Sheet Metal Assembly Research Articles

DeviationGAN: A generative end-to-end approach for the deviation prediction of sheet metal assembly

The primary objective of conducting deviation analysis in the context of assembly process is to ascertain and guarantee the quality and precision of the final product. The deviation analysis is important for components that are prone to deformation. The conventional analysis approach primarily relies on the identification of local feature points on individual components to establish relationship between deviations before and after assembly. However, the method does not sufficiently represent the spatial distribution of deviations across the parts. The conventional approach also fails to account for shape inaccuracies and the influence of multiple sources of deviation coupling to certain degree. Therefore, this paper proposes a novel deviation framework for analyzing the sheet metal assembly process based on the skin model shapes and a conditional Generative Adversarial Network (cGAN). The cross-scale shape errors of the critical feature surfaces are modeled by taking the statistical parameters into account. Additionally, the contour maps of the established surface models and the multiple-source deviations are combined using images in order to construct the data for the flexible deviation network models. Then, the finite element method is used to simulate the assembly process, yielding the final component deviations. The contour maps of the assembly deviations and the images of the part deviations are utilized as the input conditions and ground truth for the dataset. The simulations and experiments conducted in this study provide evidence to support the effectiveness of the proposed method in predicting field-to-field deviation deformation. The results indicate that the proposed method outperforms traditional approaches in terms of accuracy. Furthermore, this approach enables end-to-end deviation prediction. The well-trained model is capable of directly outputting the corresponding predicted deviations given input of deviation factors. By employing such a deviation analysis framework, it enables achieve an accurate and high efficiency analysis in the assembly process.

Metrology of Sheet Metal Distortion and Effects of Spot-Welding Sequences on Sheet Metal Distortion

Refill friction stir spot welding (RFSSW) is an emerging solid-state welding technology that demonstrates an outstanding ability to join aerospace aluminum alloys. The thermomechanical processing of RFSSW may cause variations in the workpiece in the form of distortion. This study aims to establish a metrology method for sheet metal distortion with the intent to investigate the effects of RFSSW sequences on sheet metal distortion. The approach employs a robotic metrology system and the least squares method to measure and estimate the flatness of sheet metal before RFSSW and after RFSSW. The RFSSW experimentation produces five 10-spot-weld panels with five different RFSSW sequences, whereas the RFSSW sequences are based on the common practice of making sheet metal assemblies. A panel consists of two lap-welded sheets where the top sheet, a 6013-T6 aluminum alloy, is refill friction stir spot welded onto the bottom sheet, a 2029-T8 aluminum alloy. The results suggest that RFSSW sequences do have effects on sheet metal distortion. The panel with the worst distortion has a root-mean-square error of 0.8 mm as an average deviation from the ideal flatness.

Decomposing deviations of scanned surfaces of sheet metal assemblies

We present a finite-element-based approach to support quality assurance for assembly processes of non-rigid sheet metal parts in the automotive and aerospace industries. These parts have adjustable mechanical boundaries, making it possible to compensate geometrical deviations caused by the production process of components. Every part produced must be adjusted individually. Determining what boundaries to adjust to and in what direction is challenging and requires experience. Our goal was to identify sources of measured surface deviations after part assembly and provide valuable information to the quality assurance engineer. We solve the corresponding parameter estimation problem via a least-squares approach. From the estimated parameter states that led to measured deviations, additional visualizations are calculated to support the inspection engineer when deriving countermeasures.

Manufacturable casting parts design with topology optimization of structural assemblies

This paper presents a design method for manufacturable casting parts based on topology optimization of structural assemblies, which considers the geometry requirement and the manufacturing constraint of die-set material cost. The problem formulation follows the previous work presented in multi-component topology optimization for stamped sheet metal assemblies (MTO-S). Based on the vector method combined with Heaviside function, the moldability constraints for casting parts is formulated. As the base structure of component is easily misidentified as an undercut structure by the moldability constraints in the structural assemblies, the component baseline is proposed to realize the automatic filtering of the “fake” undercut structures which can be extended to the parting line to obtain the form of two-mold design. Several numerical examples on compliance minimization of single-mold and two-mold casting parts are conducted to verify the validity of the proposed method. The optimized results show that there is no interior void for each component and the component manufacturability has been improved obviously. The setting of minimum-area bounding box (MABB) area constraint limits and the number of components will have a significant effect on the performance of the optimized structure. Users can achieve the desirable solution based on their actual demand by making trade-offs between the structural performance and manufacturing cost.

Multi-component topology optimization for thin-walled structures

This article presents a multi-component topology optimization method for thin-walled structures, which can be applied to two-dimensional plate components and three-dimensional tube components. Based on the theory of multi-component topology optimization for stamped sheet metal assemblies, an accurate calculation method for the Young’s modulus of joint elements is provided. To avoid the discontinuous derivative and improve the optimization efficiency, a non-zero gradient-based material interpolation scheme with penalization is constructed. The cost of die-set materials can be controlled effectively by the maximum size of components. Several topology designs based on minimized compliance confirm the effectiveness of the proposed method. The topology solutions illustrate that the proposed method can realize the structural design of thin-walled structures which can meet the die-set material cost requirement.

Efficient Spot Welding Sequence Simulation in Compliant Variation Simulation

Abstract 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.

Integrated Tolerance and Fixture Layout Design for Compliant Sheet Metal Assemblies

Part tolerances and fixture layouts are two pivotal factors in the geometrical quality of a compliant assembly. The independent design and optimization of these factors for compliant assemblies have been thoroughly studied. However, this paper presents the dependency of these factors and, consequently, the demand for an integrated design of them. A method is developed in order to address this issue by utilizing compliant variation simulation tools and evolutionary optimization algorithms. Thereby, integrated and non-integrated optimization of the tolerances and fixture layouts are conducted for an industrial sample case. The objective of this optimization is defined as minimizing the production cost while fulfilling the geometrical requirements. The results evidence the superiority of the integrated approach to the non-integrated in terms of the production cost and geometrical quality of the assemblies.

Optimization of sheet metal resistance spot welding process fixture design

Resistance spot welding is a method which is commonly used for sheet metal joining purposes in industry due to minimum in thickness it has variation at the time of assembly. Fixturing plays a key role in improving sheet assembly system weld efficiency. As per the survey by American automotive weld quality of the sheet metal assembly process, around 73 percent of the variance problems from pre-production to production were due to fixture based problems. This exertion introduces a new technique of fixture design for the process of sheet metal spot welding. It engrossed on the effect of the fixture location on the dimensional quality of sheet metal parts by considering welding force, clamping force and position of locator. A hybrid optimization technique is used for determining the optimal position and clamping points with finite element analysis and achieved minimum deformation.

Optimal design of fixture layouts for compliant sheet metal assemblies

A preeminent factor in the geometrical quality of a compliant sheet metal assembly is the fixture layout that is utilized to perform the assembly procedure. Despite the presence of a great number of studies about the optimization of assembly fixture layouts, there is not a comprehensive algorithm to optimize all design parameters of fixture layouts for compliant sheet metal assemblies. These parameters are the location and type of hole and slot in each part, the slot orientation, and the number and location of additional clamps. This paper presents a novel optimization method that optimizes all these parameters simultaneously to maximize the geometrical quality of the assemblies. To attain this goal, compliant variation simulations of the assemblies are utilized along with evolutionary optimization algorithms. The assembly springback and contacts between parts are considered in the simulations. After determining the optimal design parameters, the optimal positions of locators are fine-tuned in another stage of optimization. Besides, a top-down design procedure is proposed for applying this method to multi-station compliant assemblies. The presented method is applied to two industrial sample cases from the automotive industry. The results evidence a significant improvement of geometrical quality by utilizing the determined fixture layout from the presented method compared with the original fixture layouts of the sample cases.

Squeezing force detection and signal processing for robot assisted pneumatic percussive riveting system

Abstract Researches in the literature treated the pneumatic percussive riveting as an open-loop process. Vibration signals of the percussive hammer and driven-head based dimensions cannot be used to real-time control each impacting of the pneumatic percussive riveting. The paper installs a force sensor on the bucking bar to collect the squeezing force signal in pneumatic percussive riveting. And a squeezing force based closed-loop process control method for robot-assisted pneumatic percussive riveting is proposed. After introducing the robot assisted pneumatic percussive riveting system, the squeezing force and the impacting frequency under different pneumatic pressure are investigated extensively with the constructed pneumatic percussive riveting platform. A squeezing force signal processing method based on time-frequency analysis is used to extract the characteristics of the percussive riveting such as the peak squeezing force, the impacting frequency and the number of impacts etc. Based on the peak squeezing force and the impacting frequency, the pneumatic percussive riveting process is divided into four stages in order to control the squeezing force of the robot riveting efficiently. A squeezing force based real-time feedback algorithm is proposed to control the squeezing force of each impacting by adjusting the pneumatic pressure and the feed force. The squeezing force based real time control method outlined in the paper is found to be effective and accurate. It can be used for robot-assisted pneumatic percussive riveting system, and closed-loop control of percussive riveting in sheet metal assembly.

An Improved Phenotype-Genotype Mapping for Solving Selective Assembly Problem Using Evolutionary Optimization Algorithms

Abstract Selective assembly is an assembly technique for producing high-quality assemblies from relatively lower quality mating parts. Developing the application of this technique to sheet metal assemblies in the automotive industry can improve the geometrical quality and reduce production costs significantly. Nevertheless, the required calculation time is the main obstacle against this development. To apply a selective assembly technique, an optimization problem of finding the optimal combination of mating parts should be solved. This problem is an MINLP optimization problem for selective assembly of sheet metals. This paper demonstrates that the phenotype-genotype mapping commonly used in most conventional selective assembly studies enlarges the search domain of the optimization. Thereafter, a new approach that makes the mapping one-to-one is proposed and applied to three selective assembly sample cases from the literature. Moreover, it is indicated that meta-heuristic methods are superior to MILP and MINLP methods in solving this problem, particularly for assemblies of more than two components and relatively large batch sizes. The results evidence that using the new method improves the convergence rate of meta-heuristics in solving the problem by reducing the number of cost function evaluations to 45% for sheet metal assemblies. This means reducing up-till 26 h of the optimization time for the presented sample cases.

Efficient Spot Welding Sequence Optimization in a Geometry Assurance Digital Twin

Abstract A digital twin for geometry assurance contains a set of analyses that are performed to steer the real production for securing the geometry of the final assembly. In sheet metal assemblies, spot welding is performed to join the parts together. The sequence of the welding has a considerable influence on the geometrical outcome of the final assembly. In industry, the sequence of welding to secure the geometry is mainly derived by tacit manufacturing knowledge. Including such knowledge to mimic the production process requires extensive knowledge management, and the result might be just a good enough solution. Theoretically, spot welding sequence optimization for the optimal geometrical quality is among NP-hard combinatorial problems. In a geometry assurance digital twin, where assembly parameters are selected for the individual assemblies, time constraints define the quality of the optimal sequence. In this paper, an efficient method for spot welding sequence optimization with regards to the geometrical quality is introduced. The results indicate that the proposed method reduces 60–80% of the time for the sequencing of the spot welding process to achieve the optimal geometrical quality.

Integration of in-plane and out-of-plane dimensional variation in multi-station assembly process for automotive body assembly

Automotive body assembly systems contain multiple operations in multi-station processes. One of the most critical challenges for such manufacturing systems is dimensional quality, which is affected by the accumulation and propagation of variation caused by manufacturing imperfections. However, sheet metal part compliancy behavior makes the variation modeling method extremely intricate when both rigid (in-plane) and compliant (out-of-plane) variations are considered. This paper develops a more accurate variation propagation model to describe dimensional variation of sheet metal assembly in multi-station assembly system through involving both the variation types simultaneously as well as the impacts of assembly operations on each other. In this methodology, three sources of deviations—non-ideal parts, fixture errors, and assembly operations effects—are taken into account. The variation generated in every assembly operation (placing, clamping, fastening, and releasing steps) and the variation propagation through station-to-station interaction (repositioning) are analyzed by the transfer function mechanism. In the in-plane direction, the stream of variation analysis is adopted to obtain the rigid transfer function to describe the position and orientation relationships between part and assembly element errors. For the simulation of part deformation during the assembly process in the out-of-plane direction, the compliant transfer functions are extracted by variation response methodology. A nested state space model is used to integrate the overall assembly variation by updating part geometry after each assembly operation. The capability of proposed method is illustrated through a case study on an automotive body side assembly process.

A new surrogate model\u2013based method for individualized spot welding sequence optimization with respect to geometrical quality

In an individualized shee metal assembly line, form and dimensional variation of the in-going parts and different disturbances from the assembly process result in the final geometrical deviations. Securing the final geometrical requirements in the sheet metal assemblies is of importance for achieving aesthetic and functional quality. Spot welding sequence is one of the influential contributors to the final geometrical deviation. Evaluating spot welding sequences to retrieve lower geometrical deviations is computationally expensive. In a geometry assurance digital twin, where assembly parameters are set to reach an optimal geometrical outcome, a limited time is available for performing this computation. Building a surrogate model based on the physical experiment data for each assembly is time-consuming. Performing heuristic search algorithms, together with the FEM simulation, requires extensive evaluations times. In this paper, a neural network approach is introduced for building surrogate models of the individual assemblies. The surrogate model builds the relationship between the spot welding sequence and geometrical deviation. The approach results in a drastic reduction in evaluation time, up to 90%, compared to the genetic algorithm, while reaching a geometrical deviation with marginal error from the global optimum after welding in a sequence.

A Novel Rule-Based Method for Individualized Spot Welding Sequence Optimization With Respect to Geometrical Quality

Abstract Spot welding is the predominant joining process for the sheet metal assemblies. The assemblies, during this process, are mainly bent and deformed. These deformations, along with the single part variations, are the primary sources of the aesthetic and functional geometrical problems in an assembly. The sequence of welding has a considerable effect on the geometrical variation of the final assembly. Finding the optimal weld sequence for the geometrical quality can be categorized as a combinatorial Hamiltonian graph search problem. Exhaustive search to find the optimum, using the finite element method simulations in the computer-aided tolerancing tools, is a time-consuming and thereby infeasible task. Applying the genetic algorithm to this problem can considerably reduce the search time, but finding the global optimum is not guaranteed, and still, a large number of sequences need to be evaluated. The effectiveness of these types of algorithms is dependent on the quality of the initial solutions. Previous studies have attempted to solve this problem by random initiation of the population in the genetic algorithm. In this paper, a rule-based approach for initiating the genetic algorithm for spot weld sequencing is introduced. The optimization approach is applied to three automotive sheet metal assemblies for evaluation. The results show that the proposed method improves the computation time and effectiveness of the genetic algorithm.

Influence of electrode force on metallurgical studies and mechanical properties of resistance spot welded dual phase (DP800) steel joints

Abstract The DP steels are attractive to automotive sectors due to the improved fuel efficiency, occupant’s safety and reducing the car body weight. The resistance spot welding (RSW) most appropriate eco-friendly process for sheet metal and white body car assemblies over fusion welding. The present research work analysis the impact of electrode force on mechanical and metallurgical characteristics of dual phase steel. In this investigation similar joint of DP 800 joints (lap shear and cross shear) specimen fabricated and tensile shear test was evaluated using a universal testing machine. Micro and macrostructure were evaluated optical microscope. The hardness was taken along the weld nugget using Vickers microhardness tester machine. The electrode force was varied at five levels (3.75 MPa to 4.75 MPa). The tensile shear strength is found to be increased with an increase in electrode force up to a certain limit and then it decreases. The microhardness survey revealed that peak hardness in the nugget zone and the hardness is lower in the HAZ. The softening of HAZ is mainly attributed to inter-critical heating during welding, which leads to weld failure.

Multi-component topology optimization for die casting (MTO-D)

This paper presents a multi-component topology optimization method for the structural assemblies that are made of components produced by die casting (MTO-D) processes, where each part is guaranteed to be free from fully enclosed cavities and undercuts in the direction of mold drawing. Building upon our previous work on multi-component topology optimization for stamped sheet (MTO-S) metal assemblies, the proposed mathematical formulation incorporates new design variables specifying the parting line for each component. For each component, the structural elements are clustered into the core and cavity halves by using the parting line as a linear classifier for the point clouds represented by the density and membership values. The presence of undercut features and fully enclosed cavities are then evaluated for each mold half using the vector method with the numerically approximated density gradient. Several numerical examples on compliance minimization are presented to demonstrate the proposed method. The optimized results show that each part is indeed moldable with no undercut in the drawing direction and has no fully enclosed cavity.

A method for identification and sequence optimisation of geometry spot welds in a digital twin context

Geometrical variation is the main cause of the aesthetic and functional problems in the product geometry. Variation and disturbances are caused by several sources during the manufacturing process. In the automotive industry, one of the main sources of variation is the spot welding sequence. Optimising this sequence is of combinatorial Nondeterministic Polynomial (NP)-hard problems. In a typical automotive sheet metal assembly, there are a large number of spot welds. Today, if the number of spot welds in a sub-assembly is more than 10, the sequence optimisation will be a challenging and time-consuming task. Therefore, industry is mainly dependent on the experiential approach or simultaneous welding simulations for predicting the geometrical outcome. In this paper, a method is introduced to identify the geometry weld points to reduce the optimisation problem size in a geometry assurance digital twin context. This method is then applied to three automotive body-in-white assemblies and optimisation is performed. The results show that reducing the size of the problem by the proposed approach can help to save a considerable amount of time while getting geometrical outcomes within the satisfactory error levels.

Nonlinear Material Model in Part Variation Simulations of Sheet Metals

Current methodologies for variation simulation of compliant sheet metal assemblies and parts are simplified by assuming linear relationships. From the observed physical experiments, it is evident that plastic strains are a source of error that is not captured in the conventional variational simulation methods. This paper presents an adaptation toward an elastoplastic material model with isotropic hardening in the method of influence coefficients (MIC) methodology for variation simulations. The results are presented in two case studies using a benchmark case involving a two-dimensional (2D) quarter symmetric plate with a centered hole, subjected to both uniaxial and biaxial displacement. The adaptation shows a great reduction in central processing unit time with limited effect on the accuracy of the results compared to direct Monte Carlo simulations.

Developing a selective assembly technique for sheet metal assemblies

Applying the concept of Digital Twin in production processes supports the manufacturing of products of optimal geometry quality. This concept can be further supported by a strategy of finding the optimal combination of individual parts to maximise the geometrical quality of the final product, known as selective assembly technique. However, application of this technique has been limited to assemblies where the final dimensions are just function of the mating parts' dimensions and this is not applicable in sheet metal assemblies. This paper develops a selective assembly technique for sheet metal assemblies and investigates the effect of batch size on the improvements. The presented method utilises a variation simulation tool (Computer-Aided Tolerancing tool) and an optimisation algorithm to find the optimal combination of the mating parts. The approach presented is applied to three industrial cases of sheet metal assemblies. The results show that using this technique leads to a considerable reduction of the final geometrical variation and mean deviation for these kinds of assemblies. Moreover, increasing the batch size reduces the amount of achievable improvement in variation but increases the amount of achievable improvement in the mean deviation.