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Applied Sciences
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Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing...
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Design, Analysis, Intelligent Control and Optimization of Industrial and Manufacturing Processes

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1358

Special Issue Editors

Dr. Daynier Rolando Delgado Sobrino

E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
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:

  1. Smart manufacturing, design, analysis, intelligent control, and optimization of the processes;
  2. Special technologies in manufacturing, like additive manufacturing (3D printing);
  3. High-precision manufacturing optimized techniques, like surface milling, etching, electroforming and die casting;
  4. Techniques for improved manufacturing throughput;
  5. 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.;
  6. Trends in supply chain management and logistics;
  7. Advanced materials for industry, manufacturing, and products, like high-strength materials, lightweight materials, and carbon fiber;
  8. 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

Published Papers (2 papers)

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Research

15 pages, 8847 KiB  
Article
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 - 03 Apr 2024
Viewed by 435
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 [...] Read more.
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  
Article
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
Viewed by 593
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 [...] Read more.
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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