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Micromachines
Special Issues
Machine Learning for Advanced Manufacturing
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Machine Learning for Advanced Manufacturing

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 29578

Special Issue Editor

Dr. Tsz Ho Kwok

E-Mail Website
Guest Editor
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
Interests: additive manufacturing; 4D printing; polymer; design for additive manufacturing; topology optimization; generative design; lattice structure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of advanced manufacturing (AM) technologies, it is possible to rapidly fabricate complex physical objects in various scales. To monitor and control the manufacturing processes, there are different internal and external sensors producing numerous data in regard to the conditions of the machines. In recent decades, machine learning (ML) has been proved a suitable tool for analyzing large and complex datasets. Therefore, it is unsurprising that ML methods have been introduced for process planning and control. Smart manufacturing, i.e., Industry 4.0, refers to the manufacturing paradigm that makes use of sensors, cloud computing, machine learning, additive manufacturing, and/or advanced robotics to improve manufacturing productivity and cost efficiency. ML serves an important and necessary role in AM systems. Fundamental studies in ML will lead us to create more innovations in smart manufacturing and expand the manufacturing sectors. The objective of this Special Issue is to collect cutting-edge research works focused on the development of ML-based methods for AM. Specific topics of interest include but are not limited to the following:

  • ML-based product design and development;
  • Data-driven process planning and control;
  • Customization and personalization;
  • Real-time monitoring and decision making;
  • Manufacturing intelligence and informatics;
  • ML-based topology optimization;
  • Geometric deep learning methods for design and fabrication in AM.

Dr. Tsz Kwok
Guest Editor

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. Micromachines is an international peer-reviewed open access monthly 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 2600 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

  • machine learning
  • Industry 4.0
  • smart manufacturing
  • advanced manufacturing
  • computer-integrated manufacturing

Published Papers (7 papers)

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Research

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19 pages, 4665 KiB  
Article
Artificial Intelligence-Based Smart Quality Inspection for Manufacturing
by Sarvesh Sundaram and Abe Zeid
Micromachines 2023, 14(3), 570; https://doi.org/10.3390/mi14030570 - 27 Feb 2023
Cited by 24 | Viewed by 10870
Abstract
In today’s era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of [...] Read more.
In today’s era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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20 pages, 8586 KiB  
Article
Automatic Bounding Box Annotation with Small Training Datasets for Industrial Manufacturing
by Manuela Geiß, Raphael Wagner, Martin Baresch, Josef Steiner and Michael Zwick
Micromachines 2023, 14(2), 442; https://doi.org/10.3390/mi14020442 - 13 Feb 2023
Cited by 3 | Viewed by 3294
Abstract
In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly [...] Read more.
In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object’s label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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21 pages, 5000 KiB  
Article
Weighted Matrix Decomposition for Small Surface Defect Detection
by Zhiyan Zhong, Hongxin Wang and Dan Xiang
Micromachines 2023, 14(1), 92; https://doi.org/10.3390/mi14010092 - 29 Dec 2022
Cited by 1 | Viewed by 1375
Abstract
Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes [...] Read more.
Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes and extremely weak feature representation. To address the above issue, this paper presents a weighted matrix decomposition model (WMD) for small defect detection against a complex surface. Firstly, a weighted matrix is constructed based on texture characteristics of RGB channels in the defect image, which aims to improve contrast between defects and the background. Based on the sparse and low-rank characteristics of small defects, the weighted matrix is then decomposed into low-rank and sparse matrices corresponding to the redundant background and defect areas, respectively. Finally, an automatic threshold segmentation method is used to obtain the optimal threshold and accurately segment the defect areas and their edges in the sparse matrix. The experimental results show that the proposed model outperforms state-of-the-art methods under various quantitative evaluation metrics and has broad industrial application prospects. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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15 pages, 6312 KiB  
Article
Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning
by Rajalakshmi Ratnavel, Shreya Viswanath, Jeyanthi Subramanian, Vinoth Kumar Selvaraj, Valarmathi Prahasam and Sanjay Siddharth
Micromachines 2022, 13(12), 2231; https://doi.org/10.3390/mi13122231 - 16 Dec 2022
Cited by 2 | Viewed by 1912
Abstract
3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to [...] Read more.
3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to thoroughly address them, like stringing, over-extrusion, layer shifting, and overheating. This paper proposes a study using machine learning to identify the optimal process parameters such as infill structure and density, material (ABS, PLA, Nylon, PVA, and PETG), wall and layer thickness, count, and temperature. The result thus obtained was used to train a machine learning algorithm. Four different network architectures (CNN, Resnet152, MobileNet, and Inception V3) were used to build the algorithm. The algorithm was able to predict the parameters for a given requirement. It was also able to detect any errors. The algorithm was trained to pause the print immediately in case of a mistake. Upon comparison, it was found that the algorithm built with Inception V3 achieved the best accuracy of 97%. The applications include saving the material from being wasted due to print time errors in the manufacturing industry. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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20 pages, 6176 KiB  
Article
CFD Analysis and Optimum Design for a Centrifugal Pump Using an Effectively Artificial Intelligent Algorithm
by Chia-Nan Wang, Fu-Chiang Yang, Van Thanh Tien Nguyen and Nhut T. M. Vo
Micromachines 2022, 13(8), 1208; https://doi.org/10.3390/mi13081208 - 29 Jul 2022
Cited by 82 | Viewed by 6985
Abstract
In this study, we proposed a novel approach to improve centrifugal pump performance with regard to the pump head, pump efficiency, and power. Firstly, to establish constraints, an optimal numerical model that accounted for factors such as pump efficiency and the head was [...] Read more.
In this study, we proposed a novel approach to improve centrifugal pump performance with regard to the pump head, pump efficiency, and power. Firstly, to establish constraints, an optimal numerical model that accounted for factors such as pump efficiency and the head was considered. The pump was designed, and an artificial intelligence algorithmic approach was applied to the pump before performing experiments. We considered a set of models by selecting the parameters of the centrifugal pump casing section area, the interference of the impeller, the volute tongue length, and the volute tongue angle. The weights of the factors of safety and displacement on the optimization indices were estimated. The matrix of the weights for the optimal process was less than 38% or greater than 62%. This approach guarantees a complicated multi-objective optimization problem. The results show that the centrifugal pump performances were improved. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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12 pages, 3710 KiB  
Article
Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring
by Wei Sun, Jie Zhou, Bintao Sun, Yuqing Zhou and Yongying Jiang
Micromachines 2022, 13(6), 873; https://doi.org/10.3390/mi13060873 - 31 May 2022
Cited by 10 | Viewed by 1881
Abstract
Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available [...] Read more.
Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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Review

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25 pages, 5686 KiB  
Review
Analysis of Current Situation, Demand and Development Trend of Casting Grinding Technology
by Haigang Liang and Jinwei Qiao
Micromachines 2022, 13(10), 1577; https://doi.org/10.3390/mi13101577 - 22 Sep 2022
Cited by 5 | Viewed by 1984
Abstract
Although grinding is essential in the production of castings, the casting grinding process in manufacturing is complicated and there are many difficulties, such as the large amount of noise in the grinding environment, non-structural casting entities, and the inclination in the overall shape–time [...] Read more.
Although grinding is essential in the production of castings, the casting grinding process in manufacturing is complicated and there are many difficulties, such as the large amount of noise in the grinding environment, non-structural casting entities, and the inclination in the overall shape–time variation. Even in the face of complex technology and a variety of difficulties, modern casting grinding technology still demands large-batch production, low cost, fast response, thin brittleness, high precision, etc. The grinding process has a long history. This paper focus on its development from a human-operated, mechanical job, to an automatic grinding task based on compliant control theory. However, the methods mentioned above can no longer satisfy the current production need. In recent years, researchers have proposed intelligent grinding methods to meet the needs of modern casting production, and provided various strategies and alternatives to the challenges of machining accuracy, machining efficiency, and surface consistency. The research direction of casting polishing has mainly focused on online robot detection, material removal prediction, constant grinding contact force control, and high-precision matching. Although applications for online detection and constant grinding contact force control exist in industry, there are challenges in material removal prediction and three-dimensional high-precision matching. This paper also compares and analyzes the advantages and disadvantages of different grinding methods, and puts forward some research directions for future work, so as to promote more intelligent and efficient grinding of complex castings in practical application. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Manufacturing)
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