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Machines
Special Issues
Smart Processes for Machines, Maintenance and Manufacturing Processes
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Smart Processes for Machines, Maintenance and Manufacturing Processes

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 4904

Special Issue Editors

Dr. Binayak Bhandari

E-Mail Website
Guest Editor
School of Mechanical and Manufacturing, University of New South Wales, Sydney, Australia
Interests: advanced machining; advanced composites; computer vision, artificial intelligence; design and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Zhou

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: intelligent manufacturing; industrial knowledge graph; quality control and root cause analysis; digital twin
Prof. Dr. Jinsong Bao

E-Mail Website
Guest Editor
College of Mechanical Engineering, University of Donghua, Shanghai 201620,China
Interests: Industry 4.0; intelligent manufacturing system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine intelligence, maintenance and manufacturing processes have become some of the important directions for the development of the manufacturing industry.

In this Special Issue, we focus on smart processes for machines, maintenance and manufacturing processes. We are seeking papers on all kinds of engineering designs assisted by artificial intelligence (AI) and computer vision/digital twins/knowledge graph, with a clear contribution to the advancement in the discipline. The research work can include, but is not limited to, any branch of mechanical design, maintenance, self-optimizing manufacturing process, 3D generation, micro electro mechanical systems (MEMS) and optical measurements.

Original research papers, review articles and short communications are all welcome.

Possible topics include, but are not limited to, the following:

  • Computer vision (computer vision in equipment monitoring).
  • Mechanical design.
  • Machine learning/deep learning.
  • Precision manufacturing.
  • Artificial intelligence in manufacturing processes.
  • Smart maintenance method based on knowledge analytics.
  • Digital twin-driven method for equipment health status assessment and fault prediction.
  • Knowledge graph-driven quality control and analysis for equipment operation and maintenance.

Dr. Binayak Bhandari
Dr. Bin Zhou
Prof. Dr. Jinsong Bao
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. Machines 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 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

  • digital twins
  • smart manufacturing
  • artificial intelligence (AI)
  • augmented reality (AR)
  • additive manufacturing
  • cloud and edge computing in manufacturing
  • structural health monitoring
  • robotics and automation
  • predictive/ preventive maintenance
  • autonomous systems
  • quality control and root cause analysis
  • sustainable manufacturing
  • advanced manufacturing
  • industrial knowledge graph
  • cognitive manufacturing

Published Papers (4 papers)

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Research

12 pages, 5459 KiB  
Article
Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance
by Binayak Bhandari and Prakash Manandhar
Machines 2023, 11(12), 1083; https://doi.org/10.3390/machines11121083 - 12 Dec 2023
Cited by 1 | Viewed by 1237
Abstract
This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) [...] Read more.
This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) software using computer vision. Two sets of experiments were performed: one with a simple cubical object and the other with a more complex geometry that needed photogrammetry to redraw it in the CAD system. For the accurate positioning of the object, a robotic hand was used. An Internet of Things (IoT) based camera unit was used for capturing the image and wirelessly transmitting it over the network. Computer vision algorithms such as GrabCut, Canny edge detector, and morphological operations were used for extracting border points of the input. The coordinates of the vertices of the solids were then transferred to the Computer-Aided Design (CAD) software via a macro to clean and generate the border curve. Finally, a 3D solid model is generated by linear extrusion based on the curve generated in CATIA. The results showed excellent regeneration of an object. This research makes two significant contributions. Firstly, it introduces an integrated system designed to achieve precise dimension extraction from solid objects. Secondly, it presents a method for regenerating intricate 3D solids with consistent cross-sections. The proposed system holds promise for a wide range of applications, including automatic 3D object reconstruction and quality assurance of 3D-printed objects, addressing potential defects arising from factors such as shrinkage and calibration, all with minimal user intervention. Full article
(This article belongs to the Special Issue Smart Processes for Machines, Maintenance and Manufacturing Processes)
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21 pages, 4830 KiB  
Article
Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms
by Chiu-Hung Chen
Machines 2023, 11(11), 1018; https://doi.org/10.3390/machines11111018 - 11 Nov 2023
Viewed by 851
Abstract
This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space [...] Read more.
This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain multiple solutions, while a good quality of learning cannot be obtained by a single neural network approach. Therefore, this paper proposes employing Fourier descriptors to represent trajectory curves in a systematic and normalized form, developing a multi-solution distribution evaluation by random restart local searches (MDE-RRLS) to examine a better solution-space partitioning scheme, utilizing multiple DNNs to learn subspace regions separately, and creating a multi-facet query (MFQuery) to cooperatively predict multiple solutions. The experiments demonstrate that the proposed approach can obtain better or at least competitive outcomes compared to previous work in the literature. Furthermore, to verify the effectiveness and applicability, this paper investigates the design problem of an industrial six-linkage-bar ladle mechanism used in a die-casting system, and the proposed method can obtain several superior design solutions and offer alternatives in a short period of time when faced with redesign requirements. Full article
(This article belongs to the Special Issue Smart Processes for Machines, Maintenance and Manufacturing Processes)
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19 pages, 3581 KiB  
Article
Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes
by Guodong Wang, Guohua Liu and Qianqian Li
Machines 2023, 11(11), 1005; https://doi.org/10.3390/machines11111005 - 02 Nov 2023
Viewed by 1124
Abstract
There is a lack of high correlation and reuse potential among multiple manufacturing data for textiles and apparel. Moreover, the material flow traceability between production workstations is not clear, making it difficult to detect potential production bottlenecks. This paper proposes a knowledge graph [...] Read more.
There is a lack of high correlation and reuse potential among multiple manufacturing data for textiles and apparel. Moreover, the material flow traceability between production workstations is not clear, making it difficult to detect potential production bottlenecks. This paper proposes a knowledge graph embedded time serial data-driven bottleneck analysis of textile and apparel production processes. Firstly, a dynamic information association model is established to organize global manufacturing information, including the static data and time-series data features. Also, a textile-corpus-oriented knowledge extraction model is designed to construct a time-series knowledge graph for textile and apparel production (TKG4TA). Then, a temporal knowledge-driven production process bottleneck prediction model is presented based on manufacturing knowledge in the textile and apparel industry. Of these, textile knowledge is transformed into embeddings using a graph convolutional network (GCN). In turn, the context-associated information features are learned by the long short-term memory (LSTM) to predict the bottlenecks in the textile and apparel production process. Finally, a typical process flow in a shirt manufacturing workshop is used as a case study. It shows that the F1 value of the proposed method for named entity recognition and relationship extraction is up to 80.3%, and 50.6%, respectively. The performance of the proposed model for bottleneck prediction is improved by 8.2% and 14.92% compared to only the use of GCN or LSTM in the mean absolute error. This model may provide a solid foundation for the temporal knowledge-graph-driven bottleneck analysis of shirt manufacturing. Full article
(This article belongs to the Special Issue Smart Processes for Machines, Maintenance and Manufacturing Processes)
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25 pages, 6651 KiB  
Article
A Knowledge Graph-Based Approach for Assembly Sequence Recommendations for Wind Turbines
by Mingfei Liu, Bin Zhou, Jie Li, Xinyu Li and Jinsong Bao
Machines 2023, 11(10), 930; https://doi.org/10.3390/machines11100930 - 27 Sep 2023
Cited by 2 | Viewed by 1132
Abstract
There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly [...] Read more.
There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence. In this paper, we propose a knowledge graph-based approach for assembly sequence recommendations for wind turbines. First, for the multimodal data (text in process manual, image of tooling, and three-dimensional (3D) model) of assembly, a multi-process assembly information representation model is established to express assembly elements in a unified way. In addition, knowledge extraction methods for different modal data are designed to construct a multimodal knowledge graph for wind turbine assembly. Further, the retrieval of similar assembly process items based on the bidirectional encoder representation from transformers joint graph-matching network (BERT-GMN) is proposed to predict the assembly sequence subgraphs. Also, a Semantic Web Rule Language (SWRL)-based assembly process items inference method is proposed to automatically generate subassembly sequences by combining component assembly relationships. Then, a multi-objective sequence optimization algorithm for the final assembly is designed to output the optimal assembly sequences. Finally, taking the VEU-15 wind turbine as the object, the effectiveness of the assembly process information modeling and part multi-source information representation is verified. Sequence recommendation results are better quality compared to traditional assembly sequence planning algorithms. It provides a feasible solution for wind turbine assembly to be optimized from multiple objectives simultaneously. Full article
(This article belongs to the Special Issue Smart Processes for Machines, Maintenance and Manufacturing Processes)
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