Journal: Manufacturing Letters

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Publisher

Elsevier

Journal Volumes

ISSN

2213-8463

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2020 - 20246
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Publications 1 - 6 of 6
  • Kalman filter-driven state observer for thermal error compensation in machine tool digital twins
    Item type: Journal Article
    Lang, Sebastian Beat; Talleri, Sofia; Mayr, Josef; et al. (2024)
    Manufacturing Letters
    Sustainable reduction of thermal errors during production is the key challenge in modern high-precision manufacturing. Numerical compensation models provide an energy-efficient solution, but in the case of data-driven models, high-quality experimental data must be time-consuming and expensive to produce, negatively impacting overall productivity. Furthermore, robustness concerns arise in the case of new operating conditions, which were not contained in the training data. This paper presents a novel use of a Kalman filter together with model order reduced finite element models to observe the entire thermal state, which allows the subsequent solution of the mechanical model and computation of the thermal errors in real-time without requiring any training data but instead purely based on the physical system model. The effectiveness of this approach is evaluated using experiments on a thermal test bench with 16 out of 40 temperature sensors employed for observation and demonstrated on a 5-axis machine tool (MT) with 13 out of 25 temperature sensors used. Due to the combination of the reduced order model and Kalman filter these 13 temperature sensors are sufficient to represent a MT mesh of more than 350’000 elements. The entire temperature profile of the thermal test bench is reconstructed to achieve a root mean square error (RMSE) of the unobserved temperature sensors of only 2.7 °C, which accounts for more than 83% of all temperature variations and 1.3 °C for the 5-axis MT. For the thermal error of the thermal test bench, the RMSE could be reduced from 67.4μm to 33.3μm, corresponding to a reduction of 52.7 %. This could be achieved without the need for experimental data for model calibration, in a real-time capable physics-based model.
  • RLTube: Reinforcement learning based deposition path planner for thin-walled bent tubes with optionally varying diameter manufactured by wire-arc additive manufacturing
    Item type: Journal Article
    Petrik, Jan; Bambach, Markus (2024)
    Manufacturing Letters
    This study presents RLTube, an algorithm that uses reinforcement learning (RL) to compute the deposition path for thin-walled bent tubes produced by wire-arc additive manufacturing. Rigid mathematical rules are used by state-of-the-art methods and the developed Brute Force Approach (BFA) to achieve this goal. In contrast, RLTube offers greater flexibility, adaptability and efficiency. This RL-based architecture uses 2D images of bent tubes as input, eliminating the need for additional feature extraction steps. As a result, RLTube deposition paths outperform BFA in terms of the developed evaluation criteria reflecting their quality.
  • An action-oriented teaching approach for intelligent and energy efficient precision manufacturing
    Item type: Conference Paper
    Zimmermann, Nico; Mayr, Josef; Wegener, Konrad (2022)
    Manufacturing Letters ~ 50th SME North American Manufacturing Research Conference (NAMRC 50,2022)
    Thermal errors of machine tools are a major contributor to inaccuracies of produced workpieces. Especially, the reduction of thermal errors without increasing the energy consumption of the machine tool and the shop floor due to exact air conditioning has a great social, scientific, and industrial relevance. Therefore, the Institute of Machine Tools and Manufacturing (IWF) at ETH Zürich set up a series of three lab courses to increase the awareness of undergraduate students on this important topic. In the first lab course the students elaborate how the carbon footprint of machine tools can be minimized over the whole live cycle including the manufactured products. Specifically, the students measure the energy demand and analyze the energy efficiency of the most important components of a 5-axis machine tool in different operating conditions. The second lab course focuses on the thermal chain of causes, which describes the physical fundamentals leading to the thermal deformations of machine tools. Temperature and displacement measurements as well as finite-element simulations of a purpose-built test bench visualize the characteristics of the thermal chain of causes. The third lab course completes the topic by dealing with model-based thermal compensation strategies, which enables a shift from resource-based towards intelligence-based reduction strategies for thermal errors. The students evaluate the thermal behaviour of a 5-axis machine tool with an on-machine measurement cycle and learn how to create physical and data-based thermal error compensation models.
  • Composite additive manufacturing of morphing aerospace structures
    Item type: Journal Article
    Fasel, Urban; Keidel, Dominic; Baumann, Leo; et al. (2020)
    Manufacturing Letters
  • Too hot to print, too slow to handle; finding optimal path characteristics for WAAM
    Item type: Journal Article
    Sideris, Iason; Petrik, Jan; Bambach, Markus (2024)
    Manufacturing Letters
    Wire + Arc Additive Manufacturing (WAAM) is recognized as a highly capable metal additive process. However, the quality of manufactured parts is often compromised by defects stemming from process-induced temperature fields and transient weld-bead shapes, largely due to suboptimal path planning. Recent efforts have predominantly focused on optimizing either the thermal aspects or the productivity of WAAM, but the interplay between these factors and the final geometric accuracy of the parts has not been thoroughly investigated. This study introduces an automated framework for path planning optimization in WAAM, effectively bridging the gap between temperature control, geometric accuracy, and productivity. The approach utilizes a reinforcement learning agent to generate paths that minimize deposition time. These paths are subsequently segmented and optimized for their temperature response using a Monte Carlo tree search algorithm, with the thermal effects efficiently approximated through a reduced-order model. The paper presents a comparative analysis of various deposition strategies, offering insights and recommendations to enhance both productivity and part quality in WAAM path planning.
  • Interactive mixed reality live streaming technology in manufacturing
    Item type: Journal Article
    Netland, Torbjörn; Stegmaier, Michael; Primultini, Cesare; et al. (2023)
    Manufacturing Letters
    This paper presents the first field-tested application of interactive mixed reality live streaming technology in a manufacturing setting and elaborates on its implications for practitioners and scholars. A proof-of-concept of a mixed reality live streaming device that uses 360° video calls for remote presence is developed and applied in a field test during a tool changeover at a Stanley Black & Decker factory. This study documents that interactive mixed reality live streaming for shop-floor environments has passed Technology Readiness Level 6, representing a radical new way of organizing remote shop-floor tours, inspections, audits, and training.
Publications 1 - 6 of 6