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2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling

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Keywords
Published Papers

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 30221

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Special Issue Editors

Prof. Dr. Chien-Hung Liu

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Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
Interests: high precision instrument design; laser engineering; smart sensors and actuators; optical device; optical measurement; metrology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jyh-Horng Chou

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1. Department of Electrical Engineering, National Kaohsiung University of Science and Technology, 415 Chien-Kung Road, Kaohsiung 807, Taiwan
2. Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
Interests: artificial intelligence; information technology and system integration; system modeling and simulation; system dynamics and control; integration technology of automation systems; numerical analysis and computational mathematics; robust optimization technology
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Prof. Dr. Chih Jer Lin

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Graduate Institute of Automation Technology, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan
Interests: numerical simulation; nonlinear control; mechatronics; precision motion control; system identiﬁcation; sliding-mode control; robotics; evolutionary algorithms
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Prof. Dr. Cheng-Chi Wang

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Ph.D. Program, Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
Interests: nonlinear dynamic analysis; smart machine; chaos; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global manufacturing industries emphasize Smart Manufacturing/Industry 4.0 research issues, which include the following: Production systems, quality assurance, process optimization, and digital modeling. The application and integration of statistical methods, computational intelligence, artificial intelligence, and control technology provide such solutions. This Special Issue invites authors to submit their high-quality papers in 2021 related to following topics:

(1) Production systems:
Production schedule, production facilities (condition sensing and monitoring, predictive maintenance, condition measurement and estimation, automatic calibration and compensation, online adjustment, automatic control technology);

(2) Quality assurance:
Quality examination, quality estimation, quality prognosis, diagnosis, and analysis of process condition;

(3) Process optimization:
Process capability optimization, process parameter optimization, process proficiency optimization, energy usage optimization, and process stability optimization;

(4) Digital modeling:
Creating digital twins and twin models.

Prof. Dr. Chien-Hung Liu
Prof. Dr. Jyh-Horng Chou
Prof. Dr. Chih Jer Lin
Prof. Dr. Cheng-Chi Wang
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

Production system
Quality assurance
Process optimization
Digital modeling
Statistical methods
Computational intelligence
Artificial intelligence
Control technology

Published Papers (8 papers)

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Research

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22 pages, 525 KiB

Open AccessArticle

Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)

by Rim Zarrouk, Wided Ben Daoud, Sami Mahfoudhi and Abderrazak Jemai

Appl. Sci. 2022, 12(6), 2829; https://doi.org/10.3390/app12062829 - 09 Mar 2022

Cited by 3 | Viewed by 2045

Abstract

Since of the advent of Industry 4.0, embedded systems have become an indispensable component of our life. However, one of the most significant disadvantages of these gadgets is their high power consumption. It was demonstrated that making efficient use of the device’s central processing unit (CPU) enhances its energy efficiency. The use of the particle swarm optimization (PSO) over an embedded environment achieves many resource problems. Difficulties of online implementation arise primarily from the unavoidable lengthy simulation time to evaluate a candidate solution. In this paper, an embedded two-level PSO (E2L-PSO) for intelligent real-time simulation is introduced. This algorithm is proposed to be executed online and adapted to embedded applications. An automatic adaptation of the asynchronous embedded two-level PSO algorithm to CPU is completed. The Flexible Job Shop Scheduling Problem (FJSP) is selected to solve, due to its importance in the Industry 4.0 era. An analysis of the run-time performance on handling E2L-PSO over an STM32F407VG-Discovery card and a Raspberry Pi B+ card is conducted. By the experimental study, such optimization decreases the CPU time consumption by 10% to 70%, according to the CPU reduction needed (soft, medium, or hard reduction). Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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18 pages, 3808 KiB

Open AccessArticle

Integration of Functional Link Neural Networks into a Parameter Estimation Methodology

by Tuan-Ho Le, Mengyuan Tang, Jun Hyuk Jang, Hyeonae Jang and Sangmun Shin

Appl. Sci. 2021, 11(19), 9178; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199178 - 02 Oct 2021

Cited by 2 | Viewed by 1812

Abstract

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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15 pages, 952 KiB

Open AccessArticle

The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm

by Yung-Chun Liu, Ming-Huwi Horng, Yung-Yi Yang, Jian-Han Hsu, Yen-Ting Chen, Yu-Chen Hung, Yung-Nien Sun and Yu-Hsuan Tsai

Appl. Sci. 2021, 11(11), 4857; https://0-doi-org.brum.beds.ac.uk/10.3390/app11114857 - 25 May 2021

Cited by 2 | Viewed by 1891

Abstract

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model to build such a surrogate model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9000 test samples used for building the surrogate model and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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22 pages, 2923 KiB

Open AccessArticle

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

by Zeinab Shahbazi and Yung-Cheol Byun

Appl. Sci. 2021, 11(8), 3535; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083535 - 15 Apr 2021

Cited by 24 | Viewed by 4211

Abstract

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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19 pages, 7887 KiB

Open AccessArticle

Hybrid Optimization Method for Correcting Synchronization Errors in Tapping Center Machines

by Ping-Yueh Chang, Po-Yuan Yang, Shao-Hsien Chen and Jyh-Horng Chou

Appl. Sci. 2021, 11(8), 3441; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083441 - 12 Apr 2021

Cited by 2 | Viewed by 1872

Abstract

A hybrid method is proposed for optimizing rigid tapping parameters and reducing synchronization errors in Computer Numerical Control (CNC) machines. The proposed method integrates uniform design (UD), regression analysis, Taguchi method, and fractional-order particle swarm optimizer (FPSO) to optimize rigid tapping parameters. Rigid tapping parameters were laid out in a 28-level uniform layout for the experiments in this study. Since the UD method provided a layout with uniform dispersion in the experimental space, the UD method’s uniform layout provided iconic experimental points. Next, the 28-level uniform layout results and regression analysis results were used to obtain significant parameters and a regression function. To obtain the parameter values from the regression function, FPSO was selected because its diversity and algorithmic effectiveness are enhanced compared with PSO. The experimental results indicated that the proposed method could obtain suitable parameter values. The best parameter combination in FPSO yielded the best results in comparisons of the non-systematic method. Next, the best parameter combination was used to optimize actual CNC machining tools during the factory commissioning process. From the commissioning process perspective, the proposed method rapidly and accurately minimizes synchronization error from 23 pulses to 18 pulses and processing time from 20.8 s to 20 s. In conclusion, the proposed method reduced the time needed to tune factory parameters for CNC machining tools and increased machining precision and decreased synchronization errors. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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13 pages, 11575 KiB

Open AccessArticle

Novel Abrasive-Impregnated Pads and Diamond Plates for the Grinding and Lapping of Single-Crystal Silicon Carbide Wafers

by Ming-Yi Tsai, Kun-Ying Li and Sun-Yu Ji

Appl. Sci. 2021, 11(4), 1783; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041783 - 17 Feb 2021

Cited by 5 | Viewed by 3465

Abstract

In this study, special ceramic grinding plates impregnated with diamond grit and other abrasives, as well as self-made lapping plates, were used to prepare the surface of single-crystal silicon carbide (SiC) wafers. This novel approach enhanced the process and reduced the final chemical mechanical planarization (CMP) polishing time. Two different grinding plates with pads impregnated with mixed abrasives were prepared: one with self-modified diamond + SiC and a ceramic binder and one with self-modified diamond + SiO2 + Al2O3 + SiC and a ceramic binder. The surface properties and removal rate of the SiC substrate were investigated and a comparison with the traditional method was conducted. The experimental results showed that the material removal rate (MRR) was higher for the SiC substrate with the mixed abrasive lapping plate than for the traditional method. The grinding wear rate could be reduced by 31.6%. The surface roughness of the samples polished using the diamond-impregnated lapping plate was markedly better than that of the samples polished using the copper plate. However, while the surface finish was better and the grinding efficiency was high, the wear rate of the mixed abrasive-impregnated polishing plates was high. This was a clear indication that this novel method was effective and could be used for SiC grinding and lapping. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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13 pages, 1937 KiB

Open AccessArticle

The Implementation of Digital Ergonomics Modeling to Design a Human-Friendly Working Process in a Postal Branch

by Darko Drašković, Petr Průša, Svetlana Čičević and Stefan Jovčić

Appl. Sci. 2020, 10(24), 9124; https://0-doi-org.brum.beds.ac.uk/10.3390/app10249124 - 21 Dec 2020

Cited by 5 | Viewed by 2880

Abstract

In today’s business world, there are two very important health issues that the employees in the service sector are faced with: Spinal disease, especially in the lower back, and carpal tunnel syndrome. These are well-known musculoskeletal disorders. To preserve the health of its employees and prevent professional illnesses, and to thus maximize their efficiency, companies must use knowledge from the field of ergonomics. This study aims to examine the work-related health problems that the workers in transportation companies are faced with. As a case study, a postal company from Serbia is considered, with particular attention paid to the counter clerks. The research was carried out in one branch of the postal operator. All six postal clerks working at the considered branch were subjects of the study. The workers were observed visually by the researchers and recorded while performing their job tasks. Based on the analysis of their movements and body positions, the evaluation of their level of risk exposure was determined using the Ergo Fellow software, specifically with five packages within this program (Rapid Upper Limb Assessment, Rapid Entire Body Assessment, Ovako working posture analysis system, Moore and Garg, and Suzanne Rodgers). As a result of the implemented tools, the analysts are in a position to conclude what should be changed in the work organization. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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Review

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20 pages, 2543 KiB

Open AccessReview

Overall Equipment Effectiveness: Systematic Literature Review and Overview of Different Approaches

by Lisbeth del Carmen Ng Corrales, María Pilar Lambán, Mario Enrique Hernandez Korner and Jesús Royo

Appl. Sci. 2020, 10(18), 6469; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186469 - 17 Sep 2020

Cited by 35 | Viewed by 10226

Abstract

Overall equipment effectiveness (OEE) is a key performance indicator used to measure equipment productivity. The purpose of this study is to review and analyze the evolution of OEE, present modifications made over the original model and identify future development areas. This paper presents a systematic literature review; a structured and transparent study is performed by establishing procedures and criteria that must be followed for selecting relevant evidences and addressing research questions effectively. In a general search, 862 articles were obtained; after eliminating duplicates and applying certain inclusion and exclusion criteria, 186 articles were used for this review. This research presents three principal results: (1) The academic interest in this topic has increased over the last five years and the keywords have evolved from being related to maintenance and production, to being related to lean manufacturing and optimization; (2) A list of authors who have developed models based on OEE has been created; and (3) OEE is an emerging topic in areas such as logistics and services. To the best of our knowledge, no comparable review has been published recently. This research serves as a basis for future relevant studies. Full article

(This article belongs to the Special Issue 2021 Smart Manufacturing on Production System, Quality Assurance, Process Optimization, and Digital Modeling)

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