Threaded fasteners are one of the most commonly used connection methods for mechanical structures. Its primary function is to generate appropriate clamping forces and fasten the connected parts. An inappropriate preload can cause loosening, fatigue fracture, and other problems. This will affect the safety and reliability of mechanical equipment. The precise control of the preload has become a critical issue in mechanical assembly processes. Over the past few decades, various tightening measures and methods have been proposed to address this issue. However, many problems continue to exist with practical applications that have not been reviewed comprehensively and systematically. First, various control methods were summarized systematically, and their advantages and disadvantages in engineering applications were analyzed. Torque control is the most widely used tightening method owing to its simple operation and low cost. Therefore, the research on the torque control method was summarized systematically from three aspects: the torque–preload correlation formula, effective friction radius, and friction characteristics during tightening. In addition, the special circumstances that may increase preload uncertainty were discussed. Finally, based on a summary of the current research status, the prospects for future research were discussed. This study would aid researchers in extensively understanding the problems in preload control.

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.

Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faults rarely occur, resulting in less fault data. Therefore, it is necessary to study small sample fault data. For the case of less fault data, the Maml–Triplet fault classification learning framework based on the combination of maml and the triplet neural network is proposed. In the framework of Maml-Triplet fault classification, firstly, an initial signal feature extractor is obtained using the Maml training method. Secondly, the feature vectors corresponding to signal data are obtained using depth distance measurement learning in the triplet neural network, and the fault type is judged based on the feature vectors of unknown signal. The results show that the accuracy of the Maml–Triplet model is 2% higher than that of the triplet model alone and 5% higher than that of the Maml–CNN meta learning method. When there are fewer data samples, the accuracy gap is more obvious. Therefore, in the case of less data, the Maml–Triplet model has an excellent fault identification ability.

Magnetic fluids, a new type of energy transfer fluid with tunable properties, have garnered significant interest from researchers worldwide. Hybrid magnetic fluids prepared by adding different types of nanoparticles exhibit superior thermophysical properties and functional characteristics. In this paper, we prepared a water-based magnetic fluid loaded with multi-walled carbon nanotubes (MCNTs), silver (Ag), and copper (Cu) to enhance thermal conductivity. Using a transient double hot-wire method, we designed and built an experimental measurement system for the thermal conductivity of magnetic fluids with an average measurement error of less than 5%. We studied the thermal conductivity of hybrid magnetic fluids under different conditions and evaluated the advantages and disadvantages of various models, including the Maxwell model, H&C model, Tim model, Y&C model, and Evans model. Our results show that MF+MCNTs, MF+Ag, and MF+Cu nanofluids can all improve the thermal conductivity of the carrier fluid, with MF+MCNTs exhibiting the best improvement effect of 10.93%. Among the five models evaluated, the Evans model had the best predictive effect with a deviation range within 5%. This work provides theoretical and practical reference for enhancing the thermal conductivity of magnetic fluids and provides a more accurate theoretical model for calculating the thermal conductivity of hybrid magnetic fluids.