Industry 4.0: Manufacturing and Materials Processing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 30 April 2024 | Viewed by 20398

Special Issue Editor


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Guest Editor
Centre for Ultrasonic Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: welding technology; NDT; residual stress; additive manufacturing; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Industry 4.0 (Fourth Industrial Revolution) is a further development stage in the organisation and management of the entire manufacturing industry, from ‎‎embedded systems to cyber-physical systems. In this approach, machines no longer process the products; instead, the ‎products communicate with the machine and tell it ‎what to do.‎

In this Special Issue, we are keen to publish robust papers discussing any relevant Industry 4.0 approach. This can include but is not limited to, developments in "manufacturing", "material processing", "robotics", "inspection", "quality control", "welding", "additive manufacturing", "nondestructive testing/evaluation", "hybrid manufacturing and inspection", "structural integrity", "advanced materials", "material evaluation", etc. The authors are recommended to include discussions directly relevant to Industry 4.0, however, the papers addressing other topics, which can be potentially corresponded to Industry 4.0 (like robotics), will also be considered.

Dr. Yashar Javadi
Guest Editor

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. Journal of Manufacturing and Materials Processing 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 1800 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.

Published Papers (6 papers)

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Research

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16 pages, 7266 KiB  
Article
Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining
by Mohammadjafar Hadad, Samareh Attarsharghi, Mohsen Dehghanpour Abyaneh, Parviz Narimani, Javad Makarian, Alireza Saberi and Amir Alinaghizadeh
J. Manuf. Mater. Process. 2024, 8(1), 41; https://doi.org/10.3390/jmmp8010041 - 14 Feb 2024
Cited by 1 | Viewed by 1154
Abstract
Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior [...] Read more.
Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining. Full article
(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)
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20 pages, 7381 KiB  
Article
A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping
by Daniele Farioli, Ertuğrul Kaya, Andrea Fumagalli, Paolo Cattaneo and Matteo Strano
J. Manuf. Mater. Process. 2023, 7(3), 92; https://doi.org/10.3390/jmmp7030092 - 08 May 2023
Cited by 2 | Viewed by 2333
Abstract
The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring [...] Read more.
The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring progressive wear or at the detection of sudden failures or anomalies. Early detection of tool failure is the method preferred by the recent literature on data management in sheet metal stamping. However, the stamping of small parts poses challenges due to multiple tools and signals and limited visibility of die wear, requiring management of multiple sensors and data sources. This paper proposes a failure prevention approach for progressive die stamping using global and local force sensors with upper bounds for maximum values to indicate unhealthy conditions. The methodology was tested on millions of small washers made of carbon steel. The stamping process was implemented using a servo-press with a high rate. The press was equipped with eight in-process sensors, including strain gauges, thin foil force sensors, and acoustic sensors. The data of material properties, maintenance reports, statistical process control data, and in-process sensors were collected and stored in a data lake. By combining the in-process sensor acquisition with the corresponding log events and maintenance data in the same time span, it is possible to look for correlations among the variables and build an effective tool health prevention policy. Full article
(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)
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9 pages, 4681 KiB  
Communication
Effect of Welding Orientation in Angular Distortion in Multipass GMAW
by Lucas Martins Garcia, Verônica Teixeira Noronha and João Ribeiro
J. Manuf. Mater. Process. 2021, 5(2), 63; https://doi.org/10.3390/jmmp5020063 - 18 Jun 2021
Cited by 4 | Viewed by 3097
Abstract
The use of the welding process on an industrial scale has become significant over the years and is currently among the main processes for joining metallic materials. Along the weld, structural changes occur in the vicinity of the joint. These thermal stresses and [...] Read more.
The use of the welding process on an industrial scale has become significant over the years and is currently among the main processes for joining metallic materials. Along the weld, structural changes occur in the vicinity of the joint. These thermal stresses and geometric distortions are mostly undesirable and are complex to predict with precision. Using S235JR steel as the base material, laboratory experiments were carried out using the multipass GMAW process, with the aim of investigating the influence of the welding direction on angular distortion. To measure the distortions, a methodology was applied using equipment to identify the coordinates in the operational space with metrological precision. Through metrological and statistical analyses, we found that the orientation factor significantly influenced the final distortions and that the alternated orientation sequence resulted in less distortions. Full article
(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)
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Review

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30 pages, 10193 KiB  
Review
Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes
by Jingyuan Xu, Qiang Liu, Yuqing Xu, Runquan Xiao, Zhen Hou and Shanben Chen
J. Manuf. Mater. Process. 2024, 8(1), 22; https://doi.org/10.3390/jmmp8010022 - 28 Jan 2024
Viewed by 1185
Abstract
Arc welding is the common method used in traditional welding, which constitutes the majority of total welding production. The traditional manual and manual teaching welding method has problems with high labor costs and limited efficiency when faced with mass production. With the advancement [...] Read more.
Arc welding is the common method used in traditional welding, which constitutes the majority of total welding production. The traditional manual and manual teaching welding method has problems with high labor costs and limited efficiency when faced with mass production. With the advancement in technology, intelligent welding technology is expected to become a solution to this problem in the future. To achieve the intelligent welding process, modern sensing technology can be employed to effectively simulate the welder’s sensory perception and cognitive abilities. Recent studies have advanced the application of sensing technologies, leading to the advancement in intelligent welding process. The review is divided into two aspects. First, the theory and applications of various sensing technologies (visual, sound, arc, spectral signal, etc.) are summarized. Then, combined with the generalization of neural networks and attention mechanisms, the development trends in welding sensing information processing and modeling technology are discussed. Based on the existing research results, the feasibility, advantages, and development direction of attention mechanisms in the welding field are analyzed. In the end, a brief conclusion and remarks are presented. Full article
(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)
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20 pages, 1658 KiB  
Review
Insight into the Expected Impact of Sustainable Development in the Context of Industry 4.0: A Documentary Analysis Approach Based on Multiple Case Studies across the World
by Wilian Jesús Pech-Rodríguez, Eddie Nahúm Armendáriz-Mireles, Gladis Guadalupe Suárez-Velázquez, Carlos Adrián Calles-Arriaga and Enrique Rocha-Rangel
J. Manuf. Mater. Process. 2022, 6(3), 55; https://doi.org/10.3390/jmmp6030055 - 20 May 2022
Cited by 1 | Viewed by 3367
Abstract
Although industry 4.0 has gained increased attention in the industry, academic, and governmental fields, there is a lack of information about the relationship between this digital transformation and sustainable development. This work explores the concept of sustainability applied in industry 4.0 and the [...] Read more.
Although industry 4.0 has gained increased attention in the industry, academic, and governmental fields, there is a lack of information about the relationship between this digital transformation and sustainable development. This work explores the concept of sustainability applied in industry 4.0 and the main advantages that this revolution incorporates into society. To this end, a conscientiously documented investigation was conducted by reviewing actual case studies or scenarios where sustainability was applied in different manufacturing industries, enterprises, or research fields worldwide. A critical and descriptive analysis of the information was performed to identify the main tools and procedures that can be implemented in the industry to address the triple bottom line perspective of industry 4.0, and the results are presented in this document. From the analysis, it was observed that currently, I4.0 has been mainly adopted to improve efficiency and cost reduction in manufacturing companies. However, since only a few enterprises embrace the social paradigm of I4.0, a significant gap in understanding and unbalance is visualized. Therefore, we conclude that there is a lack of information on social benefits and the barriers that must be overcome from the social perspective. On the other hand, this work highlights the importance of adopting industry 4.0 as a positive way to improve the performance of emerging technologies, such as fuel cells, solar cells, and wind turbines, while producing products or services with high efficiency and profitability incomes. For practitioners, this work can provide insightful information about the real implications of I4.0 from a sustainability perspective in our daily life and the possible strategies to improve sustainable development. Full article
(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)
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17 pages, 700 KiB  
Review
Causal Discovery in Manufacturing: A Structured Literature Review
by Matej Vuković and Stefan Thalmann
J. Manuf. Mater. Process. 2022, 6(1), 10; https://doi.org/10.3390/jmmp6010010 - 14 Jan 2022
Cited by 32 | Viewed by 7183
Abstract
Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality [...] Read more.
Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed. Full article
(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)
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