Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

Abstract

In the context of intelligent manufacturing, machine tools, as core equipment, directly influence production efficiency and product quality through their operational reliability. Traditional maintenance methods for machine tools, often characterized by low efficiency and high costs, fail to meet the demands of modern manufacturing industries. Therefore, leveraging intelligent manufacturing technologies, this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults. Initially, the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools. Subsequently, it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation, thereby enhancing maintenance efficiency and reducing costs. Lastly, the paper explores the architectural design, integration, and testing evaluation methods of intelligent manufacturing systems. The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs, offering broad application prospects.