Research on PTC Cable Materials Based on Principal Component Analysis and Quantitative Correspondence Factor Analysis Method in Big Data Technology

2nd International Conference on Frontiers of Materials Synthesis and Processing

Wang, Meimei^1

School of Tianjin University of Technology, Tianjin, China^1

关键词: Analysis techniques;    Artificial neural network models;    Data technologies;    Design performance;    Formulation process;    Industrial production;    Qualitative and quantitative analysis;    Quantitative correspondence;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/493/1/012084/pdf
DOI  :  10.1088/1757-899X/493/1/012084

来源: IOP

【 摘 要 】

To analyse the huge production data scientifically, the records of enterprises were used. Principal component analysis techniques and quantitative corresponding factor analysis techniques in big data technology were applied. The primary and secondary factors affecting the design performance of PTC cable materials and the influence laws were found. Through analysis, the best process recipe conditions in the existing data were obtained. The results showed that the optimization of the PTC cable material formulation process effectively guided industrial production and met different practical needs. In summary, multi-factor and multi-level visual design and analysis methods, artificial neural network models and big data technology have good qualitative and quantitative analysis functions. A complex process optimization problem with four influencing factors and three indicators is solved.