@inproceedings{53959, abstract = {{In light of the growing interest in type inference research for Python, both researchers and practitioners require a standardized process to assess the performance of various type inference techniques. This paper introduces TypeEvalPy, a comprehensive micro-benchmarking framework for evaluating type inference tools. TypeEvalPy contains 154 code snippets with 845 type annotations across 18 categories that target various Python features. The framework manages the execution of containerized tools, transforms inferred types into a standardized format, and produces meaningful metrics for assessment. Through our analysis, we compare the performance of six type inference tools, highlighting their strengths and limitations. Our findings provide a foundation for further research and optimization in the domain of Python type inference.}}, author = {{Shivarpatna Venkatesh, Ashwin Prasad and Sabu, Samkutty and Wang, Jiawei and Mir, Amir M. and Li, Li and Bodden, Eric}}, booktitle = {{Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings}}, isbn = {{9798400705021}}, location = {{Lisbon, Portugal}}, pages = {{49--53}}, publisher = {{Association for Computing Machinery}}, title = {{{TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools}}}, doi = {{10.1145/3639478.3640033}}, year = {{2024}}, } @inproceedings{56207, abstract = {{We present a data acquisition and visualization pipeline that allows experts to monitor additive manufacturing processes, in particular laser metal deposition with wire (LMD-w) processes, in immersive virtual reality. Our virtual environment consists of a digital shadow of the LMD-w production site enriched with additional measurement data shown on both static as well as handheld virtual displays. Users can explore the production site by enhanced teleportation capabilities that enable them to change their scale as well as their elevation above the ground plane. In an exploratory user study with 22 participants, we demonstrate that our system is generally suitable for the supervision of LMD-w processes while generating low task load and cybersickness. Therefore, it serves as a first promising step towards the successful application of virtual reality technology in the comparatively young field of additive manufacturing.}}, author = {{Rupp, Daniel and Kuhlen, Torsten W. and Rarbach, Sven and Wiechel, Dominik and Pottebaum, Jens and Weidemann, Tizia and Tran, Duc Thanh and Day, Robin and Zielinski, Jonas and König, Valentina and Bremer, Jan and Kosche, Thomas and Grimm, Andreas and Bergs, Thomas and Gräßler, Iris and Weissker, Tim}}, booktitle = {{Proceedings of the GI VR/AR Workshop 2024}}, location = {{Hamburg}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Virtual Reality as a Tool for Monitoring Additive Manufacturing Processes via Digital Shadows}}}, doi = {{10.18420/vrar2024_0006}}, year = {{2024}}, } @article{56221, author = {{Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal and Trautmann, Heike and Schütze, Oliver}}, journal = {{IEEE Transactions on Evolutionary Computation}}, keywords = {{Optimization, Evolutionary computation, Approximation algorithms, Benchmark testing, Vectors, Surveys, Pareto optimization, multi-objective optimization, evolutionary computation, multimodal optimization, local solutions}}, pages = {{1--1}}, title = {{{Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}}, doi = {{10.1109/TEVC.2024.3458855}}, year = {{2024}}, } @inproceedings{46649, abstract = {{Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus sparsity. The usual approach is a simple weighting of the criteria, which formally only works in the convex setting. In this paper, we present a Multi-Objective Optimization algorithm using a modified Weighted Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect to several tasks. By employing this scalarization technique, the algorithm can identify all optimal solutions of the original problem while reducing its complexity to a sequence of single-objective problems. The simplified problems are then solved using an Augmented Lagrangian method, enabling the use of popular optimization techniques such as Adam and Stochastic Gradient Descent, while efficaciously handling constraints. Our work aims to address the (economical and also ecological) sustainability issue of DNN models, with a particular focus on Deep Multi-Task models, which are typically designed with a very large number of weights to perform equally well on multiple tasks. Through experiments conducted on two Machine Learning datasets, we demonstrate the possibility of adaptively sparsifying the model during training without significantly impacting its performance, if we are willing to apply task-specific adaptations to the network weights. Code is available at https://github.com/salomonhotegni/MDMTN.}}, author = {{Hotegni, Sedjro Salomon and Berkemeier, Manuel Bastian and Peitz, Sebastian}}, booktitle = {{2024 International Joint Conference on Neural Networks (IJCNN)}}, issn = {{ 2161-4407}}, location = {{Yokohama, Japan}}, pages = {{9}}, publisher = {{IEEE}}, title = {{{Multi-Objective Optimization for Sparse Deep Multi-Task Learning}}}, doi = {{10.1109/IJCNN60899.2024.10650994}}, year = {{2024}}, } @article{54847, abstract = {{The widespread adoption of ultra-high strength steels, due to their high bulk resistivity, intensifies expulsion issues in resistance spot welding (RSW), deteriorating both the spot weld and surface quality. This study presents a novel approach to prevent expulsion by employing a preheating current. Through characteristic analysis of joint formation under critical welding current, the importance of plastic material encapsulation around the weld nugget (plastic shell) at high temperatures in preventing expulsion is highlighted. To evaluate the effect of preheating on the plastic shell and understand its mechanism in expulsion prevention, a two-dimensional welding simulation model for dissimilar ultra-high strength steel joints was established. The results showed that optimal preheating enhances the thickness of the plastic shell, improving its ability to encapsulate the weld nugget during the primary welding phase, thereby diminishing expulsion risks. Experimental validation confirmed that by employing the optimal preheating current, the maximum nugget diameter was enhanced to 9.42 mm, marking an increase of 13.4 % and extending the weldable current range by 27.5 %. Under quasi-static cross-tensile loading, joints with preheating demonstrated a 7.9 % enhancement in maximum load-bearing capacity compared to joints without preheating, showing a reproducible and complete pull-out failure mode within the heat-affected zone. This study offers a prevention method based on underlying mechanisms, providing a new perspective for future research on welding parameter optimization with the aim of expulsion prevention.}}, author = {{Yang, Keke and El-Sari, Bassel and Olfert, Viktoria and Wang, Zhuoqun and Biegler, Max and Rethmeier, Michael and Meschut, Gerson}}, issn = {{1526-6125}}, journal = {{Journal of Manufacturing Processes}}, keywords = {{Expulsion Resistance spot welding Finite element modelling Preheating Weldable current range Ultra-high strength steel}}, pages = {{489--502}}, publisher = {{Elsevier BV}}, title = {{{Expulsion prevention in resistance spot welding of dissimilar joints with ultra-high strength steel: An analysis of the mechanism and effect of preheating current}}}, doi = {{10.1016/j.jmapro.2024.06.034}}, volume = {{124}}, year = {{2024}}, } @article{55654, author = {{Röder, Lilli Sophia and Gröngröft, Arne and Grünewald, Marcus and Riese, Julia}}, issn = {{0098-1354}}, journal = {{Computers & Chemical Engineering}}, publisher = {{Elsevier BV}}, title = {{{Optimization of Design and Operation of a Digestate Treatment Cascade for Demand Side Management Implementation}}}, doi = {{10.1016/j.compchemeng.2024.108838}}, year = {{2024}}, } @inproceedings{56888, abstract = {{Elterliche Unterstützung bei der Internetnutzung geriet durch die Covid-19-Pandemie stärker in den Fokus, auch wenn diese bereits vor der Pandemie aufgrund der nur langsam fortschreitenden Digitalisierung von Schulen elementar war. Neben der Relevanz der Quantität elterlicher Unterstützung ist in Untersuchungen zur Rolle der Familie für das Lernen mit digitalen Medien auch die Qualität von zentraler Bedeutung (Bonanati et al., 2022). Dabei erwies sich allgemein in der Forschung zur elterlichen Hausaufgabenunterstützung eine autonomieunterstützende, strukturgebende und zugleich wertschätzende Instruktion als besonders gewinnbringend (Dumont et al., 2014). Zudem ist bekannt, dass die elterliche Unterstützung unter anderem auf Grund der steigenden Komplexität der Unterrichtsinhalte, der steigenden Selbstständigkeit sowie dem zunehmenden Autonomiebedürfnis der Lernenden im Schulverlauf abnimmt (Luplow & Schneider, 2018). Wie sich die elterliche Unterstützung bei der informationsorientierten Internetnutzung von Kindern über die Zeit und mit Beginn der Covid-19-Pandemie verändert, ist relevant für den Förderkontext, bislang aber nur wenig betrachtet worden und deshalb Ziel der vorliegenden Untersuchung. Grundlage der vorliegenden Untersuchung sind längsschnittliche Daten von 395 Schüler*innen sowie 191 Eltern, die im Jahr 2019/2020 in fünften Klassen (~10-11 Jahre) und im Jahr 2021/22 in siebten Klassen (~12-13 Jahre) erhoben wurden. Die Ergebnisse zeigten eine Abnahme der Quantität elterlicher Instruktion von der fünften zur siebten Jahrgansstufe sowohl aus Eltern- als auch aus Kinderperspektive. Hinsichtlich der Qualität elterlicher Unterstützung berichteten Eltern in der 7. Klassenstufe von weniger autonomieunterstützender Instruktion, während Kinder der 7. Klassenstufe von einer weniger autonomieunterstützenden und wertschätzenden Instruktion berichteten. Weitere Ergebnisse werden in dem Vortrag diskutiert.}}, author = {{Gruchel, Nicole and Kurock, Ricarda and Buhl, Heike M.}}, location = {{Wien}}, title = {{{Digitale häusliche Lernumwelt – Veränderungen der elterlichen Unterstützung bei der informationsorientierten Internetnutzung von Fünft- und Siebtklässler*innen }}}, year = {{2024}}, } @misc{58325, author = {{Autsch, Sabiene}}, publisher = {{Edition Imorde }}, title = {{{Micro Archives. Künstlerische Arbeiten 2019-2024. }}}, year = {{2024}}, } @inproceedings{58335, author = {{Seiler, Moritz and Skvorc, Urban and Doerr, Carola and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers}}, editor = {{Festa, Paola and Ferone, Daniele and Pastore, Tommaso and Pisacane, Ornella}}, pages = {{361–376}}, publisher = {{Springer}}, title = {{{Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection}}}, doi = {{10.1007/978-3-031-75623-8_29}}, volume = {{14990}}, year = {{2024}}, } @unpublished{58441, abstract = {{This study presents a numerical approach using a 3D finite element model to quantify the remaining clamp load of a plastic nut joint after a specific time. The viscoelastic relaxation of a thermoplastic nut, which is predominantly screwed on a welding stud, is described by a material card using Prony Series. Prony Series are derived from experimental Dynamical Mechanical Analysis with different moisture and fiber contents of the thermoplastic. Since plastic nuts usually do not have preformed threads, the increased temperatures and resulting stresses from the thread-forming process are considered in the simulation. Firstly, the FE model is verified by substrate stress relaxation tests. Subsequently, experimental clamp load measurements with miniature compression load cells verify the clamp load prediction. Finally, the developed model is used to analyze the clamp load distribution within the threads}}, author = {{Wippermann, Jan and Meschut, Gerson}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Numerical modeling of clamp load relaxation of plastic nuts under varying moisture and fiber content}}}, year = {{2024}}, } @phdthesis{58448, abstract = {{Die Inbetriebnahme von Steuerungen und Regelungen stellt sicher, dass ein mechatronisches System ordnungsgemäß funktioniert und den Anforderungen gerecht wird. Der modellbasierte Entwurf basiert auf einem genauen Simulationsmodell. Allerdings ist dieser klassische Weg bei komplexen Systemen oft nicht praktikabel, da die analytische Modellierung zu kompliziert und zeitaufwendig ist. Diese Forschungslücke wird durch Verfahren adressiert, die eine effiziente und sichere Inbetriebnahme ermöglichen. Diese Verfahren kombinieren Regelungstechnik und Reinforcement Learning und nutzen vorhandenes Wissen über die Regelungsaufgabe, um Korrekturen basierend auf Messdaten und der probabilistischen Gauß-Prozess-Regression vorzunehmen. Das Vorwissen kann als teilweise bekanntes physikalisches Modell oder als Steuerungsfunktion vorliegen. Anwendungsbeispiele sind der Ultraschalldrahtbondprozess, verschiedene Pendelsysteme und ein Hexapod. Eine angepasste Bayessche Optimierung wird zur Identifikation einer Steuerparametrisierung für das Ultraschallbonden eingesetzt. Außerdem wird eine hybride Optimalsteuerung für das Doppelpendel auf einem Wagen entwickelt und erfolgreich validiert. Fur einen Hexapod zur Fahrzeugachsprüfung wird eine hybride Zustandslinearisierung formuliert und ein Funktionsnachweis im Rahmen einer Simulation erbracht. Die Einhaltung technischer Rahmenbedingungen und stabiles Systemverhalten werden durch probabilistische Pradiktionen gewährleistet. In allen Anwendungsfällen wird eine Steigerung der Effizienz und Güte erzielt.}}, author = {{Hesse, Michael}}, isbn = {{978-3-947647-45-3}}, issn = {{2365-4422}}, publisher = {{Heinz Nixdorf Institut}}, title = {{{Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression}}}, doi = {{10.17619/UNIPB/1-2135}}, volume = {{426}}, year = {{2024}}, } @article{58491, abstract = {{Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.}}, author = {{Friedlein, Johannes and Böhnke, Max and Schlichter, Malte and Bobbert, Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}}, issn = {{2504-4494}}, journal = {{Journal of Manufacturing and Materials Processing}}, keywords = {{ductile damage, stress-state dependency, failure, parameter identification, punch test, clinching}}, number = {{4}}, publisher = {{MDPI AG}}, title = {{{Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining}}}, doi = {{10.3390/jmmp8040157}}, volume = {{8}}, year = {{2024}}, } @inproceedings{59237, abstract = {{Batch and process fluctuations during the fabrication of sheet metal components result in discrepancies in the resulting component properties, affecting subsequent process steps and potentially leading to production rejects. Consequently, the identification of deviations and knowledge of the effects of fluctuations are crucial for achieving consistently high product quality, reducing waste and thus increasing resource efficiency of production processes through countermeasures derived from this. The approach presented to address this is the use of data-driven metamodeling to map entire process chains and predict process parameters in order to compensate for process and batch fluctuation. The investigated process chain consists of the sub-processes deep drawing, clamping and clinching. For each process step, relevant input and output variables are identified, numerical simulation models are created, and subsequently validated. Variant simulations of the sub-processes are conducted and evaluated to generate a database for the metamodeling of the individual process steps. Machine learning techniques are utilized for the automated selection and optimization of learning methods to create models that depict the relationships between input and output variables. Finally, the models for the sub-processes are linked together to form a superordinate metamodel for the entire process chain, with the aim to make inline-process adaptations possible.Abstract Purpose Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ($$\dot{V}$$ V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict $$\dot{V}$$ V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for $$\dot{V}$$ V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of $$\dot{V}$$ V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion Machine learning models predict $$\dot{V}$$ V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number DRKS00031401 (6 March 2023, retrospectively registered). }}, author = {{Wenzel, Charlotte and Liebig, Thomas and Swoboda, Adrian and Smolareck, Rika and Schlagheck, Marit Lea and Walzik, David and Groll, Andreas and Goulding, Richie P. and Zimmer, Philipp}}, issn = {{1439-6319}}, journal = {{European Journal of Applied Physiology}}, number = {{11}}, pages = {{3421--3431}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features}}}, doi = {{10.1007/s00421-024-05543-x}}, volume = {{124}}, year = {{2024}}, } @article{60176, abstract = {{AbstractAimTo investigate the associations of the Dietary Approaches to Stop Hypertension (DASH) score with subcutaneous (SAT) and visceral (VAT) adipose tissue volume and hepatic lipid content (HLC) in people with diabetes and to examine whether changes in the DASH diet were associated with changes in these outcomes.MethodsIn total, 335 participants with recent‐onset type 1 diabetes (T1D) and type 2 diabetes (T2D) from the German Diabetes Study were included in the cross‐sectional analysis, and 111 participants in the analysis of changes during the 5‐year follow‐up. Associations between the DASH score and VAT, SAT and HLC and their changes were investigated using multivariable linear regression models by diabetes type. The proportion mediated by changes in potential mediators was determined using mediation analysis.ResultsA higher baseline DASH score was associated with lower HLC, especially in people with T2D (per 5 points: −1.5% [−2.7%; −0.3%]). Over 5 years, a 5‐point increase in the DASH score was associated with decreased VAT in people with T2D (−514 [−800; −228] cm3). Similar, but imprecise, associations were observed for VAT changes in people with T1D (−403 [−861; 55] cm3) and for HLC in people with T2D (−1.3% [−2.8%; 0.3%]). Body mass index and waist circumference changes explained 8%‐48% of the associations between DASH and VAT changes in both groups. In people with T2D, adipose tissue insulin resistance index (Adipo‐IR) changes explained 47% of the association between DASH and HLC changes.ConclusionsA shift to a DASH‐like diet was associated with favourable VAT and HLC changes, which were partly explained by changes in anthropometric measures and Adipo‐IR.}}, author = {{Schaefer, Edyta and Lang, Alexander and Kupriyanova, Yuliya and Bódis, Kálmán B. and Weber, Katharina S. and Buyken, Anette and Barbaresko, Janett and Kössler, Theresa and Kahl, Sabine and Zaharia, Oana‐Patricia and Szendroedi, Julia and Herder, Christian and Schrauwen‐Hinderling, Vera B. and Wagner, Robert and Kuss, Oliver and Roden, Michael and Schlesinger, Sabrina}}, issn = {{1462-8902}}, journal = {{Diabetes, Obesity and Metabolism}}, number = {{10}}, pages = {{4281--4292}}, publisher = {{Wiley}}, title = {{{Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is associated with lower visceral and hepatic lipid content in recent‐onset type 1 diabetes and type 2 diabetes}}}, doi = {{10.1111/dom.15772}}, volume = {{26}}, year = {{2024}}, } @inproceedings{64104, author = {{Scheideler, Christian and Hinnenthal , Kristian and Liedtke, David Jan}}, title = {{{Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures. SAND 2024: 15:1-15:20}}}, year = {{2024}}, } @inproceedings{64106, author = {{Scheideler, Christian and Kostitsyna, Irina and Liedtke, David Jan}}, title = {{{Universal Coating by 3D Hybrid Programmable Matter.}}}, year = {{2024}}, } @article{64002, abstract = {{The production of formaldehyde on industrial scale requires huge amounts of energy due to the involvement of reforming processes in combination with the demand in the megaton scale. Hence, a direct route for the transformation of (bio)methane to formaldehyde would decrease costs and puts less pressure on the environment. Herein, we report on the use of zinc modified silicas as possible support materials for vanadium catalysts and the resulting consequences for the performance in the selective oxidation of methane to formaldehyde. After optimization of the Zn content and reaction conditions, a remarkably high space-time yield of 12.4 kgCH2O·kgcat−1·h−1 was achieved. As a result of the extensive characterization by means of UV–vis, Raman, XANES and NMR spectroscopy it was found that vanadium is in the vicinity of highly dispersed zinc atoms which promote the formation of active vanadium species as supposed by theoretical calculations. This work presents a further step of catalyst development towards direct industrial methane conversion which may help to overcome current limitations in the future.}}, author = {{Kunkel, Benny and Seeburg, Dominik and Kabelitz, Anke and Witte, Steffen and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky, Gerd and Buzanich, Ana Guilherme and Wohlrab, Sebastian}}, journal = {{Catalysis Today}}, keywords = {{Formaldehyde, Local coordination, SBA-15, Vanadium oxo species, XANES, Zinc doped silica}}, pages = {{114643}}, title = {{{Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane}}}, doi = {{10.1016/j.cattod.2024.114643}}, volume = {{432}}, year = {{2024}}, } @inproceedings{58223, abstract = {{The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields an optimal approximation of any game via a weighted least square (WLS) objective, an extension of this result to SII has been a long-standing open problem, which even led to the proposal of an alternative index. In this work, we characterize higher-order SII as a solution to a WLS problem, which constructs an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation for the SV and pairwise SII and give empirically validated conjectures for higher orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions.}}, author = {{Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}}, booktitle = {{Proceedings of the 41st International Conference on Machine Learning (ICML)}}, pages = {{14308–14342}}, publisher = {{PMLR}}, title = {{{KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}}}, volume = {{235}}, year = {{2024}}, } @article{61834, abstract = {{Abstract3D printing or additive manufacturing (AM) possesses enormous potential to benefit the manufacturing industry. Presently, rotary draw bending (RDB) is one of the most commonly used cold-forming industrial process for bending metal tubes. Pressure die is a fundamental forming tool in RDB processes, and it is conventionally made by various grades of comparatively expensive alloy steels. This research presents a novel design of a pressure die which can be 3D printed by using inexpensive polymeric filaments. In this research paper, the 3D-printed pressure die is named as “FFF-pressure die.” The material used to fabricate the FFF-pressure die is a thermoplastic polymer known as “ecoPLA.” The mechanical properties of ecoPLA are studied in relation to the process conditions of a RDB process. Firstly, an initial feasibility of using the FFF-pressure die in a RDB process is obtained by conducting a quick static stress analysis with actual process conditions. After initial feasibility, a complete RDB process is developed and simulated with actual process conditions and material properties. The FFF-pressure die is then practically fabricated by FFF 3D printer and experimentally tested on an industrial RDB machine. The results of practical experiments are compared with the simulation results. In order to make a comparison of the FFF-pressure die with the conventional metal pressure die, the simulation and practical process is also conducted with the conventional metal pressure die. A performance and cost comparison is made between the polymeric FFF-pressure die and the conventional metal pressure die. Von Mises stresses, contact forces, failure risk, and elastic deformations are analyzed. The advantages and limitations of using the FFF-pressure die in a RDB process are discussed in the end. This research intends to widen the avenue of using cost-effective and lightweight forming tools in metal forming industries.}}, author = {{Kaleem, Muhammad Ali and Steinheimer, Rainer and Frohn-Sörensen, Peter and Gabsa, Steffen and Engel, Bernd}}, issn = {{0268-3768}}, journal = {{The International Journal of Advanced Manufacturing Technology}}, number = {{3-4}}, pages = {{1789--1804}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Additive manufacturing of polymeric pressure die for rotary draw bending process}}}, doi = {{10.1007/s00170-024-14221-3}}, volume = {{134}}, year = {{2024}}, }