Journal: Journal of Manufacturing Systems

Abbreviation

J. manuf. syst.

Publisher

Elsevier

Journal Volumes

ISSN

1878-6642
0278-6125

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Publications1 - 8 of 8
  • Methodology for shape optimization of ultrasonic amplifier using genetic algorithms and simplex method
    Item type: Journal Article
    Deibel, Karl-Robert; Wegener, Konrad (2013)
    Journal of Manufacturing Systems
  • Thermal error compensation of rotary axes and main spindles using cooling power as input parameter
    Item type: Journal Article
    Mayr, Josef; Egeter, Michael; Weikert, Sascha; et al. (2015)
    Journal of Manufacturing Systems
  • Sensor placement utilizing a digital twin for thermal error compensation of machine tools
    Item type: Journal Article
    Lang, Sebastian Beat; Zorzini, Mario; Scholze, Stephan; et al. (2025)
    Journal of Manufacturing Systems
    Thermal errors in machine tools significantly impact precision and, therefore, productivity. Mitigating these errors often results in a trade-off between energy efficiency and accuracy. While data-driven compensation models show promise in addressing this challenge and achieving sustainable precision, their effectiveness hinges on the careful selection and placement of sensors as model inputs. This paper introduces a novel temperature sensor positioning method for thermal error compensation that leverages a digital twin framework to virtually determine ideal sensor positions and their effects on the compensation model. By accurately identifying temperature-sensitive points, our approach improves compensation accuracy and reduces the number of sensors required, thus enhancing both model robustness and operational efficiency. For choosing this set not only one simulation model is used but an ensemble with varying boundary conditions and thus model properties. Validation results show that the proposed method outperforms traditional, manually determined sensor placement strategies, providing a scalable solution for adaptable, energy-efficient thermal management in precision manufacturing. The selected sensor set based on a hybrid singular value decomposition and Least Absolute Shrinkage and Selection Operator approach yields a more robust compensation using only 7 instead of the manually chosen 22 temperature sensors. The thermal error reduction ranges from 77%-94% using simulated data with a corresponding reduction of 75%-85% achieved on the physical machine.
  • Augmented reality in smart manufacturing: Enabling collaboration between humans and artificial intelligence
    Item type: Other Journal Item
    Chu, Chih-Hsing; Wang, Lihui; Liu, Shengjun; et al. (2021)
    Journal of Manufacturing Systems
  • Self-optimizing thermal error compensation models with adaptive inputs using Group-LASSO for ARX-models
    Item type: Journal Article
    Zimmermann, Nico; Büchi, Tobias; Mayr, Josef; et al. (2022)
    Journal of Manufacturing Systems
    Thermal errors have a significant impact on the machining accuracy of five-axis machine tools. The thermal adaptive learning control (TALC), which combines adaptable data-based models and on-machine measurements, realizes a precise and robust long-term reduction of thermal errors of machine tools. To further improve the precision and the robustness of data-based models for thermal error compensation, this publication introduces a new method to realize adaptive model inputs. This method combines the Group-LASSO (least absolute shrinkage and selection operator) for autoregressive models with exogenous inputs (ARX) and the particle swarm optimization to realize a simultaneous estimation of the optimal inputs, the model structure, and the model parameters. Additionally, the self-optimization ability of thermal error compensation models, based on the TALC, is increased by introducing error-specific action control limits to define the frequency of model updates. The newly developed methods are applied to compensate the thermal errors of a swiveling and a rotary axis of a five-axis machine tool during a long-term test series of 350 h. Randomly generated speed profiles of the linear and rotary axes as well as the spindle and changing ambient conditions ensure a high variety of thermal load cases within in the analyzed long-term test series. The results show that the prediction accuracy measured as peak-to-peak values and the robustness of the thermal error compensation models are improved by up to 36% and 58% respectively when adaptive instead of static model inputs are used. Furthermore, the compensation results of the new method outperform the previously used sequential input selection method regarding prediction accuracy and repeatability. The average peak-to-peak value of the compensated translational thermal errors is reduced by 23% and the repeatability of the corresponding compensation results is increased by 57%. Consequently, the consideration of the resulting model structure during the selection of the optimal model inputs significantly enhances the performance of the resulting data-based thermal error compensation models.
  • Adaptive learning control for thermal error compensation of 5-axis machine tools
    Item type: Journal Article
    Blaser, Philip; Pavlicek, Florentina; Mori, Kotaro; et al. (2017)
    Journal of Manufacturing Systems
  • Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition
    Item type: Journal Article
    Rokhforoz, Pegah; Fink, Olga (2021)
    Journal of Manufacturing Systems
    Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system’s reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system’s operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.
  • Modeling and evaluation tool for supporting decisions on the design of reconfigurable machine tools
    Item type: Journal Article
    Lorenzer, Thomas; Weikert, Sascha; Bossoni, Sergio; et al. (2007)
    Journal of Manufacturing Systems
Publications1 - 8 of 8