As the energy cost accounts for about one-third of the total manufacturing cost, there is great significance in evaluating and managing energy consumption in manufacturing processes. The energy consumption during multi-axis end milling, which represents a large part of the industrial energy costs, is usually extraordinarily large, especially for complex free-form surfaces requiring multi-finish-machining. To obtain the most efficient tool path, the tool orientation is adjusted to obtain the largest cutting stripe width at each cutter contact point. However, the use of excessive driving energy consumption and cutting energy to obtain the largest cutting stripe width may reduce the energy efficiency of the tool path. To solve this problem, the geometry features of the tool path are analyzed firstly, and the global energy consumption analysis, which includes a cutting energy analysis and driving energy analysis, is conducted. The discrete energy consumption path model is constructed to find the most energy-efficient tool orientation sequence for a tool path. Finally, contrast experiments are carried out to validate the proposed method.

Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

The continuous static contraction of the upper limb during overhead work often leads to progressive muscle fatigue, which is the main cause of work-related musculoskeletal disorders (WMSDs) and reduced operational safety. Traditional fatigue detection methods cannot capture subtle early fatigue characteristics under dynamic working conditions, and sensors are difficult to conveniently deploy in harsh environments. This study constructs an upper limb fatigue detection model based on wearable interface pressure signals and proposes a spatiotemporal dual-entropy fusion strategy. Sample entropy evaluates temporal muscle movement regularity, with a 10% incremental rate-of-change threshold for significant fatigue identification, superior fatigue sensitivity, and anti-interference capability. Information entropy reflects the spatial dispersion of pressure amplitude, adopting a 5% rate-of-change threshold for early fatigue warning. Combined with sEMG comparative verification and nonlinear fitting analysis, a hierarchical monitoring framework is established: single-index abnormality indicates slight fatigue, while dual-entropy synchronous elevation represents severe neuromuscular fatigue. The proposed method overcomes the limitations of single-index evaluation, accurately identifies multiple fatigue levels, and reliably monitors fatigue in real time, providing effective technical support for ergonomic monitoring and occupational safety protection in overhead operations.