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Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 7529

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Special Issue Editors

Dr. Daynier Rolando Delgado Sobrino

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Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, Bratislava, Slovakia
Interests: Simulation of Manufacturing Processes and Systems; Industry 4.0; Logistics and Logistics 4.0; Optimization Methods, Design, and Analysis of Manufacturing Decisions (Material Flow, Layout, Others); Production and Maintenance Programming; Robotics Programming; Statistical Design and Validation of Measurement Experiments; Digital Twins; Virtual Commissionning
Special Issues, Collections and Topics in MDPI journals

Dr. Rafał Gołębski

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Faculty of Mechanical Engineering and Computer Science, Department of Technology and Automation, Częstochowa University of Technology, 42-201 Czestochowa, Poland
Interests: manufacturing technology; machining; CNC machine tools; gears technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Manufacturing is one of the most important backbones of modern society. Through the processes of industrial manufacturing, raw materials are transformed into finished products. These processes not only involve chemical, physical, electrical and mechanical steps, but also the same technological design of manufacturing instances, the whole process of control, optimization, logistics and innovation. As society evolves, so do manufacturing techniques. However, with the growing population and materials consumption, it is important to maintain sustainability and to improve the efficiency, precision, and flexibility of industrial and manufacturing processes.

With this Special Issue, we aim to promote discussion among researchers and engineers and share the most recent advancements in this field. We encourage the submission of studies that cover aspects related to the industrial and manufacturing processes, including, but not limited to, the following topics:

Smart manufacturing, design, analysis, intelligent control, and optimization of the processes;
Special technologies in manufacturing, like additive manufacturing (3D printing);
High-precision manufacturing optimized techniques, like surface milling, etching, electroforming and die casting;
Techniques for improved manufacturing throughput;
Industry 4.0 and its main technologies, such as the Internet of Things (IoT), cyber-physical systems (CPS), big data, simulation and digital twins, advanced robotics, augmented reality, artificial intelligence, machine learning, etc.;
Trends in supply chain management and logistics;
Advanced materials for industry, manufacturing, and products, like high-strength materials, lightweight materials, and carbon fiber;
New or hybrid forms of manufacturing and manufacturing systems.

Dr. Daynier Rolando Delgado Sobrino
Dr. Rafał Gołębski
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

manufacturing engineering
manufacturing processes
smart manufacturing
high-precision manufacturing
Industry 4.0
supply chain management
logistics
smart logistics
internet of things (IoT)
additive manufacturing
artificial intelligence
machine learning
processes and manufacturing design
processes and manufacturing analysis
processes and manufacturing intelligent control and optimization

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Published Papers (9 papers)

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Research

22 pages, 4119 KiB

Open AccessArticle

Fast Detection of Idler Supports Using Density Histograms in Belt Conveyor Inspection with a Mobile Robot

by Janusz Jakubiak and Jakub Delicat

Appl. Sci. 2024, 14(23), 10774; https://doi.org/10.3390/app142310774 - 21 Nov 2024

Viewed by 140

Abstract

The automatic inspection of belt conveyors gathers increasing attention in the mining industry. The utilization of mobile robots to perform the inspection allows increasing the frequency and precision of inspection data collection. One of the issues that needs to be solved is the location of inspected objects, such as, for example, conveyor idlers in the vicinity of the robot. This paper presents a novel approach to analyze the 3D LIDAR data to detect idler frames in real time with high accuracy. Our method processes a point cloud image to determine positions of the frames relative to the robot. The detection algorithm utilizes density histograms, Euclidean clustering, and a dimension-based classifier. The proposed data flow focuses on separate processing of single scans independently, to minimize the computational load, necessary for real-time performance. The algorithm is verified with data recorded in a raw material processing plant by comparing the results with human-labeled objects. The proposed process is capable of detecting idler frames in a single 3D scan with accuracy above 83%. The average processing time of a single scan is under 22 ms, with a maximum of 75 ms, ensuring that idler frames are detected within the scan acquisition period, allowing continuous operation without delays. These results demonstrate that the algorithm enables the fast and accurate detection and localization of idler frames in real-world scenarios. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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25 pages, 4092 KiB

Open AccessArticle

Optimization of Wiring Harness Logistics Flow in the Automotive Industry

by Cicerone Laurentiu Popa, Ioana Iorga, Costel Emil Cotet, Ana Maria Ifrim, Constantin-Adrian Popescu and Tiberiu Gabriel Dobrescu

Appl. Sci. 2024, 14(22), 10636; https://doi.org/10.3390/app142210636 - 18 Nov 2024

Viewed by 317

Abstract

This paper presents a compelling argument for optimizing the logistics flow of wiring harnesses within the automotive industry to address the rising production demands of vehicle manufacturers. It introduces an innovative assembly line structure specifically designed to boost efficiency and enhance responsiveness to client needs. Drawing from data gathered from an actual assembly line dedicated to producing engine harnesses for K9K engines, this study offers a practical and impactful foundation for its proposed optimization strategies. The new assembly structure effectively merges the benefits of a dynamic line—which emphasizes efficient space utilization and flexibility—with the strengths of a rotary line, particularly in light of the increasing complexity associated with harness production. The paper features a mathematical model that calculates cycle times for workstations within this new system architecture, optimizing the entire production process. Moreover, it illustrates how advanced modeling, simulation, and optimization techniques using WITNESS Horizon Version Release 25.0 can identify necessary adjustments for achieving optimal assembly line balance. Additionally, this research addresses pressing environmental concerns by proposing a robust recycling strategy for the scrap produced during wiring harness manufacturing. By advocating for sustainable practices and responsible waste management, the study highlights the importance of minimizing the ecological footprint of the automotive manufacturing process. In summary, this research provides essential insights and practical solutions for optimizing wiring harness logistics flow in the automotive industry. By implementing these strategies, manufacturers can significantly enhance their production capacity, improve operational efficiency, and maintain competitiveness in an ever-evolving market landscape. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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35 pages, 4036 KiB

Open AccessArticle

Optimization Processes in Automotive Logistic Flow

by Cicerone Laurentiu Popa, Floarea-Loredana Seileanu, Costel Emil Cotet, Florina Chiscop and Constantin-Adrian Popescu

Appl. Sci. 2024, 14(21), 10064; https://doi.org/10.3390/app142110064 - 4 Nov 2024

Viewed by 493

Abstract

This paper presents a logistic flow of assembling automotive rear axles. The product is presented in detail starting from the detailed research and analysis of relevant documentation about its functionality, including the manufacturing logistic flow diagram and the required equipment for the product manufacturing and assembly. This study is focused on optimizing the logistic flow for the manufacturing and assembly of automotive rear axles using WITNESS Horizon for system modeling and simulation in order to conduct system diagnostics, identify problems, and find solutions that will facilitate the optimization process. The study included a comprehensive assessment of the logistic flow, highlighting the performance of the equipment involved and identifying potential bottlenecks. Using the results obtained after the simulations, the Simplex linear mathematical method was applied to maximize production efficiency and profitability, considering the suppliers’ capacity constraints and the components’ delivery requirements. The results demonstrated a significantly optimized rear-axle production process, with increased profitability and improved productivity by eliminating identified bottlenecks. This research contributes to a deeper understanding of the complexities within the automotive industry and provides a solid foundation for continuously improving manufacturing and assembly processes. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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22 pages, 9879 KiB

Open AccessArticle

Optimizing Assembly in Wiring Boxes Using API Technology for Digital Twin

by Carmen-Cristiana Cazacu, Ioana Iorga, Radu Constantin Parpală, Cicerone Laurențiu Popa and Costel Emil Coteț

Appl. Sci. 2024, 14(20), 9483; https://doi.org/10.3390/app14209483 - 17 Oct 2024

Viewed by 694

Abstract

This study explores the automation enhancement in the assembly process of wiring harnesses for automotive applications, focusing on manually inserting fuses and relays into boxes—a task known for quality and efficiency challenges. This research aimed to address these challenges by implementing a robotic arm integrated with API technology for digital twin. The methods used included the development of a digital twin model to simulate and monitor the assembly process, supported by real-time adjustments and optimizations. The results showed that the robotic system significantly improved the accuracy and speed of fuse insertion, reducing the insertion errors typically seen in manual operations. The conclusions drawn from the research confirm the feasibility of using robotic automation supported by digital twin technology to enhance assembly processes in automotive manufacturing, promising substantial improvements in production efficiency and quality control. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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21 pages, 5164 KiB

Open AccessArticle

Camera Calibration in High-Speed Robotic Assembly Operations

by Radu Constantin Parpală, Mario Andrei Ivan, Lidia Florentina Parpală, Costel Emil Coteț and Cicerone Laurențiu Popa

Appl. Sci. 2024, 14(19), 8687; https://doi.org/10.3390/app14198687 - 26 Sep 2024

Viewed by 643

Abstract

The increase in positioning accuracy and repeatability allowed the integration of robots in assembly operations using guidance systems (structured applications) or video acquisition systems (unstructured applications). This paper proposes a procedure to determine the measuring plane using a 3D laser camera. To validate the procedure, the camera coordinates and orientation will be verified using robot coordinates. This procedure is an essential element for camera calibration and consists of developing a mathematical model using the least square method and planar regression. The mathematical model is considered necessary as a step towards optimizing the integration of robotic vision systems in assembly applications. A better calibrated camera has the potential to provide better recognition results, which are essential in this field. These improved results can then be used to increase the accuracy and repeatability of the robot. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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28 pages, 1533 KiB

Open AccessArticle

Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems

by Daniel Guzman Vargas, Sidharta Gautama, Mehmet Uzunosmanoglu, Birger Raa and Veronique Limère

Appl. Sci. 2024, 14(14), 6035; https://doi.org/10.3390/app14146035 - 10 Jul 2024

Viewed by 823

Abstract

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a “one-size-fits-all” approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra—a state-of-the-art PIAC method—and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method’s performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra’s methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra’s inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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21 pages, 3997 KiB

Open AccessArticle

The Logistics of Volkswagen Development Center Applies Operations Research to Optimize Transshipments

by Bastian Vorwerk and Sebastian Trojahn

Appl. Sci. 2024, 14(11), 4917; https://doi.org/10.3390/app14114917 - 5 Jun 2024

Viewed by 1159

Abstract

Volkswagen Technical Development (TE) is responsible for all prototype development and prototype production for the Volkswagen brand and has its own logistics department (TE-Logistics). In the logistics of prototype parts in the automotive industry, new versions of prototype parts (henceforth referred to as updating parts) are repeatedly assembled in finished prototype vehicles. These updating parts are stored in warehouses and provided to an assembly site to ensure a timely assembly of the associated prototype vehicles. As the internal warehouse on the company site is not large enough for the high variety of parts, an additional external warehouse in the logistics network is needed. However, since prototype parts are unique, the allocation of the parts in suitable warehouses is particularly important. Currently, the various warehouses and the short-term demands repeatedly lead to reactive transshipments between the warehouses. To this end, we developed an approach for proactive transshipments based on a machine learning forecast and a mixed-integer linear programming model for planning proactive transshipments of parts between the warehouses to minimize transport costs. The model is based on a probability estimation of future demands to anticipate the expected optimal warehouse. After the model had revealed high improvement potential through a case study with real-world data in terms of costs and availability time compared to the current reactive process, we derived decision rules and developed a rule-based heuristic algorithm that leads to the optimal solution for the industrial use case. We implemented the heuristic with a spreadsheet-based decision support system (DSS) for daily transshipment planning. After successful test implementation, TE-Logistics estimated the annual cost savings for transport to be approximately 10%. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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15 pages, 8847 KiB

Open AccessArticle

Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications

by Uğur Akış and Serkan Dişlitaş

Appl. Sci. 2024, 14(7), 3002; https://doi.org/10.3390/app14073002 - 3 Apr 2024

Viewed by 1573

Abstract

In applications reliant on image processing, the management of lighting holds significance for both precise object detection and efficient energy utilization. Conventionally, lighting control involves manual switching, timed activation or automated adjustment based on illuminance sensor readings. This research introduces an embedded system employing image processing methodologies for intelligent ambient lighting, focusing specifically on reference-color-based illumination for object detection and positioning within robotic handling scenarios. Evaluating the system’s efficacy entails analyzing the illuminance levels and power consumption through a tailored experimental setup. To minimize illuminance, the LED-based lighting system, controlled via pulse-width modulation (PWM), is calibrated using predetermined red, green, blue and yellow (RGBY) reference objects, obviating the need for external sensors. Experimental findings underscore the significance of color choice in detection accuracy, highlighting yellow as the optimal color requiring minimal illumination. Successful object detection based on color is demonstrated at an illuminance level of approximately 50 lx, accompanied by energy savings contingent upon ambient lighting conditions. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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12 pages, 288 KiB

Open AccessArticle

Monitoring, Evaluation, and Improvement Model for Process Precision and Accuracy

by Chih-Ming Tsai, Kuo-Ching Chiou, Kuen-Suan Chen and Chun-Min Yu

Appl. Sci. 2023, 13(20), 11280; https://doi.org/10.3390/app132011280 - 13 Oct 2023

Cited by 1 | Viewed by 1027

Abstract

Process Capability Indices (PCIs) are devices widely used in the industry to evaluate process quality. The commonly used process capability indices all contain accuracy indices and precision indices. As the accuracy index is closer to zero, the process accuracy is higher. The precision index mainly represents the extent of process variation. As the value is smaller, the process variation is smaller, that is, the precision is higher. In fact, process capability indices are the functions of accuracy indices and precision indices. Obviously, as long as accuracy indices and precision indices are controlled, the process capability indices can be controlled as well. Therefore, this study first derived accuracy and precision control charts to observe not only process accuracy but also process precision. Then, this study adopted in-control data to acquire a 100 (1 −

α

)% confidence region of an accuracy index and a precision index, with which statistical tests were performed. Subsequently, according to the definition of the six sigma quality level, both indices were examined. Furthermore, based on the testing results, suggestions for process improvement were proposed, including correcting the direction of process deviation and deciding whether to reduce process variation. Finally, this study demonstrated the applicability of the proposed model using an empirical example. Full article

(This article belongs to the Special Issue Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes)

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