Research on the Capability Maturity Evaluation of Intelligent Manufacturing Based on Firefly Algorithm, Sparrow Search Algorithm, and BP Neural Network

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Authors: Shi, Li, Ding, Xuehong, Li, Min, Liu, Yuan
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Back propagation networks; Batteries; Bottlenecks; Evaluation; Factories; Food; Heuristic methods; Intelligent manufacturing systems; Manufacturing; Methods; Neural networks; Operations management; Optimization; Questionnaires; Search algorithms; Stability analysis; Thresholds; China
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2021/5554215

Intelligent manufacturing capability evaluation is the key for enterprises to scientifically formulate the implementation path and continuously improve the level of intelligent manufacturing. To help manufacturing enterprises diagnose the level of intelligent manufacturing capability, this paper conducts research on intelligent manufacturing capability maturity evaluation based on maturity theory. The evaluation problem is a complex nonlinear problem, and BP neural network is particularly suitable for solving such complex mapping problems. Aiming at the problem that the BP neural network is sensitive to initial weights and thresholds, the sparrow search algorithm (SSA) is used to optimize the initial weights and thresholds of the BP neural network. In order to overcome the shortcoming of SSA that it is easy to fall into the local optimum, the firefly disturbance strategy is introduced to improve it, a new sparrow search algorithm (FASSA) is proposed, and on this basis, an intelligent manufacturing capability maturity evaluation model based on the FASSA-BP algorithm is constructed. Finally, a large battery manufacturing enterprise in China is selected for empirical research, and the comparison experiments are carried out on the FASSA-BP model, BP model, SSA-BP model, and PSO-BP model in terms of accuracy, stability, etc. The results show that the evaluation of intelligent manufacturing capability maturity through this model can effectively help companies diagnose problems in the construction of intelligent manufacturing and provide a reference for companies to accurately improve their intelligent manufacturing capabilities.