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Innovations in Intelligent Machinery and Industry 4.0

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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: closed (20 December 2022) | Viewed by 27789

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Special Issue Editors

Prof. Dr. Amy J.C. Trappey

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Guest Editor

Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300044, Taiwan
Interests: knowledge engineering; intelligent system design; intellectual property (IP, patents, trademarks) analysis; engineering (tangible and intangible) asset management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ching-Hung Lee

E-Mail Website
Guest Editor

School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China
Interests: service design; smart retail; knowledge-based systems, digital transformation; e-government; sectoral systems of innovation

Prof. Dr. John P.T. Mo

E-Mail Website
Guest Editor

School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: robotics; product development; mechatronics; mobile robotics; system modeling; automation; machining; advanced control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent machinery and Industry 4.0 are often viewed in three levels, i.e., the device level, the connection level, and the systems management level. In the device level, intelligent machine tools need to have the ability to process and communicate information of inputs, outputs, and intermediary data for smart machining. The level of connection allows machine tools to exchange information and interrogate each other intelligently. The systems management level enables the entire factory or supply chain system to be operated and managed with full synchronization of humans, machines, and systems physically and cybernetically. This Special Issue calls for research papers which present innovative and advanced theories and practices in intelligent machinery and Industry 4.0.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

AI, machine learning, and deep learning approaches for complex decision making in smart machinery, such as intelligent control, intelligent diagnosis, and intelligent manufacturing planning;
Management and full utilization of big data collected in real-time from supply chains for intelligent decision makings;
Cloud and edge computing architectures, frameworks, and practical applications for smart machinery;
IT governance, data security, and accessibility issues for complex manufacturing systems and their stakeholders;
Real cases of innovative machinery design, intelligent machinery applications, implementations, and their impacts on manufacturing and services;
Lifecycle value chain management for smart machinery and Industry 4.0;
Advanced and integrated digital transformation (DT) technologies and applications for Industry 4.0;
Theoretical and empirical performance models for AI applications;
Explainable AI, responsible AI, and trustworthy AI for Industry 4.0;
Sustainable and smart product-service system (smart PSS) in Industry 4.0;
Managing risk of new technologies in Industry 4.0;
Green and circular economy concept-centric digital transformation (DT) and innovation ecosystems in Industry 4.0;
Business model-centric digital transformation (DT) and innovation ecosystems in Industry 4.0;
Green and circular economy concept-centric digital transformation (DT) and innovation ecosystems in Industry 4.0;
Forecasting and industrial competitiveness analysis for emerging digital technologies in Industry 4.0 based on bibliometrics and patent analysis;
Human factors and design for emerging manufacturing and servitization model in Industry 4.0;
Analyzing the functional dynamics and new models of supply chain and demand chain in Industry 4.0;
Industry 4.0-based industrial innovation model and technology policy research based on emerging digital technologies;
Expert systems, knowledge-based systems, and clouding manufacturing systems for Industry 4.0.

We look forward to receiving your contributions.

Prof. Dr. Amy J.C. Trappey
Prof. Dr. Ching-Hung Lee
Prof. Dr. John P.T. Mo
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

intelligent machinery
smart manufacturing
Industry 4.0
cyber-physical system (CPS)
digital twin
smart machine tool
intelligent control
intelligent sensor technology
artificial intelligence
machine/deep learning
smart product service system (smart PSS)
digital transformation
mass customization
sustainable manufacturing for Industry 4.0
human factors for Industry 4.0

Published Papers (12 papers)

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Editorial

Jump to: Research

5 pages, 183 KiB

Open AccessEditorial

Innovations for Interpretability, Flexibility, and Sustainability in Intelligent Machinery and Industry 4.0

by Amy J. C. Trappey, Ching-Hung Lee and John P. T. Mo

Appl. Sci. 2023, 13(9), 5257; https://doi.org/10.3390/app13095257 - 23 Apr 2023

Viewed by 1203

Abstract

Three levels, namely the device level, the connection level, and the systems management level, are frequently used to conceptualize intelligent machinery and Industry 4 [...] Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

Research

Jump to: Editorial

24 pages, 3833 KiB

Open AccessArticle

Service Process Problem-Solving Based on Flow Trimming

by Bai Zhonghang, Lin Siyue and Zhang Xu

Appl. Sci. 2023, 13(4), 2092; https://doi.org/10.3390/app13042092 - 6 Feb 2023

Cited by 2 | Viewed by 1034

Abstract

Since entering the era of the experience economy, consumers’ attention gradually turns toward gaining a pleasant service process experience. This study addresses the service process problem, aiming to discover the root cause of the service process problem and solve it by analyzing the service touchpoints flow delivery process. A method for solving problems in the service process based on flow trimming is proposed. The trimming method and the stochastic dominance rule are applied to the field of service design, which provides new concepts for service process problem solving. The flow is taken as the entry point of the proposed method. First, a flow model of problematic service touchpoints is constructed based on the service blueprint method to visualize the flow delivery process. Then, service process trimming rules are proposed and used as guidance to trim flow disadvantages, and resource analysis is employed to obtain specific programs. Finally, the stochastic dominance rule is used to rank the programs and select the optimal program. Problem solving in the medical treatment service process was taken as an example to trim the fundamental flow disadvantages of problematic service touchpoints. A series of programs were obtained and the optimal program was selected for presentation based on the stochastic dominance rule, which verified the feasibility of the proposed method. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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14 pages, 2208 KiB

Open AccessArticle

Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network

by Chien-Liang Liu, Chun-Jan Tseng, Wen-Hoar Hsaio, Sheng-Hao Wu and Shu-Rong Lu

Appl. Sci. 2022, 12(22), 11478; https://doi.org/10.3390/app122211478 - 11 Nov 2022

Cited by 4 | Viewed by 1699

Abstract

Predicting the wafer material removal rate (MRR) is an important step in semiconductor manufacturing for total quality control. This work proposes a deep learning model called a fusion network to predict the MRR, in which we consider separating features into shallow and deep features and use the characteristics of deep learning to perform a fusion of these two kinds of features. In the proposed model, the deep features go through a sequence of nonlinear transformations and the goal is to learn the complex interactions among the features to obtain the deep feature embeddings. Additionally, the proposed method is flexible and can incorporate domain knowledge into the model by encoding the knowledge as shallow features. Once the learning of deep features is completed, the proposed model uses the shallow features and the learned deep feature embeddings to obtain new features for the subsequent layers. This work performs experiments on a dataset from the 2016 Prognostics and Health Management Data Challenge. The experimental results show that the proposed model outperforms the competition winner and three ensemble learning methods. The proposed method is a single model, whereas the comparison methods are ensemble models. Besides the experimental results, we conduct extensive experiments to analyze the proposed method. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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19 pages, 3657 KiB

Open AccessFeature PaperArticle

Using Machine Learning Language Models to Generate Innovation Knowledge Graphs for Patent Mining

by Amy J. C. Trappey, Chih-Ping Liang and Hsin-Jung Lin

Appl. Sci. 2022, 12(19), 9818; https://doi.org/10.3390/app12199818 - 29 Sep 2022

Cited by 6 | Viewed by 2425

Abstract

To explore and understand the state-of-the-art innovations in any given domain, researchers often need to study many domain patents and synthesize their knowledge content. This study provides a smart patent knowledge graph generation system, adopting a machine learning (ML) natural language modeling approach, to help researchers grasp the patent knowledge by generating deep knowledge graphs. This research focuses on converting chemical utility patents, consisting of chemistries and chemical processes, into summarized knowledge graphs. The research methods are in two parts, i.e., the visualization of the chemical processes in the chemical patents’ most relevant paragraphs and a knowledge graph of any domain-specific collection of patent texts. The ML language modeling algorithms, including ALBERT for text vectorization, Sentence-BERT for sentence classification, and KeyBERT for keyword extraction, are adopted. These models are trained and tested in the case study using 879 chemical patents in the carbon capture domain. The results demonstrate that the average retention rate of the summary graphs for five clustered patent texts exceeds 80%. The proposed approach is novel and proven to be reliable in graphical deep knowledge representation. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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17 pages, 3145 KiB

Open AccessArticle

Cost-Effective and Portable Instrumentation to Enable Accurate pH Measurements for Global Industry 4.0 and Vertical Farming Applications

by Rolando Hinojosa-Meza, Ernesto Olvera-Gonzalez, Nivia Escalante-Garcia, José Alonso Dena-Aguilar, Martín Montes Rivera and Paulino Vacas-Jacques

Appl. Sci. 2022, 12(14), 7038; https://doi.org/10.3390/app12147038 - 12 Jul 2022

Cited by 2 | Viewed by 2007

Abstract

Global Vertical Farming (VF) applications with characteristic Industry 4.0 connectivity will become more and more relevant as the challenges of food supply continue to increase worldwide. In this work, a cost-effective and portable instrument that enables accurate pH measurements for VF applications is presented. We demonstrate that by performing a well-designed calibration of the sensor, a near Nernstian response, 57.56 [mV/pH], ensues. The system is compared to a ten-fold more expensive laboratory gold standard, and is shown to be accurate in determining the pH of substances in the 2–14 range. The instrument yields precise pH results with an average absolute deviation of 0.06 pH units and a standard deviation of 0.03 pH units. The performance of the instrument is ADC-limited, with a minimum detectable value of 0.028 pH units, and a typical absolute accuracy of ±0.062 pH units. By meticulously designing bias and amplification circuitry of the signal conditioning stage, and by optimizing the signal acquisition section of the instrument, a (minimum) four-fold improvement in performance is expected. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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17 pages, 4199 KiB

Open AccessArticle

Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry

by Samuel Ching Xin Ren, Jun Kit Chaw, Yee Mei Lim, Wah Pheng Lee, Tin Tin Ting and Cheng Weng Fong

Appl. Sci. 2022, 12(13), 6499; https://doi.org/10.3390/app12136499 - 27 Jun 2022

Cited by 2 | Viewed by 2164

Abstract

This paper aims to investigate the optimal sorting of orders reflecting on the material changing lead time over the machines in the roofing manufacturing industry. Specifically, a number of jobs were sorted together based on the material used and then consolidated for subsequent processes, i.e., assigned to the corresponding machines. To achieve the optimal sorting for the received orders, a combinatorial dispatch rule was proposed, which were Earliest Due Date (EDD), First In First Out (FIFO), and Shortest Processing Time (SPT). The sequence of orders organized by the scheduling algorithm was able to minimize the changing material lead time and also maximize the number of orders to be scheduled in the production. Consequently, on-time delivery could be achieved. Tests based on real data have been set up to evaluate the performance of the proposed algorithm in sorting the received orders. As a result, the proposed algorithm has successfully reduced the material changing lead time by 47.3% and 40% in the first and second tests, respectively. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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21 pages, 2773 KiB

Open AccessArticle

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

by Minyeol Yang, Junhyung Moon, Seowon Yang, Hyungsuk Oh, Soojin Lee, Yoonkyum Kim and Jongpil Jeong

Appl. Sci. 2022, 12(13), 6390; https://doi.org/10.3390/app12136390 - 23 Jun 2022

Cited by 10 | Viewed by 2244

Abstract

Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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17 pages, 6761 KiB

Open AccessArticle

A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt

by Dainius Varna and Vytautas Abromavičius

Appl. Sci. 2022, 12(11), 5608; https://doi.org/10.3390/app12115608 - 31 May 2022

Cited by 9 | Viewed by 3219

Abstract

The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor. Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems. This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components. The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection. The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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35 pages, 9132 KiB

Open AccessArticle

An Intelligent Handover Mechanism Based on MOS Predictions for Real-Time Video Conference Services in Mobile Networks

by Tsung-Han Lee, Lin-Huang Chang and Ya-Shu Chan

Appl. Sci. 2022, 12(8), 4049; https://doi.org/10.3390/app12084049 - 16 Apr 2022

Cited by 5 | Viewed by 2058

Abstract

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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22 pages, 353 KiB

Open AccessArticle

A Complete Software Stack for IoT Time-Series Analysis that Combines Semantics and Machine Learning—Lessons Learned from the Dyversify Project

by Dieter De Paepe, Sander Vanden Hautte, Bram Steenwinckel, Pieter Moens, Jasper Vaneessen, Steven Vandekerckhove, Bruno Volckaert, Femke Ongenae and Sofie Van Hoecke

Appl. Sci. 2021, 11(24), 11932; https://doi.org/10.3390/app112411932 - 15 Dec 2021

Cited by 4 | Viewed by 1947

Abstract

Companies are increasingly gathering and analyzing time-series data, driven by the rising number of IoT devices. Many works in literature describe analysis systems built using either data-driven or semantic (knowledge-driven) techniques. However, little to no works describe hybrid combinations of these two. Dyversify, a collaborative project between industry and academia, investigated how event and anomaly detection can be performed on time-series data in such a hybrid setting. We built a proof-of-concept analysis platform, using a microservice architecture to ensure scalability and fault-tolerance. The platform comprises time-series ingestion, long term storage, data semantification, event detection using data-driven and semantic techniques, dynamic visualization, and user feedback. In this work, we describe the system architecture of this hybrid analysis platform and give an overview of the different components and their interactions. As such, the main contribution of this work is an experience report with challenges faced and lessons learned. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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26 pages, 4992 KiB

Open AccessArticle

A Digital Twin-Based Platform towards Intelligent Automation with Virtual Counterparts of Flight and Air Traffic Control Operations

by Cho Yin Yiu, Kam K. H. Ng, Ching-Hung Lee, Chun Ting Chow, Tsz Ching Chan, Kwok Chun Li and Ka Yeung Wong

Appl. Sci. 2021, 11(22), 10923; https://doi.org/10.3390/app112210923 - 18 Nov 2021

Cited by 13 | Viewed by 2811

Abstract

Automation technologies have been deployed widely to boost the efficiency of production and operations, to trim the complicated process, and to reduce the human error involved. Nevertheless, aviation remains human-centred and requires collaboration between different parties. Given the lack of a collaborative decision-making training platform for air traffic operations in the industry, this study utilises the concept of cyber-physical systems (CPS) to formulate a system architecture for pilots and air traffic control officers training in collaborative decision making by linking and integrating the virtual counterparts of flights and air traffic control operations. Collaborative decision-making training and the corresponding intelligent automation aids could be realised and supported. A performance analysis via a flight task undertaken with different computational load settings was prepared to evaluate the platform’s latency and integrity. The latency is presented using its 95% confidence interval, and integrity is presented using the percentage of data loss during wireless transmission. The results demonstrated convincing performance and a promising system robustness in both domains. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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14 pages, 1830 KiB

Open AccessArticle

Applying Clustering Methods to Develop an Optimal Storage Location Planning-Based Consolidated Picking Methodology for Driving the Smart Manufacturing of Wireless Modules

by Tzu-An Chiang, Zhen-Hua Che, Ching-Hung Lee and Wei-Chi Liang

Appl. Sci. 2021, 11(21), 9895; https://doi.org/10.3390/app11219895 - 22 Oct 2021

Cited by 4 | Viewed by 2030

Abstract

Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically. Full article

(This article belongs to the Special Issue Innovations in Intelligent Machinery and Industry 4.0)

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