AI Summary & Key Takeaways
AI InsightThe results demonstrated that the developed software is capable of making high ratio first quality fabric decision within seconds.
📌 3 Key Takeaways
Abstract
Development of loom technology has significantly increased the efficiency of fabric output in the textile industry. Additionally, preventing the occurrence of defects during the manufacturing process on the fabric is not easy. Therefore, after the production completed, the aim is deciding the cutting location of the product, which has the error map, to increase the first quality product quantity by considering the customer quality parameters. In this article, a decision support system has been developed to help the inspector in the final stage which will also prevent losses. The first of the proposed two algorithms is Simulated Annealing algorithm, which is well known and rendered good results for the different type of problems in the literature, and the other is the K-means method which is frequently used in clustering. In the study, a sample problem is used to explain the adaptation of algorithms to the problem, the results of methods are compared, and design of the experiment is deployed to obtain the best parameter values for the selected algorithm. Finally, the software, which is prepared to use the algorithm in the real production environment, is introduced and the results of the performance analysis are evaluated. The results demonstrated that the developed software is capable of making high ratio first quality fabric decision within seconds.