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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 28460

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Special Issue Editors

Prof. Dr. Shih-Ming Wang

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Guest Editor

Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
Interests: smart manufacturing; on-line intelligent monitoring and control; error measurement and compensation; machining dynamics and application; precision machine design and analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Chunhui Chung

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Guest Editor

Department of Mechanical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan
Interests: smart manufacturing; machining; abrasive machining; computer-aided manufacturing

Prof. Dr. Ping Guo

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Guest Editor

Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208-3109, USA
Interests: micro/meso-scale manufacturing; computer-vision-based metrology; micro-additive manufacturing; micro-structured functional surfaces; structural coloration; elliptical vibration texturing

Special Issue Information

Dear Colleagues,

As the concept of Industry 4.0 has become widely implemented in the manufacturing industry with modern technologies such as AIoT, sensing technology, information technology, artificial etc., it has led to significant changes and possibilities in the methods, tools, and systems supporting the machines and factories of the future. This Special Issue on Smart Machines and Intelligent Manufacturing (SMIM) provides a platform for the review and discussion of theoretical advances, research results, and industrial experiences among scientists, researchers, industry experts, and users dealing with the issues of Smart Machines and Smart Manufacturing.

This Special Issue welcome articles with original ideas and high-quality research outcomes on smart machine development and performance enhancement for manufacturing processes such as machining, forming, additive manufacturing, auto assembly, digital manufacturing, and quality measurement, with a view to practical implementation, including industrial case studies and original solutions. Original research contributions and reviews are invited for this Special Issue.

Prof. Dr. Shih-Ming Wang
Dr. Chunhui Chung
Prof. Dr. Ping Guo
Guest Editors

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Keywords

smart machine
intelligent manufacturing
sensing technology
artificial intelligence
machining
additive manufacturing
digital manufacturing
quality measurement
CPS

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Published Papers (11 papers)

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Research

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13 pages, 3415 KiB

Open AccessArticle

Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering

by Young Jong Song, Ki Hyun Nam and Il Dong Yun

Appl. Sci. 2023, 13(13), 7569; https://doi.org/10.3390/app13137569 - 27 Jun 2023

Cited by 1 | Viewed by 1094

Abstract

Surface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee products, the number of models has to be reduced and products with similar characteristics have to be grouped and overseen. In this paper, we show that it is possible to handle a large number of new products using latent vectors obtained from the autoencoder model. By hierarchically clustering latent vectors, the model finds product groups with similar characteristics and oversees them by group. Furthermore, we validate our multi-product operation strategy for anomaly detection with a newly collected SMD dataset. Experimental results show that the anomaly detection method using hierarchical clustering of latent vectors is a practical management method for SMD anomaly detection. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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27 pages, 7603 KiB

Open AccessArticle

Design of Manufacturing Systems Based on Digital Shadow and Robust Engineering

by Dimitris Mourtzis and Nikos Balkamos

Appl. Sci. 2023, 13(8), 5184; https://doi.org/10.3390/app13085184 - 21 Apr 2023

Cited by 4 | Viewed by 2259

Abstract

In the era of digital transformation, industry is facing multiple challenges due to the need for implementation of the Industry 4.0 standards, as well as the volatility of customer demands. The latter has created the need for the design and operation of more complex manufacturing systems and networks. A case study derived from Process Industries (PIs) is adopted in this research work in order to design a framework for flexible design of production lines, automation of quality control points, and improvement of the performance of the manufacturing system. Therefore, a Digital Shadow of a production line is developed to collect, analyze and identify potential issues (bottlenecks). An edge computing system for reliable and low-latency communications is also implemented. The digital model is validated using statistical Design Of Experiments (DOE) and ANalysis Of VAriance (ANOVA). For the assessment of what-if scenarios, the Digital Shadow model will be used in order to evaluate and find the desired solution. Ultimately, the goal of this research work is to improve the design and performance of the industry’s production section, as well as to increase the production rate and the product mix. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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20 pages, 5363 KiB

Open AccessArticle

Selection of Additive Manufacturing Machines via Ontology-Supported Multi-Attribute Three-Way Decisions

by Meifa Huang, Bing Fan, Long Chen, Yanting Pan and Yuchu Qin

Appl. Sci. 2023, 13(5), 2926; https://doi.org/10.3390/app13052926 - 24 Feb 2023

Cited by 3 | Viewed by 1512

Abstract

Selection of a suitable additive manufacturing (AM) machine to manufacture a specific product is one of the important tasks in design for AM. So far, many selection approaches based on multi-attribute decision making have been proposed within academia. Each of these approaches works well in its specific context. However, the approaches are not flexible enough and could produce undesirable results as they are all based on multi-attribute two-way decisions. In this paper, a selection approach based on ontology-supported multi-attribute three-way decisions is presented. Firstly, an ontology for AM machine selection is constructed according to vendor documents, benchmark data, expert experience, and the Senvol database. Supported by this ontology, a selection approach based on multi-attribute three-way decisions is then developed. After that, four AM machine selection examples are introduced to illustrate the application of the developed approach. Finally, the effectiveness and advantages of the approach are demonstrated via a set of comparison experiments. The demonstration results suggest that the presented approach is as effective as the existing approaches and more flexible than them when the information for decision making is insufficient or the cost for undesirable decision results is high. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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22 pages, 13533 KiB

Open AccessArticle

Design of a Compact Planar Magnetic Levitation System with Wrench–Current Decoupling Enhancement

by Chanuphon Trakarnchaiyo, Yang Wang and Mir Behrad Khamesee

Appl. Sci. 2023, 13(4), 2370; https://doi.org/10.3390/app13042370 - 12 Feb 2023

Cited by 7 | Viewed by 2664

Abstract

Magnetic levitation technology has promising applications in modern manufacturing, especially for fine-motion stage and long-range omnidirectional planar motors. This paper presents the development of a compact planar maglev prototype with the potential to achieve both applications to increase flexibility for the manufacturing system. The planar stator is designed by using optimized square coils arranged in the zigzag configuration, which provides a better uniform magnetic flux density compared with another configuration. The stator is a compact and portable module with built-in current amplifier units. The single-disc magnet mover is deployed with five controllable degrees of freedom. The cross-coupling effect is decoupled by a precomputed Lorentz force based wrench—current transformation matrix stored in the lookup table. A 2-D linear interpolation is implemented to enhance decoupling effectiveness which is offered via discrete lookup data. Experiments with motion-tracking cameras and a basic controller demonstrate the results of fine step motion of 10 and 20 µm and rotation steps of 0.5 and 1.0 mrad. The potential for multidirectional material handling is represented by a total horizontal translation range of 20 mm by 20 mm with a maximum air gap of 26 mm and a total rotation range of 20 degrees for both roll and pitch. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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18 pages, 6178 KiB

Open AccessArticle

Investigation of Cutting Path Effect on Spindle Vibration and AE Signal Features for Tool Wear Monitoring in Micro Milling

by Ci-Rong Huang and Ming-Chyuan Lu

Appl. Sci. 2023, 13(2), 1107; https://doi.org/10.3390/app13021107 - 13 Jan 2023

Cited by 6 | Viewed by 1773

Abstract

In the development of a tool wear monitoring system in milling, the complex cutting path always brings challenges to the system’s reliability in the production line. The cutting path effect on the acoustic emission (AE) and vibration signals during the micro milling processes was investigated in this study by implementing three types of cutting paths in a micro milling experiment. To generate the data for analysis, an experiment was conducted on a micro milling research platform using an AE sensor and an accelerometer installed on a fixture attached to the spindle housing. To demonstrate the effect of the cutting path on the performance in the monitoring of tool wear, a simple linear classifier is proposed, along with the signal features generated from the different signal lengths and the bandwidth size in the frequency domain. The results show that the signal features generated from the cutting of a straight line, the corner of the square path, and the circle path are different from each other. The increase in the signal length to generate features, which will reduce the corner effect, could improve the performance of the developed monitoring system. However, the results suggest that avoiding the complex cutting path for feature generation might be a better strategy for developing a micro milling tool wear monitoring system. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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24 pages, 7447 KiB

Open AccessArticle

A Study on Improving the Machining Performance of Scrolls

by Yi-Tsung Lin, Jia-Lun Jhang, Michael Schabacker, Der-Min Tsay, Guan-Shong Hwang and Bor-Jeng Lin

Appl. Sci. 2023, 13(1), 286; https://doi.org/10.3390/app13010286 - 26 Dec 2022

Viewed by 2269

Abstract

To improve machining efficiency and product precision in scroll manufacturing, we studied three adaptive milling processes: adaptive feed rate planning, chatter suppression measures, and optimization of milling parameters during the rough, semi-fine, and fine machining of scrolls. In the rough machining of scrolls, adaptive feed rate planning was used to compute the cutting area per cutter tooth in real time in order to adjust the feed rate and optimize the material removal rate (MRR) under a given maximum acceptable cutting load. To suppress the possibility of chatter in the semi-fine and fine machining processes, the chatter frequencies were detected with a microphone and the spindle speeds were promptly modified using a developed program in combination with the controller of the milling machine. Based on the Taguchi method and analysis of variance (ANOVA), we determined the optimum milling parameters for the fine machining processes to improve contour characteristics, such as the profile errors and surface roughness of scrolls. Experimental tests were implemented to demonstrate and verify the feasibility of the adaptive milling processes proposed in this study. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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16 pages, 3152 KiB

Open AccessArticle

Research on the Tooth Surface Integrity of Non-Circular Gear WEDM Based on HPSO Optimization SVR

by Jiali Zhao, Qing Wang, Yazhou Wang, Dan Wu, Liang Zhang and Bobo Shen

Appl. Sci. 2022, 12(24), 12858; https://doi.org/10.3390/app122412858 - 14 Dec 2022

Cited by 3 | Viewed by 1407

Abstract

Non-circular gears have the characteristics of gear ratio accuracy, good dynamic performance, and wide application prospects but are difficult to manufacture. Wire electrical discharge machining (WEDM) can process almost all kinds of non-circular gear. In order to solve the problem that the process parameters are mainly adjusted using the operator’s experience and to improve the surface quality of non-circular gears machined using WEDM, this research took Pascal gears processed with a fast-walking WEDM machine as the object, conducted orthogonal tests, and used hybrid particle swarm optimization (HPSO) to optimize support vector regression (SVR) with different kernel functions, to predict various surface integrity indicators. The results showed that the rbf kernel function had a better performance in the prediction model of surface integrity indicators, which can provide a reference for the parameter selection of non-circular gear machining using WEDM. The final predicted results were R² = 0.9978, MAPE = 0.4534 for surface roughness, R² = 0.9483, MAPE = 3.1673 for surface residual stress, and R² = 0.9786, MAPE = 0.4779 for surface microhardness. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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21 pages, 11042 KiB

Open AccessArticle

Estimation and Adjustment of Interface Stiffnesses for Machine-Tool

by Shen-Yung Lin and Guan-Chen Li

Appl. Sci. 2022, 12(23), 12384; https://doi.org/10.3390/app122312384 - 3 Dec 2022

Cited by 3 | Viewed by 1630

Abstract

This study adopts a practical approach to establish the estimation and adjustment methods of the interface stiffness for the machine-tool through the finite element analysis (FEA). First, numerical and experimental modal analysis (FMA and EMA) for each single subsystem of the machine-tool structure are performed. Then, the parameters obtained from EMA are used as the objective criterion function, and the FMA is conducted iteratively to solve the material Young’s modulus and Poisson’s ratio for each single subsystem structure, in which the geometrical model is simplified and FE mesh convergence is performed to ensure the quality and efficiency of the numerical analysis. Next, by considering that the machine-tool is assembled by subsystem stacking and that loading on each contact surface is deduced, the FE method is used to calculate the deformation of each contact interface after the single subsystem is stacked successively. The initial value of the interface stiffness estimated by the deformation formula in the mechanics of material is utilized as the initial condition for the iterative calculation in FMA. The changes of the modal parameters are observed in the analysis and the interface, which have a significant impact on the natural frequency variations of the whole machine-tool is selected as the main adjustment object. Then, the adjustment method proposed in this study is applied repeatedly to modify this interface stiffness. The results show that footing interface stiffnesses play the most important role that intensively affects the numerical analysis results of modal parameter. After the repeated adjustments of the interface stiffnesses of the footing, the error of the natural frequency of the whole machine-tool is less than 5%, which is calculated from the comparison between EMA and FMA results. It indicates that the proposed adjustment method in this study for footing interface stiffness determination has a valuable reference in practical use. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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18 pages, 5465 KiB

Open AccessArticle

Knowledge Management for Injection Molding Defects by a Knowledge Graph

by Zhe-Wei Zhou, Yu-Hung Ting, Wen-Ren Jong and Ming-Chien Chiu

Appl. Sci. 2022, 12(23), 11888; https://doi.org/10.3390/app122311888 - 22 Nov 2022

Cited by 4 | Viewed by 2182

Abstract

Injection molding is a technique with a high knowledge content. However, most of the injection molding knowledge is stored in books, and it is difficult for personnel to clarify the influence of the different factors. This study applies the concept of a knowledge graph by using three types of nodes and edges to express the complex injection molding knowledge in the related literature, and also combines SBERT and search engine building to retrieve the graph. The search engine can follow different search logics, according to the types of nodes, then find the knowledge related to the node, classify it according to the search path, and visualize the search results to the user. Users can clarify the relationship between various process factors and product qualities in a different way. We also use multiple tests to show the actual search results and verify the performance of the search engine. The results show that the search engine can quickly and correctly find the relevant knowledge in the graph, and maintain its performance when the graph is expanded. At the same time, users can clarify the impact of various process factors on the product quality, according to the search results. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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20 pages, 4607 KiB

Open AccessArticle

Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing

by Shih-Ming Wang, Jia-Xuan Wu, Hariyanto Gunawan and Ren-Qi Tu

Appl. Sci. 2022, 12(20), 10324; https://doi.org/10.3390/app122010324 - 13 Oct 2022

Cited by 9 | Viewed by 1688

Abstract

Corner accuracy occurring in wire electrical discharge machining (WEDM) is influenced by machining parameters such as wire vibration, wire lag, and excessive discharge, etc. This study proposed an optimization method which can improve the corner accuracy of the WEDM process. The parameters of pulse-on time (ON), pulse-off time (OFF), open circuit voltage (OV), servo voltage (SV), wire tension (WT), and flushing pressure (WA) were selected to investigate the influences of the major parameters on the machining accuracy in this study. Three different corner angles of 30°, 60°, and 90° were chosen for the verification experiments. The response surface methodology (RSM) was used to analyze and investigate the effect of each parameter on the corner error. After integrating the response surface value and algorithm, an optimization system with a friendly human–machine interface, which has a procedure guiding function, was developed with use of C# language. The system can predict the corner error and also recommend optimal machining parameters for smaller corner error and faster machining speed based on the original machining parameters. Finally, cutting experiments were conducted to verify the proposed system, and the results showed that the proposed method can effectively improve the corner accuracy by 39%, 20%, and 33%. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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Review

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30 pages, 3754 KiB

Open AccessEditor’s ChoiceReview

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

by Alejandro del Real Torres, Doru Stefan Andreiana, Álvaro Ojeda Roldán, Alfonso Hernández Bustos and Luis Enrique Acevedo Galicia

Appl. Sci. 2022, 12(23), 12377; https://doi.org/10.3390/app122312377 - 3 Dec 2022

Cited by 35 | Viewed by 8457

Abstract

In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution. Full article

(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)

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