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      Exploration of Laser Marking Path and Algorithm Based on Intelligent Computing and Internet of Things

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          Abstract

          Nowadays, laser processing technology is being used more and more in various fields, and the requirements for laser control procedures are getting higher and higher. This paper aims to study the path generation problem of laser marking technology in order to improve the efficiency of laser marking as well as the protection of the marking material. Therefore, we creatively propose two-path generation methods, namely, sawtooth parallel and contour parallel, and design the boundary curve offset algorithm and domain partition intersection algorithm for the computer simulation of the two marking paths, respectively. Through the simulation, we discussed the efficiency and marking quality of the two path generation methods and gave the conclusion that the efficiency of the sawtooth parallel path generation method is greater than that of the contour parallel path generation method under specific parameters.

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          A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era

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            Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

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              Equidistant path generation for improving scanning efficiency in layered manufacturing

              Path generation is an important factor that affects the quality and efficiency of most laminated manufacturing processes such as SLS, SLA and FDM. This paper introduces an efficient path generation algorithm. The principle of the algorithm and its implementation are presented. A comparative study is used to analyze the effectiveness of this method. The results of comparison on both path length and processing time between the traditional method and the proposed method are discussed.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                24 June 2022
                : 2022
                : 7443410
                Affiliations
                1Department of Electrical Engineering & Information Technology, Shandong University of Science and Technology, Jinan, Shandong, China
                2Swinburne College, Shandong University of Science and Technology, Jinan, Shandong, China
                Author notes

                Academic Editor: Arpit Bhardwaj

                Author information
                https://orcid.org/0000-0003-1291-4099
                Article
                10.1155/2022/7443410
                9249445
                03bed594-4136-4b45-a791-0f013eb7bd75
                Copyright © 2022 Gang Lu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 April 2022
                : 31 May 2022
                Categories
                Research Article

                Neurosciences
                Neurosciences

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