A flexible manufacturing system (FMS) improves productivity and makes it more efficient. Maintaining reliability levels and reducing costs through proper maintenance strategies are key problems for the development and application of a FMS. This paper proposes a grouping preventive maintenance strategy of a FMS with optimized parameters by considering both reliability and cost. In this work, a three-layer evaluation index system is first presented to accurately estimate the reliability of the FMS; index weights of each layer were obtained by reliability importance modeling and analysis, considering maintenance strategies. An element-grouping preventive strategy is proposed based on an influencing analysis, and a parameter optimization problem (considering reliability and maintenance costs) was established. In this strategy, three maintenance methods are presented for the elements, including low-level maintenance with a large period, low-level maintenance with a small period, as well as the combination of low-level maintenance with a small period and high-level maintenance with a large period; the effects of reliability improvement of the elements on the subsystem’s reliability were analyzed to provide evidence for element grouping. Finally, the proposed method was applied to a box-part finishing FMS; the results indicate that this method can effectively reduce maintenance costs on the premise of satisfying the reliability requirements.

Tolerance allocation optimization (TAO) is a crucial step in the manufacturing process of the portal milling machine (PMM). Traditional design approaches often consider only the machining errors arising from geometric errors. However, the thermal characteristics of the bi-rotary milling head (BRMH), which significantly machine tool accuracy, are frequently overlooked. Hence, a novel TAO model of PMM is proposed, considering the thermal characteristics of BRMH across multiple operating conditions. Firstly, based on the multi-body system (MBS) theory and homogeneous transform matrix (HTM), a mapping function relating geometric errors to tolerance and volume errors is developed. Secondly, the heat generation and dissipation mechanisms of the BRMH are analyzed, and precise boundary conditions for thermal simulations are determined. Notably, the spin friction torque of the rolling body on the bearing, which contributes to heat generation, must be considered. Finally, the established accuracy design optimization model, which balances total cost and machining accuracy, is solved using the NSGA-II algorithm. The Pareto optimal solution set reveals an 11.74% reduction in total cost and improved machining accuracy for the PMM. Besides, the proposed framework for accuracy design optimization with thermal characteristics is applicable to the manufacturing processes of other machine tools.

Surface roughness is influenced by various factors with uncertainty characteristics, and roughness reliability can be used for the assessment of the surface quality of CNC milling. The paper develops a method for the assessment of surface quality by considering the coupling effect and uncertainty characteristic of various factors. According to the milling kinematics theory, the milling surface topography simulation was conducted by discretizing the cutting edge, machining time, and workpiece. Considering the coupling effect of various factors, a roughness prediction model is established by the SSA-LSSVM, and its prediction accuracy reaches more than 95%. Then, the roughness reliability model was developed by applying the response surface methodology to achieve the assessment of surface quality. The proposed method is verified by the milling experiments. The maximum values of the relative errors between the simulation and experimental results of the surface roughness and roughness reliability are 9% and 1.5% respectively, indicating the correctness of the method proposed in the paper.

The dual-drive ball screw pair serves as a crucial element within the fixed gantry machine tool with cross-rail movement. When in service, the dual-drive ball screw pair experiences variations in axial load, impacting the contact load distribution of the ball screw pair. A calculation model for determining the axial load offset of the dual-drive ball screw pair is proposed to investigate the variation in axial load. The impact of the geometric error associated with the guide rail and the position of the slide are considered. This paper presents the contact load distribution model for the dual-drive ball screw pair. This study investigates the contact load and contact angle distribution of the dual-drive ball screw pair during the machine tool in service. Additionally, based on fractal theory, the stiffness models of individual micro-convex body and contact surfaces have been established. This study provides a comprehensive analysis of the contact stiffness of the ball screw pair, considering the influence of guide rail geometric error and slide position. In addition, the three-dimensional surface morphology of ball screw pair is obtained by experiments. This paper investigates the contact stiffness distribution of dual-drive ball screw pair during service.

Within the realm of smart manufacturing, the growing complexity and scale of computational tasks expose the limitations of conventional computing architectures with rigid task allocation and fragmented collaboration, which impede the efficiency and security of smart manufacturing systems. To address these challenges, this paper presents an innovative cloud-fog-edge-terminal collaborative strategy that provides an adaptive and comprehensive offloading solution for tasks with complex dependencies and large scale, thereby enhancing the seamless collaborative potential of hierarchical computing structures. Additionally, a joint optimization mathematical model for collaborative computational offloading is developed, aiming to minimize task offloading time and assess manufacturing risk. To refine the solution, an advanced multi-objective optimization algorithm is formulated to identify the optimal solutions. The effectiveness and practical applicability of the proposed method are substantiated through simulation experiments and empirical case studies, demonstrating a performance enhancement of 12–29% over other benchmarks. The joint optimization method effectively synchronizes cloud-fog-edge-terminal computing resources, realizing efficient and secure task offloading and execution in smart manufacturing scenarios.