Optimizing Cycle Time of Industrial Robot for Loading Molding Machine: A Comprehensive Analysis and Optimization Approach

Abstract

In the highly competitive manufacturing industry, optimizing the cycle time of industrial robots plays a crucial role in enhancing production efficiency and maximizing profitability. This paper presents a comprehensive analysis and optimization approach for reducing the cycle time of industrial robots specifically used for loading molding machines. The study begins by investigating the key factors that affect the cycle time, including robot movement, tool selection, part handling, and machine setup. Through in-depth analysis and empirical data collection, the paper identifies the critical bottlenecks and inefficiencies that contribute to prolonged cycle times. Based on the identified issues, a systematic optimization approach is proposed to streamline the robot loading process. This approach encompasses various strategies, such as optimizing robot trajectories, implementing intelligent part recognition systems, improving gripper designs, and leveraging advanced machine learning algorithms for real-time decision- making. The paper explores the benefits and implementation challenges associated with each strategy. Furthermore, the study discusses the integration of simulation models and digital twin technologies to predict and optimize the cycle time in a virtual environment. To validate the effectiveness of the proposed optimization approach, experimental evaluations are conducted using a real-world industrial setup same as the one implemented in the manufacturing. The results demonstrate significant cycle time reductions while maintaining high product quality and ensuring worker safety.