usually choose the Acceptable Quality Level (AQL) for each stage based on experience but need practical guidelines to follow. Previous research endeavors have predominantly centered around statistical perspectives and emphasized the reduction of sample size or sampling frequency while allocating lesser consideration to cost factors and practical applications when formulating sampling decisions. This study proposes a dynamic sampling strategy to minimize costs and estimate AQL values and sample sizes for each stage based on product quality performance to establish a more effective and flexible sampling strategy. The study verifies the scenario in an integrated circuit (IC) testing factory, considering multiple combinations of between-batch quality conditions, within-batch quality conditions, sampling method, and cost ratio, and conducts sampling inspection simulations. When quality changes, the dynamic strategy is activated to adjust AQL. Finally, based on the sampling errors and costs in the inspection results, a comparison is made with the traditional MIL-STD-105E sampling plan, confirming that the dynamic AQL sampling plan has significantly improved performance. Full article
(This article belongs to the Special Issue Advances in Data Analytics for Manufacturing Quality Assurance)
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