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Kinetic Mechanical Engineering, 2023, 4(2); doi: 10.38007/KME.2023.040202.

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence


Aras Bozkurt

Corresponding Author:
Aras Bozkurt

Bangladesh University of Engineering and Technology, Bangladesh


With the continuous development of artificial intelligence, more and more researchers begin to attach importance to the realization of perfection testing in products, especially the intelligent and simulation technology. In this paper, based on the combination of computer and robot, the simulation experiment on mechanical structure is carried out. This paper first introduces the important role of machine vision system in the process of defect detection of automobile parts, then expounds the method of feature recognition of automobile parts, related principles and key technical basic knowledge points, etc. Finally, a three-dimensional entity model simulating real products is constructed using MATLAB software, and its results are calculated and analyzed. The concepts of intelligence and simulation are described in detail. The test results show that, the accuracy of the model in detecting the degree of perfection of product parts is more than 90%, and the error rate is low. This shows that the testing performance of the model is excellent.


Artificial Intelligence, Construction Machinery, Product Parts, Degree of Perfection

Cite This Paper

Aras Bozkurt. A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence. Kinetic Mechanical Engineering (2023), Vol. 4, Issue 2: 10-18. https://doi.org/10.38007/KME.2023.040202.


[1] Anubhav Singh, Kavita Saini, Varad Nagar, Vinay Aseri, Mahipal Singh Sankhla, Pritam P. Pandit, Rushikesh L. Chopade:Chapter Sixteen - Artificial intelligence in edge devices. Adv. Comput. 127: 437-484 (2022). 

[2]Ihsan Uluocak, Hakan Yavuz:Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers. Comput. Syst. Sci. Eng. 44(1): 221-234 (2023).

[3] Yupeng Hu, Wenxin Kuang, Zheng Qin, Kenli Li, Jiliang Zhang, Yansong Gao, Wenjia Li, Keqin Li:Artificial Intelligence Security: Threats and Countermeasures. ACM Comput. Surv. 55(2): 20:1-20:36 (2023).

[4]Özlem Özmen Garibay, Brent Winslow, Salvatore Andolina, Margherita Antona, Anja Bodenschatz, Constantinos Coursaris, Gregory Falco, Stephen M. Fiore, Ivan Garibay, Keri Grieman, John C. Havens, Marina Jirotka, Hernisa Kacorri, Waldemar Karwowski, Joseph T. Kider Jr., Joseph A. Konstan, Sean Koon, Monica Lopez-Gonzalez, Iliana Maifeld-Carucci, Sean McGregor, Gavriel Salvendy, Ben Shneiderman, Constantine Stephanidis, Christina Strobel, Carolyn Ten Holter, Wei Xu:Six Human-Centered Artificial Intelligence Grand Challenges. Int. J. Hum. Comput. Interact. 39(3): 391-437 (2023).

[5]Martijn Mes, Eduardo Lalla-Ruiz, Stefan Voß:Special issue on "Artificial Intelligence for Automation in Freight Transport". Int. Trans. Oper. Res. 30(2): 1173-1174 (2023).

[6]Fakir Mashuque Alamgir, Md. Shafiul Alam:An artificial intelligence driven facial emotion recognition system using hybrid deep belief rain optimization. Multim. Tools Appl. 82(2): 2437-2464 (2023).

[7]Alexander Brem, Ferran Giones, Marcel Werle:The AI Digital Revolution in Innovation: A Conceptual Framework of Artificial Intelligence Technologies for the Management of Innovation. IEEE Trans. Engineering Management 70(2): 770-776 (2023).

[8] Yadi Liu, Abdullah A. Al-Atawi, Izaz Ahmad Khan, Neelam Gohar, Qamar Zaman:Using the fuzzy analytical hierarchy process to prioritize the impact of visual communication based on artificial intelligence for long-term learning. Soft Comput. 27(1): 157-168 (2023).

[9]Geetanjali Rathee, Sahil Garg, Georges Kaddoum, Bong Jun Choi, Mohammad Mehedi Hassan, Salman A. AlQahtani:TrustSys: Trusted Decision Making Scheme for Collaborative Artificial Intelligence of Things. IEEE Trans. Ind. Informatics 19(1): 1059-1068 (2023).

[10]Yuerong Su, Weiwei Sun:Classification and interaction of new media instant music video based on deep learning under the background of artificial intelligence. J. Supercomput. 79(1): 214-242 (2023).

[11]Yannick Meneceur, Clementina Barbaro: Artificial intelligence and the judicial memory: the great misunderstanding. AI Ethics 2(2): 269-275 (2022).

[12]Petar Radanliev, David De Roure, Carsten Maple, Uchenna Ani:Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X. AI Ethics 2(4): 623-630 (2022).

[13]Nitesh Rai:Why ethical audit matters in artificial intelligence? AI Ethics 2(1): 209-218 (2022).

[14] Inga Strümke, Marija Slavkovik, Vince Istvan Madai:The social dilemma in artificial intelligence development and why we have to solve it. AI Ethics 2(4): 655-665 (2022)

[15]Daniel Vale, Ali El-Sharif, Muhammed Ali:Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law. AI Ethics 2(4): 815-826 (2022).

[16]Michal Araszkiewicz, Trevor J. M. Bench-Capon, Enrico Francesconi, Marc Lauritsen, Antonino Rotolo:Thirty years of Artificial Intelligence and Law: overviews. Artif. Intell. Law 30(4): 593-610 (2022).

[17]Guido Governatori, Trevor J. M. Bench-Capon, Bart Verheij, Michal Araszkiewicz, Enrico Francesconi, Matthias Grabmair:Thirty years of Artificial Intelligence and Law: the first decade. Artif. Intell. Law 30(4): 481-519 (2022).

[18]Stanley Greenstein:Preserving the rule of law in the era of artificial intelligence (AI). Artif. Intell. Law 30(3): 291-323 (2022).