Multi-Objective Optimization of Manufacturing Process in Carbon Fiber Industry Using Artificial Intelligence Techniques

Abstract

Seeking high profitability by improving energy efficiency and production quality is the prime goal of manufacturing industries. However, achieving this aim involves the realization of several conflicting objectives. In carbon fiber industry, the stabilization process is the most vital step with high energy consumption. The aim of this study is to use intelligent modeling methods in the stabilization process to maximize energy efficiency while considering better production quality, avoiding defects, and not scarifying the prediction accuracy. To this aim, a modified DOE method was used to reduce the number of required experiments. The mechanical and physical properties were then modeled based on input-output data derived from the experiments. In this way, the SVR method is used to develop a set of mathematical models for mechanical and physical properties of the fibers. The skin-core defect and energy consumption were considered as objective functions within the given range of physical and mechanical properties of fibers. The state-of-the-art NSGA-II algorithm used to find the optimum Pareto front, including non-dominated solutions among these conflicting objective functions. The results showed that by using the integrated NSGA-II and technique for order preference by similarity to ideal solution (TOPSIS), the energy efficiency of the system was improved. Moreover, the discussions showed how similar hybrid algorithms with high accuracy can be used by other industries to reduce the overall energy consumptions.

Keywords

• Predictive models
• manufacturing processes
• multi-objective optimization
• thermal stabilization
• artificial intelligence
• energy efficiency