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Applied Sciences
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Quality Control and Product Monitoring in Manufacturing
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Quality Control and Product Monitoring in Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 9482

Special Issue Editors

Dr. Roham Sadeghi Tabar

E-Mail Website
Guest Editor
Department of Industrial and Materials Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
Interests: smart design and manufacturing; geometry assurance; optimziation
Prof. Dr. Kristina Wärmefjord

E-Mail Website
Guest Editor
Department of Industrial and Materials Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
Interests: engineering mathematics; mechanics statistics and probability; variation simula-tion; tolerances; assembly; inspection data

Special Issue Information

Dear Colleagues,

Quality control and product monitoring play integral roles in modern manufacturing, ensuring consistent product quality, customer satisfaction, and regulatory adherence. This proposal outlines our intent to contribute to a special journal issue focused on these crucial aspects of manufacturing.

Quality control encompasses systematic processes like statistical process control and Six Sigma methodologies. These methods establish rigorous quality standards and continuously monitor production processes to minimize defects and optimize resource use. Product monitoring extends beyond the factory floor, involving real-time data collection and analysis using technologies such as IoT sensors, machine learning, and data analytics. These tools enable manufacturers to detect anomalies swiftly, improving product quality reducing waste and costs. In this Special Issue, we aim to explore recent advancements, best practices, and case studies in quality control and product monitoring across various manufacturing sectors. We will highlight how emerging technologies, data-driven insights, and collaborative strategies are reshaping these vital aspects of manufacturing, fostering innovation, sustainability, and global competitiveness. By examining the intersection of technology, regulations, and operational excellence, we hope to provide a comprehensive understanding of the present and future landscape of quality control and product monitoring in manufacturing.

Dr. Roham Sadeghi Tabar
Prof. Dr. Kristina Wärmefjord
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. Applied Sciences 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 2400 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

  • product quality
  • quality control
  • aritificial intelligence
  • statistical quality control
  • geometric variation

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Published Papers (5 papers)

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Research

22 pages, 8543 KiB  
Article
Efficiency Assessment Method for Evoking Cultural Empathy in Symbolic Cultural and Creative Products Based on Fuzzy-FMEA
by Ning Wang, Weiwei Wang, Suihuai Yu, Jian Chen and Xiaoyan Yang
Appl. Sci. 2025, 15(1), 221; https://doi.org/10.3390/app15010221 - 30 Dec 2024
Viewed by 647
Abstract
To address the issue of user empathy throughout the emotional experience process, this study presents a method to evaluate the efficacy of cultural empathy evoked based on fuzzy-FMEA. The method focuses on symbolic culture and creative products, constructing an evaluation index system and [...] Read more.
To address the issue of user empathy throughout the emotional experience process, this study presents a method to evaluate the efficacy of cultural empathy evoked based on fuzzy-FMEA. The method focuses on symbolic culture and creative products, constructing an evaluation index system and decision-making framework in terms of cultural empathic evoking. It utilizes thematic analysis to discover and categorize the factors that influence cultural empathy, as well as an evaluation index system to improve the Failure Mode and Effects Analysis framework. It effectively solves the limitations of traditional FMEA, such as single weighting and uncertainty. According to the assessment report, cognitive association failure and scenario restoration failure are significant risk factors for cultural empathy-evoking failure. This study’s findings provide designers with realistic proposals for thematic symbolic imagery and serialized design forms, as well as scientific assessment tools and decision-making resources for cultural industries and policymakers. Full article
(This article belongs to the Special Issue Quality Control and Product Monitoring in Manufacturing)
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18 pages, 12645 KiB  
Article
Enhancing Welding Geometric Precision: Analyzing the Impact of Weld Path Directions, Sequences and Locating Schemes on Displacement
by Roham Sadeghi Tabar, Lars Lindkvist, Kristina Wärmefjord, Pasquale Franciosa, Dariusz Ceglarek and Rikard Söderberg
Appl. Sci. 2024, 14(23), 11144; https://doi.org/10.3390/app142311144 - 29 Nov 2024
Cited by 1 | Viewed by 896
Abstract
Welding-induced geometric deviations remain a critical challenge in industrial manufacturing, particularly in achieving high-precision assembly. This study investigates the effects of welding path directions, sequences, and locating schemes on the displacement of welded assemblies, focusing on minimizing geometric deviations. Using finite element method [...] Read more.
Welding-induced geometric deviations remain a critical challenge in industrial manufacturing, particularly in achieving high-precision assembly. This study investigates the effects of welding path directions, sequences, and locating schemes on the displacement of welded assemblies, focusing on minimizing geometric deviations. Using finite element method (FEM) simulations and a design of experiments (DOE) approach, the interactions between these parameters were systematically analyzed. Results show that the locating scheme plays a dominant role in controlling displacement, with optimal configurations significantly reducing geometric errors. Welding sequences were also found to have a considerable impact, further minimizing distortions when appropriately optimized. The effect of weld path direction, while less significant for simpler geometries, became more pronounced in assemblies with curvature. These findings pinpoint the necessity of integrating a combined factor approach, including fixturing, welding sequence, and path direction, to optimize and improve the geometric quality of welded assemblies. Full article
(This article belongs to the Special Issue Quality Control and Product Monitoring in Manufacturing)
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19 pages, 5038 KiB  
Article
Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence
by Edgar Augusto Ruelas-Santoyo, Vicente Figueroa-Fernández, Moisés Tapia-Esquivias, Yaquelin Verenice Pantoja-Pacheco, Edgar Bravo-Santibáñez and Javier Cruz-Salgado
Appl. Sci. 2024, 14(21), 9705; https://doi.org/10.3390/app14219705 - 24 Oct 2024
Viewed by 1081
Abstract
Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in [...] Read more.
Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems. Full article
(This article belongs to the Special Issue Quality Control and Product Monitoring in Manufacturing)
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15 pages, 1417 KiB  
Article
Using Bayesian Regularized Artificial Neural Networks to Predict the Tensile Strength of Additively Manufactured Polylactic Acid Parts
by Valentina Vendittoli, Wilma Polini, Michael S. J. Walter and Stefan Geißelsöder
Appl. Sci. 2024, 14(8), 3184; https://doi.org/10.3390/app14083184 - 10 Apr 2024
Cited by 2 | Viewed by 1858
Abstract
Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mechanical performance in Fused Filament Fabrication. Using a fractional Taguchi design, [...] Read more.
Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mechanical performance in Fused Filament Fabrication. Using a fractional Taguchi design, seven key process parameters are systematically varied to provide a robust dataset for model training. The resulting model confirms its accuracy in predicting tensile strength. In particular, the mean squared error is 0.002, and the mean absolute error is 0.024. These results significantly advance the understanding of 3D manufactured parts, shedding light on the intricate dynamics between process nuances and mechanical outcomes. Furthermore, they underscore the transformative role of machine learning in precision-driven quality prediction and optimization in additive manufacturing. Full article
(This article belongs to the Special Issue Quality Control and Product Monitoring in Manufacturing)
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27 pages, 14846 KiB  
Article
Enhancing Scrap Reduction in Electric Motor Manufacturing for the Automotive Industry: A Case Study Using the PDCA (Plan–Do–Check–Act) Approach
by Miguel-Ángel Rangel-Sánchez, José-De-Jesús Urbina-González, José-Luis Carrera-Escobedo, Omar-Alejandro Guirette-Barbosa, Virgilio-Alfonso Murillo-Rodríguez, José-María Celaya-Padilla, Héctor-Antonio Durán-Muñoz and Oscar Cruz-Domínguez
Appl. Sci. 2024, 14(7), 2999; https://doi.org/10.3390/app14072999 - 3 Apr 2024
Cited by 2 | Viewed by 4030
Abstract
The automotive industry is increasingly focused on waste management, elimination, and reduction to achieve sustainability and cost reduction. This focus drives the industry towards resource-efficient operations that minimize environmental impact while exceeding customer expectations. Meeting these demands necessitates the adoption of more efficient [...] Read more.
The automotive industry is increasingly focused on waste management, elimination, and reduction to achieve sustainability and cost reduction. This focus drives the industry towards resource-efficient operations that minimize environmental impact while exceeding customer expectations. Meeting these demands necessitates the adoption of more efficient production methodologies, such as the PDCA cycle. This work presents a case study that illustrates the application of the PDCA methodology to minimize scrap generation due to process variability in a multinational company that manufactures electric motors for the automotive industry. The aim was to demonstrate how the PDCA methodology can improve quality standards by minimizing scrap generated during the manufacture of electrical armatures. Notably, the organization in this case study set a waste target of 0.7%, which was significantly exceeded. Finally, the implementation of this methodology can deliver significant economic benefits, with a total annual cost reduction of approximately USD 135,000. Full article
(This article belongs to the Special Issue Quality Control and Product Monitoring in Manufacturing)
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