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Machines
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Smart Manufacturing and Industrial Automation
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Smart Manufacturing and Industrial Automation

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5347

Special Issue Editor

Prof. Dr. Nand Jha

E-Mail Website
Guest Editor
Mechanical Engineering Department, Manhattan College, New York, NY 10471, USA
Interests: robotics; automation; manufacturing sciences; smart manufacturing; ML and AI in manufacturing; data analysis in smart manufacturing

Special Issue Information

Dear Colleagues,

The international Journal of Machines is publishing the Special Issue of Smart Manufacturing. This Special Issue is inviting submissions on multidisciplinary approaches toward smart manufacturing. We welcome theory on the development of digitization in manufacturing processes, including ML and AI in manufacturing.

Industrial automation via use of the Internet of Things (IOT) has become an incredibly popular topic in manufacturing. Automation is driving positive change in the customer experience, offering convenience, speed, and quality service. The topic is contemporary and industrially relevant.

The aim and objective of smart manufacturing is to provide low-cost solutions for end-to-end visibility in terms of manufacturing processes. The manufacturing industry is changing rapidly with the application of industrial automation, ML and AI. This may be an opportune time to discuss these issues under the broad umbrella of Smart Manufacturing. Smart manufacturing is a flexible and software-driven IoT platform that can be applied in any location. The authors are encouraged to select any topic of their choice from the list of options below.

  • Review Smart Manufacturing: researches, industry, and future of smart manufacturing
  • Digitization in Manufacturing Automation
  • AI and ML in Manufacturing
  • Industrial Automation and Industry 4.0
  • Industrial Internet of things (IIOT) and practice
  • Sensors and its design for Smart Manufacturing
  • Industrial Automation and Robotics for Industry 4.0
  • Automation, IIOT, and Sustainability
  • Data Acquisition and data analysis in Smart Manufacturing
  • Growing cyber-security for smart manufacturing
  • Industry 4.0, fog, and cloud computing
  • Software for Industry 4.0 and smart manufacturing

Prof. Dr. Nand Jha
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. 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.

Published Papers (3 papers)

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Research

33 pages, 11734 KiB  
Article
Optimization Method of Sheet Metal Laser Cutting Process Parameters under Heat Influence
by Yeda Wang, Xiaoping Liao, Juan Lu and Junyan Ma
Machines 2024, 12(3), 206; https://doi.org/10.3390/machines12030206 - 20 Mar 2024
Viewed by 677
Abstract
To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method [...] Read more.
To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model Tpr,t is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model Tpr,t, the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation)
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20 pages, 27582 KiB  
Article
Sim-to-Real Dataset of Industrial Metal Objects
by Peter De Roovere, Steven Moonen, Nick Michiels and Francis wyffels
Machines 2024, 12(2), 99; https://doi.org/10.3390/machines12020099 - 01 Feb 2024
Viewed by 941
Abstract
We present a diverse dataset of industrial metal objects with unique characteristics such as symmetry, texturelessness, and high reflectiveness. These features introduce challenging conditions that are not captured in existing datasets. Our dataset comprises both real-world and synthetic multi-view RGB images with 6D [...] Read more.
We present a diverse dataset of industrial metal objects with unique characteristics such as symmetry, texturelessness, and high reflectiveness. These features introduce challenging conditions that are not captured in existing datasets. Our dataset comprises both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data were obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions, and lighting conditions. This resulted in over 30,000 real-world images. We introduce a new public tool that enables the quick annotation of 6D object pose labels in multi-view images. This tool was used to provide 6D object pose labels for all real-world images. Synthetic data were generated by carefully simulating real-world conditions and varying them in a controlled and realistic way. This resulted in over 500,000 synthetic images. The close correspondence between synthetic and real-world data and controlled variations will facilitate sim-to-real research. Our focus on industrial conditions and objects will facilitate research on computer vision tasks, such as 6D object pose estimation, which are relevant for many industrial applications, such as machine tending. The dataset and accompanying resources are available on the project website. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation)
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20 pages, 13342 KiB  
Article
Integration of Deep Learning for Automatic Recognition of 2D Engineering Drawings
by Yi-Hsin Lin, Yu-Hung Ting, Yi-Cyun Huang, Kai-Lun Cheng and Wen-Ren Jong
Machines 2023, 11(8), 802; https://doi.org/10.3390/machines11080802 - 04 Aug 2023
Cited by 4 | Viewed by 3281
Abstract
In an environment where manufacturing precision requirements are increasing, complete project plans can consist of hundreds of engineering drawings. The presentation of these drawings often varies based on personal preferences, leading to inconsistencies in format and symbols. The lack of standardization in these [...] Read more.
In an environment where manufacturing precision requirements are increasing, complete project plans can consist of hundreds of engineering drawings. The presentation of these drawings often varies based on personal preferences, leading to inconsistencies in format and symbols. The lack of standardization in these aspects can result in inconsistent interpretations during subsequent analysis. Therefore, proper annotation of engineering drawings is crucial as it determines product quality, subsequent inspections, and processing costs. To reduce the time and cost associated with interpreting and analyzing drawings, as well as to minimize human errors in judgment, we developed an engineering drawing recognition system. This study employs geometric dimensioning and tolerancing (GD&T) in accordance with the ASME (American Society of Mechanical Engineers) Y14.5 2018 specification to describe the language of engineering drawings. Additionally, PyTorch, OpenCV, and You Only Look Once (YOLO) are utilized for training. Existing 2D engineering drawings serve as the training data, and image segmentation is performed to identify objects such as dimensions, tolerances, functional frames, and geometric symbols in the drawings using the network model. By reading the coordinates corresponding to each object, the correct values are displayed. Real-world cases are utilized to train the model with multiple engineering drawings containing mixed features, resulting in recognition capabilities surpassing those of single-feature identification. This approach improves the recognition accuracy of deep learning models and makes engineering drawing and image recognition more practical. The recognition results are directly stored in a database, reducing product verification time and preventing errors that may occur due to manual data entry, thereby avoiding subsequent quality control issues. The accuracy rates achieved are as follows: 85% accuracy in detecting views in 2D engineering drawings, 70% accuracy in detecting annotation groups and annotations, and 80% accuracy in text and symbol recognition. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation)
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