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PublicationAssessing the Environmental and Economic Impact of Wire-Arc Additive Manufacturing( 2024-05)
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PublicationData-driven indirect punch wear monitoring in sheet-metal stamping processes( 2024)The wear state of the punch in sheet-metal stamping processes cannot be directly observed, necessitating the use of indirect methods to infer its condition. Past research approaches utilized a plethora of machine learning models to infer the punch wear state from suitable process signals, but have been limited by the lack of industrial-grade process setups and sample sizes as well as their insufficient interpretability. This work seeks to address these limitations by proposing the sheared surface of the scrap web as a proxy for the punch wear and modeling its quality from acoustic emission signals. The experimental work was carried out in an industrial-grade fine blanking process setting. Evaluation of the model performances suggests that the utilized regression models are capable of modeling the relationship between acoustic emission signal features and sheared surface quality of the scrap webs. Subsequent model inference suggests adhesive wear on the punch as a root cause for the sheared surface impairment of the scrap webs. This work represents the most extensive modeling effort on indirect punch wear monitoring in sheet-metal stamping both from a model prediction and model inference perspective known to the authors.
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PublicationNumerical Modeling of the Redistribution of Residual Stresses in Deep Rolled Cross Bores in Shafts from GJS700-2( 2024)
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PublicationImproved lifetime estimation of shot-peened shaft bores using a numerical approach( 2024)
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PublicationInfluence on the Bead Geometry in Laser Metal Deposition with Wire( 2023-09-28)Laser metal deposition with wire (LMD-w) is a promising additive manufacturing technology, which attracts interest due to the low waste of material, the flexible application possibilities along the production chain and the improved metallurgical properties compared to powder-based processes. However, the complex handling of the technology and the resulting low process stability inhibit the broad industrial application. In particular, the varying bead geometry prevents automation and series production. To improve the geometric accuracy, it is necessary to understand influencing parameters. For this purpose, a parameter study is carried out in the present work. Different combinations of laser power, wire feed rate, traverse speed and welding angle are set, and the deposited beads are evaluated in terms of height and width. A factorial design experiment with the Box-Behnken was used to analyse and understand the interaction of these parameters on the deposited beads.
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PublicationDigital twins for cutting processes( 2023)Collecting and utilizing data in industrial production are becoming increasingly important. One promising approach to utilize data is the concept of digital twin (DT). DTs are virtual representations of physical assets, updated by real data and enhanced by models. This paper provides an overview of DTs for cutting processes. After giving a definition, we discuss requirements derived from representative use cases. As process models are central for DT creation, we present an overview of the latest research as well as conditions for how it can be implemented in industrial environments. The paper concludes with main challenges for future research.
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PublicationPotential of prediction in manufacturing process and inspection sequences for scrap reduction( 2023)With increasing quality requirements, especially for safety-critical products such as in medical technology, manufacturing companies face the challenge, that the quality inspection of component characteristics may become a crucial factor for economic production. For increasing the ratio of correctly identified scrap without increasing the amount of physical inspection, prediction models pose a valuable tool. Prediction models in manufacturing build process models through process data collected during the manufacturing process. The prediction models can for example be used to predict the product quality from the process data, which enables the substitution of physical quality inspections through virtual inspections. Before selecting suitable prediction models, checkpoints in the manufacturing process and inspection sequence (MPIS) for integrating the prediction models profitably have to be identified. Therefore, the paper aims to provide a methodology to generate integrated quality strategies for MPIS that combine physical inspection with the prediction of quality characteristics. Furthermore, a methodology for the economic evaluation of the quality strategies as decision support is provided. Since the number of prediction models available for predicting quality based on process data is manifold, the explicit recommendation for the implementation of a certain prediction model is not the focus of this paper. The methodology presented rather focuses on the identification of suitable checkpoints in the MPIS for prediction model integration. For generating quality strategies suitable checkpoints in the manufacturing process sequence have to be identified. Therefore, three aspects are considered, the influence of the manufacturing process on the change in a characteristic and the overall importance of a characteristic for the component function as well as the ability of the characteristic and process for being predicted. A prediction recommendation is calculated that enables the derivation of quality strategies as a combination of physical and virtual inspection for different characteristics in different checkpoints. For the economic evaluation of the derived quality strategies two cases for supplementing physical inspection with prediction models to either compensate for the alpha- or beta-error rate are presented. The most reasonable quality strategy is chosen according to its economic potential. For validation, the applicability of the methodology for integrating prediction models in manufacturing process and inspection sequences to a use case of medical technology is demonstrated successfully. It is shown that the methodology supports users in identifying possible quality strategies and their economic evaluation for manufacturing process and inspection sequences as a basis for the decision regarding quality strategies to be implemented.
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PublicationData-driven model for process evaluation in wire EDM( 2023)To digitalize the wire EDM process, data-driven models are necessary for evaluating its performance. This presents a challenge due to the high volume of data and the stochastic nature of the process. In this paper, electrical parameters are measured and processed by an FPGA (field programmable gate array) system to recognize and characterize temporally and spatially resolved single discharges as either normal or abnormal. Supervised machine learning methods such as artificial neural networks (ANN) are used and models are trained with different data sets to predict the machined geometrical accuracy and cutting speed based on recorded process data.
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PublicationReduction of Taper Angle and Jet Trailback in Waterjet Cutting of Complex Geometries by a Revised Model of the Process Control( 2023)The high-pressure waterjet is a flexible and powerful tool for machining of high-performance products with reduced manufacturing time and costs. However, waterjet machining of complex geometries is difficult to handle because of the complication in controlling and adjusting the process. Therefore, the goal of this study is to improve a process control method to adjust the waterjet tool orientation and to optimize the waterjet cutting process in a simple and efficient manner. As a result, a method is developed which is based on constant feed rate and a distinction between concave and convex curvature of the workpiece geometry.
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PublicationFine blanking of pre-hardened high manganese steel to investigate the sheared surface hardening and part quality( 2023)Fine blanking is a highly productive process for manufacturing of high accuracy sheet metal parts with functional surfaces. The specific process characteristic leads to high forming in the shear zone and an associated strain hardening of the sheared functional surfaces. Utilization of the process-immanent sheared surface hardening can reduce time and resources of downstream heat treatment processes such as case hardening. High Manganese Steels (HMnS) are characterized by a high strain hardening capacity due to the deformation mechanisms of twinning and transformation induced plasticity occurring during forming. As a result of high tensile strengths, HMnS are suitable as lightweight materials, but often exhibit a relatively low yield strength in terms of structural design features. One approach for increasing the strength values without changing the alloy design is a forming-induced strain hardening of the semi-finished sheet metal by means of upsetting. Therefore, this paper deals with an experimental investigation of the influence of pre-hardening on the blanked part properties during fine blanking of HMnS X40MnCrAlV19-2 LY (1.7401). For this purpose, sheet blanks were strain hardened by means of flat coining and subsequently fine blanked with an analog geometry representing tribologically stressed functional surfaces. Relevant functional surfaces were then analyzed by means of microhardness measurements with regard to the sheared surface hardening as well as characterized in terms of the quality-determining attributes die roll and clean-shear area. Due to the deformation mechanism of twinning, fine blanking of pre-hardened HMnS resulted in a combination of process-immanent high sheared surface hardening and increased yield strength with simultaneous optimal functional surface quality.
