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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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PublicationImproved lifetime estimation of shot-peened shaft bores using a numerical approach( 2024)
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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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PublicationProzessoptimierung beim Glaswafer-Trennschleifen( 2023)Fast-Axis-Kollimatoren (FAC) sind essenzielle optische Elemente für Diodenlasersysteme. Beim aktuellen Prozess des Trennschleifens mit nachgelagerter Reinigung von FAC-Optiken aus gepressten antireflexionsbeschichteten Glaswafern entstehen vermehrt Beschädigungen, die eine Verwendung der Optiken limitiert. Die Verwendung von Schneidfolie zur Substratfixierung beim Trennschleifen der FAC-Optiken ermöglicht ein defektfreies Schneiden und Lösen ohne Reinigung von der Folie und gleichzeitig können Kosten und Fertigungszeit eingespart werden.
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PublicationInfluence of heat treatment and densification on the load capacity of sintered gears( 2023)The powder metallurgical manufacturing of gears offers a promising opportunity in terms of reducing the noise emission and increasing the power density. Sintered gears weigh less than conventional gears and potentially have a better noise-vibration-harshness behaviour, due to the remaining porosity. However, the potential of sintered gears for highly loaded applications is not fully utilised yet. Six variants of surface densified and case-hardened sintered gears from Astaloy Mo85 are tested to analyse the impact of the densification and case hardening depths on both the tooth root and flank load bearing capacities. Experimental investigations including metallography and computer tomography are carried out to characterise the microstructure. Furthermore, a simulation model is developed to quantitatively describe the residual stress and hardness profiles after the heat treatment. The load bearing capacity was improved by increasing the densification and case hardening depths, where the effect of the case hardening was identified to be predominant.
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PublicationMethodology for the selection of manufacturing technology chains based on ecologic and economic performance indicators( 2023)When manufacturing products, it becomes increasingly important to consider ecological factors in addition to conventional key performance indicators such as manufacturing costs, time or quality. The greatest leverage for creating the most sustainable form of product manufacture is in the early stages of technology planning, because emissions can be avoided in the long term through the selection of sustainable technologies. However, there are methods that allow ecological factors to be taken into account during technology planning to supplement the usual key performance indicators, but the level of detail and the applicability are not yet sufficient. In order to support decision makers in the planning and evaluation of technology chains, this paper develops a methodology that supports technology chain selection. For this purpose, an economic-ecological key performance indicator was developed which combines manufacturing costs, material flow costs and environmental impacts determined through life cycle assessment to enable a holistic view.
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PublicationModel for tool wear prediction in face hobbing plunging of bevel gears( 2023)This paper deals with tool wear investigations for face hobbing plunging of bevel gears. Initially, the influence of process parameters and tool geometry on tool wear is analyzed both in cutting trials and with the help of the manufacturing simulation BEVELCUT. Subsequently, a tool wear model is presented. Input parameters into the model are tool and workpiece data as well as process parameters and chip characteristics. Through the manufacturing simulation BevelCut, which is based on a planar penetration algorithm, chip characteristics such as the maximum chip thickness hcu,max are calculated resolved in time and location along the blade's cutting edge. Combined with the local cutting speed, the chip characteristics are used to determine the thrust force, which is required to calculate the elastic workpiece deformation. The model coefficients are calibrated by multi-variable regression analysis using the results of the cutting trials and simulation results. The quality of the regression is determined with the help of equivalence tests. For verification, the process parameters and tool geometry are varied in series production and the tool wear is assessed. Finally, the modeled tool wear is compared to the measured tool wear.
