Bolted joints are widely used in the aerospace and automotive industries due to their ease of assembly, disassembly and design flexibility. Optimizing threaded fasteners is essential to achieve uniform load distribution and minimize the number of bolts required, thereby reducing system cost and weight. This review paper aims at summarizing the five optimization techniques available in the literature, including bolt layout, tightening strategies, tightening sequences, bolt size, and stresses. The purpose is to emphasize the importance of optimizing bolted joints via the proper selection of materials, geometry, patterns, and bolt sizes, to obtain efficient joints with low assembly time while maintaining strength.

In this work, Friction Stir Welding (FSW) is used to study the impact of different pin tool geometries on hardness, microstructure, and tensile strength of welded AA6061 and AA7075 butt joints. H13 steel-tool including three distinct pin geometries, i.e. threaded cylindrical (THC), straight cylindrical (SC), and tapered cylindrical (TAC) are used for welding. The welds were produced using a spindle speed of 1800 rpm and welding speed (traverse speed) of 15 mm/min by a customized clamping system. As shown by the hardness test, the welded sample using the threaded pin tool geometry produces more hardened joints than other geometries of the pin tools. As analyzed by the tensile strength experiment performed on the Universal Testing Machine (UTM), the samples welded by threaded pin give higher ultimate tensile strength, i.e. 282 MPa compared to other tool geometries. A fine grain structure was also observed in the nugget zone of welded joints obtained by threaded pin.

Threaded fasteners are one of the most important joining techniques, especially due to their ease of mounting and dismounting. These are essentially friction joints; therefore, friction coefficients between the surfaces play a critical role in the correct mounting of the threaded fasteners. Materials and lubrication conditions are the major factors that can affect the correct preload of the threaded fasteners. Particularly, when shifting from steel to titanium fasteners to achieve a high strength-to-weight ratio, the friction shift is very significant. Researchers have studied varying levels of lubrication to achieve optimum friction conditions. VG46 has been used most in the literature; however, its non-renewable nature necessitates the use of another alternative that is good for the environment and can cause a reduction in pollution in the environment. For this reason, in the present study, castor oil and fractionated coconut oil have been used for Ti bolts to achieve low friction coefficients. Torque tension tests have been performed on the Ti bolts using the different lubricants mentioned above and friction coefficients at the underhead and the threaded portions are compared with the commercial VG46 lubricant. Castor oil shows good performance compared to the other lubricants tested in terms of underhead friction coefficients, whereas the thread friction coefficients remain almost the same in all the lubrication conditions.

In this study, machine learning (ML) models were developed to optimize the bolt layout to minimize the I-beam deflection at the free end when subjected to different loads. Finite element analysis (FEA) was integrated with Latin hypercube sampling (LHS) to build comprehensive datasets. Three load cases, including vertical, horizontal and combined (vertical + horizontal), and a cantilever beam fixed with the end plate bolted to the support plate, were considered. A total of 150 data points were generated, with each data point comprising of varying bolt layouts and corresponding beam deflections. Six ML models including lasso, ridge, Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), and random forest (RF) were trained and validated. The model performance was evaluated using the root mean square error (RMSE), and coefficient of determination ( R ² ) values, and the results were analyzed using SHapley Additive ExPlanation (SHAP) method. Subsequently, the best predicted model was used as a surrogate model for optimization, including particle swarm optimization (PSO) and genetic algorithms (GA) to obtain the best optimal bolt layout to minimize beam deflection. Finally, the optimal layout given by the surrogate model was verified against a full FEA analysis in ANSYS. The results from the model and FEA show a good match, indicating that the model accurately predict the deformation and can eliminate costly FEA simulations.

A generalized sequence-dependent constitutive model is formulated and coupled with genetic algorithm (GA) to minimize the effect of bolt-on-bolt elastic interaction (EI) of a large size wind turbine blade joint during the process of joint preloading prior to turbine operation. The composite blade flange is bolted to a steel hub flange on the turbine tower side. A Python-based tool is developed for big data sets of elastic interaction among the 54-bolt wind turbine blade-to-hub connection. Trial tightening sequence and patterns include (a) clockwise sequential tightening of six bolts at each apex of three rotated triangles, (b) clockwise sequential tightening of nine bolts at each apex of two rotated triangles, (c) separately tightening one pair of bolts that are located 180° diagonally across each other and repeat 26 more times in a clockwise direction. A genetic algorithm is used to identify feasible tightening sequences that minimize the scatter of bolt preload and achieve the desired level of a uniform joint preload.