An Evidence-Based Study on Teaching Computer Aided Design in Higher Education during the COVID-19 Pandemic

The pandemic has had a major effect on engineering education, transforming both current and future teaching practice. The physical meetings between student and teacher have during the pandemic been replaced by online contact and recordings of lectures and demonstrations. In this paper, the focus is on computer aided design (CAD) teaching for first-year engineering students. CAD is a topic usually characterized by a close contact by student and teacher, with hands-on instruction at the computer using the CAD software. In the paper, the experiences and learnings from the rapid shift to on-line teaching in CAD are summarized and discussed, and learnings and takeaways for a redesign of future CAD teaching are discussed. Both the students’ learning and their mental wellbeing are evaluated. It is found that on a general level, the students were satisfied with the online teaching and rated it as better or equal to traditional teaching. However, there is still room for improvement, since some students found the situation stressful and pointed out the difficulty to ask questions online. The findings are based on a student survey, existing literature, and the authors own teaching practices during the pandemic

Model-based definition in computer aided tolerance analyses

Recent advancements in means of data collection and utilization have stepped forward to the realization of model-based definition approaches in product and production development processes. This realization is further facilitated by the presentation of open-source standards for model-based definition including STEP AP 242 and QIF 3.0. As a result, engineers are empowered with a significant amount of data to improve the development processes, particularly the tolerancing and metrology processes. This paper evaluates the opportunities enabled in the era of tolerance analyses, particularly computer-aided tolerance analyses by model-based design approaches particularly through the utilization of STEP AP 242 and QIF 3.0 standards. The main breakthrough in these standards is the semantic representation of tolerancing data in the models. However, the existing methods of modeling and analyzing tolerance information are diverse and not all these methods can utilize the new standards in the same manner. The potential usages of the new standards in different tolerance modeling techniques are reviewed. Furthermore, the research gaps and wishes for further improvements in the tolerance analysis era through model-based definition are discussed

Evaluating different strategies to achieve the highest geometric quality in self-adjusting smart assembly lines

Digital twin-driven productions have opened great opportunities to increase the efficiency and quality of production processes. Smart assembly lines are one of these opportunities in which the effects of geometric variations of the mating parts on the assemblies can be minimized. These assembly lines utilize different techniques, including selective assembly and locator adjustments, to improve the geometric quality. This paper signifies that the achievable improvements through these techniques are highly dependent on the utilized fixture layout for the assembly process. Hence, different design methods and productions that can be followed in a smart assembly line are discussed. Furthermore, different scenarios are applied to two industrial sample cases from the automotive industry. The aptest design strategy for each improvement technique is determined. Moreover, the strategy that can result in the highest geometric quality of assemblies through a smart assembly line is defined

Rapid sequence optimization of spot welds for improved geometrical quality using a novel stepwise algorithm

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

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

In an individualized sheet 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 Multistage Approach to the Selective Assembly of Components Without Dimensional Distribution Assumptions

Selective assembly is a means of obtaining higher quality product assemblies by using relatively low-quality components. Components are selected and classified according to their dimensions and then assembled. Past research has often focused on components that have normal dimensional distributions to try to find assemblies with minimal variation and surplus parts. This paper presents a multistage approach to selective assembly for all distributions of components and with no surplus, thus offering less variation compared to similar approaches. The problem is divided into different stages and a genetic algorithm (GA) is used to find the best combination of groups of parts in each stage. This approach is applied to two available cases from the literature. The results show improvement of up to 20% in variation compared to past approaches

Combining selective assembly and individualized locator adjustments techniques in a smart assembly line

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

Geometrical Variation Mode Effect Analysis (GVMEA) for Split Lines

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

Predicting Geometrical Variation in Fabricated Assemblies Using a Digital Twin Approach Including a Novel Non-Nominal Welding Simulation

The aerospace industry faces constantly increasing demands on performance and reliability, especially within the vital area of engine development. New technologies are needed in order to push the limits of high precision manufacturing processes for the next generation of aircraft engines. An increased use of in-line data collection in manufacturing is creating an opportunity to individualize each assembly operation rather than treating them identically. Welding is common in this context, and the interaction between welding distortion and variation in part geometries is difficult to predict and manage in products with tight tolerances. This paper proposes an approach based on the Digital Twin paradigm, aiming to increase geometrical quality by combining the novel SCV (Steady-state Convex hull Volumetric shrinkage) method for non-nominal welding simulation with geometrical data collected from 3D scanning of parts. A case study is presented where two parts are scanned and then welded together into an assembly. The scan data is used as input for a non-nominal welding simulation, and the result of the simulation is compared directly to scan data from the real welded assembly. Three different welding simulation methods are used and assessed based on simulation speed and ability to predict the real welding result. The segmented SCV method for welding simulation shows promising potential for this implementation, delivering good prediction accuracy and high simulation speed