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Applied Sciences
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Intelligent Manufacturing and Automation
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Intelligent Manufacturing and Automation

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 4544

Special Issue Editors

Prof. Dr. Aidy Ali

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Faculty of Engineering, National Defence University of Malaysia (UPNM), Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia
Interests: composites; fatigue fracture mechanics; FEM; impact; mechanical engineering
Prof. Dr. Nader Asnafi

E-Mail Website
Guest Editor
School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden
Interests: digital transformation; materials and technologies for a circular economy; development and operation/use of sustainable products and manufacturing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current COVID-19 pandemic has created a significant challenge for manufacturing operations at a high production rate and automation level.

The cost of repair and operation breakdowns in manufacturing during the pandemic has set alarm bells ringing for designers, engineers, and manufacturers, who must decide whether the existing automation systems in production can be kept or whether their approach needs to change. 

This Special Issue aims to cover recent advances in manufacturing and automation including the methods, and the use of Artificial Intelligence (AI), Internet of Things (IoT), cyber-physical control and own automation systems integrated with those of the suppliers’, distributors’ and customers’. Intelligent manufacturing and automation cover a range of topics, from material handling, process and production control, optimization and quality engineering to robotics, AI, IOT, just-in-time production, and all the tools used in advanced manufacturing to support highly efficient production of mechanical, civil, and electrical applications.

The aim of this issue is to share recent findings related to manufacturing and automation and how they can be applied toward fast and sustainable production in a circular economy resilient against the challenges brought on by the COVID-19 pandemic. Especially welcome are papers which are not too theoretical and which guide the reader in terms of how the research findings can be applied to their own manufacturing and automation systems.

Your contribution to this Special Issue is highly valued and appreciated.

Prof. Dr. Aidy Ali
Prof. Dr. Nader Asnafi
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.

Published Papers (2 papers)

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Research

25 pages, 8516 KiB  
Article
Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System
by Sangil Lee and Kwangyeol Ryu
Appl. Sci. 2022, 12(10), 5172; https://doi.org/10.3390/app12105172 - 20 May 2022
Cited by 6 | Viewed by 2049
Abstract
Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the [...] Read more.
Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Automation)
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15 pages, 5619 KiB  
Article
The 3D Deburring Processing Trajectory Recognition Method and Its Application Base on Random Sample Consensus
by Chun-Chien Ting, Cheng-Kai Huang, Shean-Juinn Chiou and Kun-Ying Li
Appl. Sci. 2022, 12(10), 4852; https://doi.org/10.3390/app12104852 - 11 May 2022
Cited by 1 | Viewed by 1481
Abstract
As of 2022, most automatic deburring trajectories are still generated using offline programming methods. The trajectories generated using these methods are often suboptimal, which limits the precision of the robotic arms used to perform automatic deburring and, in turn, results in workpiece dimensional [...] Read more.
As of 2022, most automatic deburring trajectories are still generated using offline programming methods. The trajectories generated using these methods are often suboptimal, which limits the precision of the robotic arms used to perform automatic deburring and, in turn, results in workpiece dimensional errors. Therefore, despite advances in automated deburring trajectory generation, deburring is still mostly performed manually. However, manual deburring is a time-consuming, labor-intensive, and expensive process that results in small profit margins for organizational equipment manufacturers (OEMs). To address these problems and the obstacles to the implementation of automated deburring in the robotics industry, the present study developed an online automated deburring trajectory generation method that uses 2D contouring information obtained from linear contour scanning sensors, a CAD model, and curve fitting to detect burrs and generate appropriate trajectories. The method overcomes many of the limitations of common deburring methods, especially by enabling real-time trajectory tracking. When the method was tested using bicycle forks, work that originally took three to four people 8–12-h to complete was completed by one person in 30 min, and the production cost was reduced by 70%. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Automation)
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