Data Mining Application in Process Control of Smart Material Manufacturing
Nymphea Saraf Sandhu1, A.K.Upadhyay2, Sanjiv Sharma3
1Nymphea Saraf Sandhu, Pursuing PHD, Ast, Amity University, Gwalior, India.
2Dr. A. K Upadhyay, Professor, ASET, Amity University, Gwalior, India.
3Dr. Sanjiv Sharma, Assistant professor in Department of Computer science Engineering and Information Technology in Madhav Institute of Technology and Science, Gwalior, Indian.

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4999-5005 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6860018520/2020©BEIESP | DOI: 10.35940/ijrte.E6860.018520

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Abstract: According to Industry 4.0 concept, huge amounts of data is generated in Smart Material Manufacturing and this data needs to be collated, stored, organised and analysed in order to develop a more efficient manufacturing system. This study focuses on the prediction of smart material manufacturing process, based on the current production data. It presents a route of knowledge gain about predicting future manufacturing systems, using data mining. The model proposed for the actual manufacturing process is made to acquire the necessary data for process control. Implementing particular methods of data mining, and by altering the input parameters, we can predict the behaviour of the manufacturing processes. This prediction is then verified by the use of a simulation model. After analysing various methods, the method using neural networks is chosen for deployment of the latest data in the concluding phases. This research aims at designing and verifying the tools for mining data for supporting system control in manufacturing. It aims at improvement of the process of decision making. The practical control strategies can be accurately modified, depending on the predictions made and the targeted results of production. These strategies can then be used in real time manufacturing, without a chance of failure.
Keywords: Smart Material Manufacturing, Simulation, Forecasting, Data Mining, Decision Support, Process Control.
Scope of the Article: Data Mining.