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The automation capabilities and virtual tools within engineering disciplines, such as structural mechanics and aerodynamics, enable efficient Multidisciplinary Design Optimization (MDO) approaches to evaluate and optimize the performance of a large number of design variants during early design stages of aircraft components. However, for components that are designed to be welded, in which multiple functional requirements are satisfied by one single welded structure, the automation and simulation capabilities to evaluate welding-producibility and predict welding quality (geometrical deformation, weld bead geometrical quality, cracks, pores, etc) are limited. Besides the complexity of simulating all phenomena within the welding process, one of the main problems in welded integrated components is the existing coupling between welding quality metrics and product geometry. Welding quality can vary for every new product geometrical variant. Thus, there is a need of analyzing rapidly and virtually the interaction and sensitivity coefficients between design parameters and welding quality to predict welding producibility. This paper presents as a result an automated and interactive welding-producibility evaluation approach. This approach incorporates a data-based of welding-producibility criteria, as well as welding simulation and metamodel methods, which enable an interactive and automated evaluation of welding quality of a large number of product variants. The approach has been tested in an industrial use-case involving a multidisciplinary design process of aircraft components. The results from analyzing the welding-producibility of a set of design variants have been plotted together with the analysis results from other engineering disciplines resulting in an interactive tool built with parallel coordinate graphs. The approach proposed allows the generation and reuse of welding producibility information to perform analyses within a big spectrum of the design space in a rapid and interactive fashion, thus supporting designers on dealing with changes and taking fact-based decisions during the multidisciplinary design process.

Selective laser melting (SLM) has gained prominence in the manufacturing industry for its ability to produce lightweight components. As the raw material used is in powder form, the stochastic nature of the powder distribution influences the powder layer thickness and affects the final build quality. In this paper, a multi-layer multi-track simulation study is conducted to investigate the effect of stochastic powder distribution on the layer thickness and plastic strain in a printed geometry. A faster simulation approach is employed to simulate multiple layers. First, the powder distribution and the melt layer thickness of the first layer are obtained from discrete element method (DEM) and computational fluid dynamics (CFD) simulations respectively. Next, the melt layer thickness of the first layer is used as an input to the finite element (FE) based structural mechanics solver to predict the deformation and layer thickness of subsequent layers. Two nominal layer thicknesses 67.4 μm and 20 μm were considered. Two particle size distribution (PSD) configurations and two scanning strategies were tested. The results showed that variation in PSD and scanning strategy leads to variation in layer thickness which in turn leads to variation in the plastic strain that is known to drive the deformation. However, the nominal layer thickness of 20 μm was found to be less influenced by the PSD configuration. The proposed simulation approach and the insights achieved can be used as inputs in the part-scale simulations for geometric robustness evaluation in the early design stages of SLM products.

In this paper the focus is on variation analysis for composite CFRP (carbon fibre reinforced polymers) parts. A detailed study of possible sources of variation in manufacturing of CFRP parts is presented. Based on the variables identified a simulation chain on how to manage and simulate this variation is described. This paper also presents a logical approach to how a digital twin for a fully automated production line of compression molded CFRP parts can be built, and to investigate and quantify how much each individual variable contributes to the final variation in the component.

Geometric variation is one of the causes of aesthetic and functional issues in mechanical assemblies. To predict the geometric variation in assemblies of rigid and non-rigid parts, statistical variation simulation is introduced. For non-rigid parts, bending and deformation occur during the assembly process. In non-rigid variation simulation, contact modeling is utilized to avoid the virtual penetration of the components in the adjacent areas. Contact modeling imposes non-linear behavior to the MIC approach for variation simulation, and thereby the problem complexity and simulation time increase. Traditionally, iterative node search is used to identify and define the computational contact nodes. However, iterative search is time-demanding, specifically in large-scale models, as the search space increases by the number of nodes included in the assembly. To allow for faster contact search, a data structuring method using Kd-trees and nearest neighbor search (NN) is implemented and integrated into a computer aided tolerancing tool, enhancing the search functionality and reducing the search time compared to iterative one-by-one node search. The method is applied to three reference assemblies of different size, and the identified contact nodes and the time needed to perform the search is compared to an iterative node search. The results show that the K-tree structure and nearest neighbor search perform considerably, 96%, faster than the iterative node search. The method increases the search performance, while the identified contact points are similar to the ones identified by an iterative search. The approach efficiently enables the contact search of large models and reduces the modeling time required for non-rigid variation simulation.

The availability of automated production lines and production data has opened a new opportunity for improving the geometrical quality of assemblies by using a digital twin in the concept of a smart assembly line. In this concept, a digital twin is generated from scanned data of incoming parts for each assembly. The assembly process is then simulated for the digital twin using variation simulation tools. Subsequently, the optimal production parameters are found by utilizing optimization algorithms along with the simulations so that the geometrical qualities of assemblies are maximum. Two effective production parameters that can be optimized in this concept are the combination of parts and adjustments of locators. The techniques to implement optimize these parameters in the production are referred to as selective assembly and individualized locator adjustments, respectively. This paper evaluates the results of optimizing each parameter separately and both parameters together. To attain this goal, the results of applying the optimal parameters on three industrial cases are determined and compared. The results evidence that the potential of individualized locator adjustment in improving geometrical quality is considerably greater than selective assembly.

Traditional computer aided tolerancing method could not accomplish the tolerancing and variation simulation well in composite assembly. The paper presents a clamping force integrated computer aided tolerancing method for composite assembly. The stochastic variations and the stackup of variations are affected by clamping forces. The clamping force modified probability distribution is used to represent the modification based on the verified FEA model of the composite assembly. The clamping forces are coordinated based on the main deformation mode extracted by the principal component analysis to satisfy the coaxial tolerance requirements of the composite assembly. The assembly of an aircraft composite elevator is considered to illustrate the computational aspects of the proposed methods. The computer aided tolerancing method involving clamping forces outlined in the paper is found to be effective for composite assembly.

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.

Selective laser heat treatment is a well-known process for its ability to produce tailor heat treated blanks (THTB). Specifically, ultra high strength boron steels with tailored material properties can be produced. However, this process generates unwanted distortion and influences geometrical variation. This in turn can affect functionality, aesthetics, and performance of the final product. Understanding the effects on geometrical variation in the final product or the assembly will enable in designing and producing geometry assured products. In this paper, boron steel blanks were selectively laser heat treated with a specific heat treatment pattern and laser heating direction sequence. These heat treated blanks were then cold formed. Further on, spot welding simulation of the cold formed parts was performed to assess the effect on geometrical variation at the assembly level. The results show that the effect of selective laser heat treatment on geometrical variation at part level propagates further to the assembly level. It implies that the effect on geometrical variation should be minimized at part level, when the blanks are laser heat treated. Hence, the sources that influence geometrical variation at part level when employing selective laser heat treatment are presented and discussed. The motivation and possibilities to minimize the effects in the early design concept stages is provided.

Joining sequence optimization is a combinatorial problem, requiring extensive computational time. The significance of determination of an optimal sequence for improved geometrical quality is substantial. Previously, genetic algorithms have been studied for defining the optimal sequence. However, these algorithms are highly dependent on the internal parameters, requiring additional computational analysis and thereby extended evaluation time. In this article, a novel robust stepwise algorithm is introduced to determine the optimal weld sequence. Application of the proposed algorithm leads to drastic time improvements for defining the optimal weld sequence of each assembly. Three industrial assemblies are evaluated. Comparison with the previously applied population-based optimization algorithms indicates that the optimization time can be reduced drastically with the proposed stepwise algorithm. The stepwise algorithm is intended to be applied in a geometry assurance digital twin, where the assembly parameters are being optimized for each individual assembly.

It is expected that aero engine businesses will develop radically new types of propulsion solutions to meet the new sustainability-driven targets. Consequently, to meet new conditions not previously encountered, both in design and in production, the structural components of the engines will also have to be radically different. Implications of this are significant for aero engine manufacturers. Production will need to respond accurately and flexibly to provide advanced engine structures to new engine designs. This makes the ability to trade and optimise the balance between functional performance and cost of realising them, critical. Furthermore, the time and accuracy of such investigations is a competitive factor. In this paper, a method is proposed and demonstrated for how weld manufacturability of structural aero engine components can be simulated and traded against performance already in the early phases of design., The project leading to this paper has received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation programme. Grant agreement No 887174. And from the Swedish Innovation Agency (VINNOVA) through the DIFAM project (ID 2019-02756).

As the aerospace industry continues its shift towards digital manufacturing, an increased use of inline sensors and data collection is creating an opportunity to further optimize the production process. Measurements from individual parts make it possible to adapt each process to current conditions rather than running all processes nominally. This is sometimes described as individualized production as opposed to traditional mass production. The concept of a digital twin for manufacturing has recently gained more attention as a promising method for individualized production. A digital twin collects data from a real environment to create a virtual copy of a physical phenomenon, which can be used to predict how its real counterpart is going to behave. The approach has been proposed for a manufacturing environment where it would be used to predict the outcome of a production process. This could prove particularly useful for fabrication processes, a method used for making aero engine parts by joining large assemblies of smaller parts through welding. This paper presents functionalities that can be used to implement a digital twin in a high precision fabrication process, outlining different approaches for data collection, data analysis, and adaptive process adjustments. An example is shown where physical measurements are used to improve the predictive capabilities of a welding simulation in order to enable more accurate process adjustments.

Selective laser melting (SLM) process is a powder bed fusion additive manufacturing process that finds applications in aerospace and medical industries for its ability to produce complex geometry parts. As the raw material used is in the powder form, particle size distribution (PSD) is a significant characteristic that influences the build quality in turn affecting the functionality and esthetic aspects of the product. This article investigates the effect of PSD on the printed geometry for 316L stainless steel pow der, where three coupled in-house simulation tools based on discrete element method (DEM), computational fluid dynamics (CFD), and structural mechanics are employed. DEM is used for simulating the powder bed distribution based on the different powder PSD. The CFD is used as a virtual testbed to determine thermal parameters such as heat capacity and thermal conductivity of the powder bed viewed as a continuum. The values found as a stochastic function of the powder distribution are used to analyze the effect on the melted zone and deformation using structural mechanics. Results showed that mean particle size and PSD had a significant effect on the packing density, melt pool layer thickness, and the final layer thickness after deformation. Specifically, a narrow particle size distribution with smaller mean particle size and standard deviation produced solidified final layer thickness closest to nominal layer thickness. The proposed simulation approach and the results will catalyze the development of geometry assurance strategies to minimize the effect of particle size distribution on the geometric quality of the printed part.

The visual quality is a large contributor to the over-all quality impression of a product. For a complex, assembled product the visual quality is often judged by the geometrical quality in its split lines, where parallel split lines with small gaps and no flush usually are the desirable outcome. The gap, flush and parallelism in the split lines are affected by the variation on part level, variation in the joining process and the design concept itself. The visual sensitivity of a split line is also important in this context, e.g. if a split line is hidden, its visual quality is not important. In this paper, the ideas from traditional failure mode effect analysis (FMEA) are adapted to a geometry assurance context, where the visual impression of split lines is in focus. The visual sensitivity, as well as the probability of non-nominal outcomes, are included in the analysis. The probabilities of non-nominal outcomes are calculated using advanced non-rigid variation simulation based on Monte Carlo simulation combined with finite element analysis. In this way, all forces and bending due to joining and non-nominal geometries can be included. The goal of the suggested geometrical variation mode effect analysis (GVMEA) is to rank the split lines from the most critical one to the least critical one for the visual quality of a product. This is done by calculating a risk priority number for each split line. In this way, the split lines with the highest risk to impair the visual quality of a product can be identified and hopefully fixed. The method is demonstrated on a ready-to-assemble chest, i.e. on an example from the furniture industry.

Surface texture parameters can provide a link between texture, its processing and function. Recent surveys and industrial experience have shown that the ISO 25178 areal surface texture parameters have not received the level of traction in industry that was predicted when introduced despite the fact that the areal parameters were predicted to have more functionally relevant characterisations than the ISO 4287 profile parameters. The objective of the paper is to enable more functionally relevant specifications of surface texture to be taken up by industry and the scientific community by increasing the knowledge of the ISO 25178 texture and novel feature parameters, and their potential use, as well as knowledge about methods for establishing functionally relevant surface texture specifications. In the paper, existing methods for functional tolerancing of surface texture are reviewed and discussed, examples of applications are given and a direction for continued research is presented.

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.

The quality assurance of dimensions with asymmetric tolerances is as important as for dimensions with symmetric tolerances. A number of methods for calculating corrected capability indices for dimensions with asymmetric tolerances have been suggested, of which all but one involves reassigning the tolerances to become symmetric. As tolerances should be specified in the design phase, not the quality assurance phase the logic of this is flawed. The methods suggested in this article result in the same values for corrected capability as the previously suggested method that does not change the tolerances. This does not mean that one of the two methods are redundant; depending on preference and to some degree the form the data is presented in suggest one is preferred over the other. This article explains how the method suggested was derived and validates the result with computer generated data.

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.

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.

Additive manufacturing (AM) for metals is a widely researched, continuously enhanced manufacturing process and is implemented across various industries. However, the AM process exhibits variation that affects the geometric quality of the end product. The effect of process variation on geometric quality is rarely considered during the design stages. In this paper, sources that influence the geometric quality in a metal AM process are reviewed from a robust design perspective and further sorted into control factors and noise factors. A framework for geometric robustness analysis of AM products is presented as an outcome. This framework would facilitate development of methods and tools to produce geometry assured AM products. Also, the prospects of variation simulation to support geometric robustness analysis and the challenges associated with it are discussed.

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 Finite Element Method (FEM). The sequence, with which the spot welding operation is performed, influences the final geometric deformations of the assembly. Finding the optimal sequence that results in the minimum geometric deformation is a combinatorial problem that is experimentally and computationally expensive. For an assembly with N number of welds, there are N! possible sequences to perform the spot welding operation. Traditionally, spot welding optimization strategies have been to simulate the geometric variation of the spot-welded assembly after the assembly has been positioned in an inspection fixture, using an appropriate measure of variation. In this approach, the calculation of deformation after springback is one of the most time-consuming steps. In this paper, the cause of variation in the deformations after the springback, between different sequences is identified. The relative displacements of the weld points in the assembly fixture, when welded in a sequence, is the source of such behavior. Capturing these displacements leads to large time savings during sequence optimization. Moreover, this approach is independent of the inspection fixture. The relative weld displacements have been evaluated on two sheet metal assemblies. The sequence optimization problem has been solved for the two assemblies using this approach. The optimal sequence, the corresponding final assembly deformations, and the time-consumption have been compared to the traditional approach. The results show a significant correlation between the weld relative displacements in the assembly fixture, and the assembly deformation in the inspection fixture. Considering the relative weld displacement makes each assembly evaluation less time-consuming, and thereby, sequence optimization time can be reduced up to 30%, compared to the traditional approach.

When the geometry of a non-rigid part or pre-assembly is measured fully clamped (over-constrained) in a measurement fixture, the spring-back information and influence from gravity forces are usually lost in the collected data. From the 3D-measurement data, it is hard to understand built in tensions, and the detail’s tendency to bend, twist and warp after release from the measurement fixture. These effects are however important to consider when analyzing each part’s contribution to geometrical deviations after assembly. In this paper a method is presented, describing how free state shape and over-constrained shape of a measured detail can be virtually estimated starting from acquired data when the part or the preassembly is resting on only 3-points. The objective is to minimize the information loss, to spare measurement resources and to allow for a wider use of the collected data, describing the geometry. Part stiffnesses, part to part contacts and gravity effects are considered in the proposed method. The method is based on 3D-scanning techniques to acquire the shape of the measured object. Necessary compensations for part stiffnesses and gravity effects are based upon Finite Element Analysis (FEA) and the Method of Influence Coefficients (MIC). The presented method is applied to an industrial case to demonstrate its potential. The results show that estimated over-constrained shapes show good resemblance with measurements acquired when part is over-constrained in its measurement fixture.