Real-time parameter optimization based on neural network for smart injection molding

2017 5th International Conference on Mechanical Engineering, Materials Science and Civil Engineering

机械制造;材料科学;土木建筑工程

Lee, H.^1 ; Liau, Y.^1 ; Ryu, K.^1

Department of Industrial Engineering, Pusan National University, Busan, Korea, Republic of^1

关键词: Communication technologies;    Injection molding process;    Manufacturing industries;    Manufacturing process;    Optimal parameter settings;    Parameter optimization;    Quality of production;    Real-time process parameters;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/324/1/012076/pdf
DOI  :  10.1088/1757-899X/324/1/012076

来源: IOP

【 摘 要 】

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.