Roham Sadeghi Tabar

Ensuring geometric accuracy in joining mechanical structures is a key challenge in smart manufacturing, particularly during assembly. To improve assembly and joining accuracy, this paper employs digital twin technology, with a focus on high-fidelity digital modeling as a foundational step toward realizing digital twins–while treating bidirectional automated data flow as a future development direction. Digital twin models are developed for two representative joint types: threaded connections and spot-welded sheet metal structures, incorporating critical influences from manufacturing, assembly, and service phases. Compared to traditional idealized simulations, the proposed models enable more effective assembly process optimization and enhanced geometric quality control.

Welding-induced geometric deviations remain a critical challenge in industrial manufacturing, particularly in achieving high-precision assembly. This study investigates the effects of welding path directions, sequences, and locating schemes on the displacement of welded assemblies, focusing on minimizing geometric deviations. Using finite element method (FEM) simulations and a design of experiments (DOE) approach, the interactions between these parameters were systematically analyzed. Results show that the locating scheme plays a dominant role in controlling displacement, with optimal configurations significantly reducing geometric errors. Welding sequences were also found to have a considerable impact, further minimizing distortions when appropriately optimized. The effect of weld path direction, while less significant for simpler geometries, became more pronounced in assemblies with curvature. These findings pinpoint the necessity of integrating a combined factor approach, including fixturing, welding sequence, and path direction, to optimize and improve the geometric quality of welded assemblies.

Geometric deviation associated with the assembly of sheet metal is a general concern for manufacturers. The typical assembly step involves a sequence of events that exert forces on the parts to enforce them to the nominal condition and to connect the parts together. The simulation and optimization of the assembly steps often neglect the sequence of operations due to the problem and computation complexity. This paper investigates the influence of the clamping sequence in the body-in-white (BIW) manufacturing process on the geometrical quality of the assembly. An approach for modeling clamping sequences for non-rigid variation simulation is introduced in a digital twin context, taking the part deviation into consideration. An optimization method is proposed to achieve minimum geometric deviation after clamping the parts and welding them together. The method is successfully applied on two reference assemblies, and the results show that the sequence of clamping can impact the total geometric deviation up to 31%. Combining clamping and welding sequence optimization can enhance the quality improvement to 77% after releasing the assembly from the fixture and springback.

Geometric variation causes functional and aesthetic problems in the assemblies. The challenge is predicting the moments of the distribution of geometric deviations of assemblies to evaluate compliance with the set requirements. The joining operation, i.e., resistant spot welding (RSW), is one of the most crucial steps in the assembly process of nonrigid components, imposing forces on the parts and causes bending and deformation during the assembly, consequently contributing considerably to the final geometric outcome of the assembly. To model the behavior of the assembly realistically and achieve accurate simulation results, considering the sequence of joining is essential. In a digital twin of the assembly process, joining sequences need to be provided for the optimal geometric outcome of the batch of assemblies. The sequence optimization of the joining processes is a time-consuming combinatorial problem to solve. Variation analysis of nonrigid assemblies with stochastic part inputs, including optimal joining sequences, requires an extensive amount of the computational effort. More efficient approaches for evaluating assembly geometric variation are desired. In this article, a computationally efficient approach is proposed for geometric variation analysis and optimization of nonrigid assemblies with stochastic part inputs with respect to the RSW sequences. A clustering approach is proposed categorizing the incoming parts based on the part variation. Sequence optimization is performed, and geometric variation is analyzed for each cluster. The results show that the proposed method drastically reduces the computation time needed for sequence optimization compared to individualized optimization for each assembly.

Geometric variation is one of the sources of quality issues in a product. Spot welding is an operation that impacts the final geometric variation of a sheet metal assembly considerably. Evaluating the outcome of the assembly, considering the existing geometrical variation between the components can be achieved using the Method of Influence Coefficients (MIC), based on the

Geometrical variation is one of the sources of quality issues in a product. Spot welding is an operation that impacts the final geometrical variation of a sheet metal assembly considerably. Evaluating the outcome of the assembly, considering the existing geometrical variation between the components, can be achieved using the method of influence coefficients (MICs), based on the finite element method (FEM). The sequence with which the spot welding operation is performed influences the final geometrical deformations of the assembly. Finding the optimal sequence that results in the minimum geometrical deformation is a combinatorial problem that is experimentally and computationally expensive. Traditionally, spot welding sequence optimization strategies have been to simulate the geometrical variation of the spot-welded assembly after the assembly has been positioned in an inspection fixture. In this approach, the calculation of deformation after springback is one of the most time-consuming steps. In this paper, a method is proposed where the springback calculation in the inspection fixture is bypassed during the sequence evaluation. The results show a significant correlation between the proposed method of weld relative displacements evaluation in the assembly fixture and the assembly deformation in the inspection fixture. Evaluating the relative weld displacement makes each assembly simulation less time-consuming, and thereby, sequence optimization time can be reduced by up to 30%, compared to the traditional approach.

Identifying the optimal sequence of joining is an exhaustive combinatorial optimization problem. On each assembly, there is a specific number of weld points that determine the geometrical deviation of the assembly after joining. The number and sequence of such weld points play a crucial role both for sequencing and assembly planning. While there are studies on identifying the complete sequence of welding, identifying such joints are not addressed. In this paper, based on the principles of machine intelligence, black-box models of the assembly sequences are built using the support vector machines (SVM). To identify the number of the critical weld points, principle component analysis is performed on a proposed data set, evaluated using the SVM models. The approach has been applied to three assemblies of different sizes, and has successfully identified the corresponding critical weld points. It has been shown that a small fraction of the weld points of the assembly can reduce more than 60% of the variability in the assembly deviation after joining.