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International Journal of Soft Computing And Artificial Intelligence (IJSCAI)-IJSCAI
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Volume Issue
Volume-9,Issue-1  ( May, 2021 )
Paper Title
Laser Application and Artificial Intelligence Utilization in Production Quality Management: Piping Fabrication
Author Name
Engineering Management Program, ISTANBUL GEDÄ°K UNIVERSITY
The development of quality control systems in production is one of the matters of concern to producers and quality control institutions alike. The pipe industry is considered one of the important industries because of its wide and sensitive applications in the infrastructure of projects, so limiting quality control to human capabilities only is considered a weakness in these systems. The benefit of the applications of artificial intelligence in quality control has been used by many producers, despite the difficulties that accompany the use of these techniques, as they require training and a special work environment. Deep neural networks were used in this work to monitor the quality of tubes and classify them according to the criteria that were determined.One of the things that has been focused on is to reduce the time and data needed to train the deep game network, by relying on the technology of transfer learning. Transfer learning is one of the ways to train deep neural networks that have been previously trained, which saves time and data for training and allows the user to use high-resolution models in the design and make some modifications to them, making them commensurate with the nature of the application he wants to design. Alexnet is used in this work because it has high accuracy in classification of images. Alexnet contains 25 layers, only three layers were modified to be suitable with our application. About 1000 images only used in training and validation processes. The simulation results shows that the transfer learning is accepted technique and is also save time with high accuracy results. Keywords - Quality, Pipes, Monitoring, Transfer Learning, Deep Neural Networks, Alexnet, Training, Validation.
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