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Applied Sciences
Special Issues
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: 31 October 2024 | Viewed by 1823

Special Issue Editors

Dr. Roham Sadeghi Tabar

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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

Published Papers (2 papers)

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Research

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
Viewed by 536
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 - 03 Apr 2024
Viewed by 965
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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