A collaborative design platform for new alloy material development

To overcome the shortcomings of the conventional trial and error mode for new material development, a full-process collaborative design platform for steel rolling is developed based on an industrial internet of things (IIoT) system in this study. Equipment, process and product entities are modeled in both the physical domain and the cyber domain. A systematic data-driven Mamdani-type fuzzy modeling methodology is proposed to map the relationship between material chemical compositions, organizational structures, process parameters and mechanical performances. The proposed methodology employs a random forest (RF) algorithm to select important parameters from mechanism models, simulation models and production process variables, utilizes a K-means algorithm to merge diverse steel grades into sub-clusters, and implements a multi-objective particle swarm optimization (MOPSO) algorithm to further improve the fuzzy model in terms of both the structure and the membership function parameters. A dataset of 3500 steel coils collected by the prototype platform built in a large hot rolling mill is used to evaluate the performance of the proposed approach. Experiment results show that the proposed methodology performs well in predicting the yield strength, tensile strength and elongation, with the coverage probability over 90% under 10% deviation and about 70% under 5% deviation on average.