Universität Stuttgart
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Item Open Access Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm(2024) Albus, Marcel; Hornek, Timothée; Kraus, Werner; Huber, Marco F.Assembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to ∼2.75 ×, reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.Item Open Access Machine learning-driven multi-objective parameter optimization for sustainable, efficient, and high-quality ultrasonic wire bonding(2025) Buchner, Christoph; Riedle, Benjamin; Krauß, Jonas; Seidler, Christian T.; Huber, Marco F.; Eigenbrod, Hartmut; von Ribbeck, Hans-Georg; Schlicht, FranzUltrasonic wire bonding, a highly automated production process, finds extensive use in the electronics and electromobility sectors, with billions of applications annually. Wire bonding, a critical step in electrical manufacturing, demands high quality while rising energy costs push industries to improve efficiency. The complexity of the process and the multitude of non-linear influencing parameters in the bonding process make it difficult for engineers to quickly find the optimum parameter sets for multiple response problems simultaneously solely based on their experience. As a result, engineers commonly resort to iterative trial and error approaches to establish wire bond parameters in practice. This paper introduces a novel, machine-learning-based methodology using established optimization algorithms for automated multi-objective parameter optimization in ultrasonic copper wire bonding, considering ten key parameters that influence the normal force profile, ultrasonic power profile, and process duration. The novelty of the proposed method lies in its ability to improve process sustainability by reducing energy input and tool wear, while simultaneously maximizing bond quality (shear force) and minimizing process time, without the need for a physical model or prior process knowledge. The paper shows that the combination of Bayesian optimization with artificial neural networks is particularly effective, achieving a 3.7% reduction in energy input and a 14.4% reduction in process time, while maintaining bond quality and reducing tool wear. This approach proves to be faster, less resource-intensive, and more effective than manual optimization methods, offering a scalable solution for industrial use.Item Open Access Machine learning-based shear force quality prediction of ultrasonic wire bonds : utilizing process data and machine data without additional sensors(2024) Buchner, Christoph; Seidler, Christian T.; Huber, Marco F.; Eigenbrod, Hartmut; Ribbeck, Hans-Georg von; Schlicht, FranzUltrasonic wire bonding is a highly automated production process that is used billions of times a year in the electronics and electromobility industries. Due to the complexity of the process and the large number of influencing parameters, there are currently no automated methods that can be used without additional sensors to evaluate the shear force bond quality quantitatively and non-destructively with sufficiently high precision. For this reason, this paper presents a new methodology that uses machine learning to enable quantitative, non-destructive prediction of bond quality without additional sensors. For this purpose, a machine learning algorithm was developed that uses various machine data and process data from existing sensors to quantitatively predict the shear force of the bonded joint. In addition, features are extracted from process time series, such as current, power, and frequency of the ultrasonic generator as well as deformation during bonding. Of the total of 2,090 features considered, the number of features could be reduced to 26 by recursive feature elimination, while maintaining the prediction accuracy. By using optimized deep neural networks, on average, a prediction precision of the regression on the shear force of the source bond of over 89.6% R 2 -score and a mean absolute error of 241 cN can be achieved.Item Open Access Smart nesting : estimating geometrical compatibility in the nesting problem using graph neural networks(2023) Abdou, Kirolos; Mohammed, Osama; Eskandar, George; Ibrahim, Amgad; Matt, Paul-Amaury; Huber, Marco F.Reducing material waste and computation time are primary objectives in cutting and packing problems (C &P). A solution to the C &P problem consists of many steps, including the grouping of items to be nested and the arrangement of the grouped items on a large object. Current algorithms use meta-heuristics to solve the arrangement problem directly without explicitly addressing the grouping problem. In this paper, we propose a new pipeline for the nesting problem that starts with grouping the items to be nested and then arranging them on large objects. To this end, we introduce and motivate a new concept, namely the Geometrical Compatibility Index (GCI). Items with higher GCI should be clustered together. Since no labels exist for GCIs, we propose to model GCIs as bidirectional weighted edges of a graph that we call geometrical relationship graph (GRG). We propose a novel reinforcement-learning-based framework, which consists of two graph neural networks trained in an actor-critic-like fashion to learn GCIs. Then, to group the items into clusters, we model the GRG as a capacitated vehicle routing problem graph and solve it using meta-heuristics. Experiments conducted on a private dataset with regularly and irregularly shaped items show that the proposed algorithm can achieve a significant reduction in computation time (30% to 48%) compared to an open-source nesting software while attaining similar trim loss on regular items and a threefold improvement in trim loss on irregular items.Item Open Access A survey on learning-based robotic grasping(2020) Kleeberger, Kilian; Bormann, Richard; Kraus, Werner; Huber, Marco F.This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provided. Model-free approaches are attractive due to their generalization capabilities to novel objects, but are mostly limited to top-down grasps and do not allow a precise object placement which can limit their applicability. In contrast, model-based methods allow a precise placement and aim for an automatic configuration without any human intervention to enable a fast and easy deployment. Both approaches to robotic grasping and manipulation with and without object-specific knowledge are discussed. Due to the large amount of data required to train AI-based approaches, simulations are an attractive choice for robot learning. This article also gives an overview of techniques and achievements in transfers from simulations to the real world.