A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Srinivasu, D. S.
- Thermal Error Modeling of Machine Tool Spindle Through an Ensemble Approach
Authors
1 Indian Institute of Technology Madras, Chennai, IN
Source
Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 1 - 9Abstract
Thermal error compensation of machine tool is cost-effective than other methods. Towards this, data-driven machine learning (ML) algorithms have been used to produce accurate prediction models. However, ML models have limitations, such as overfitting, requiring a large data etc. In present work, a hybrid model is proposed by exploiting the linear regression (LR), support vector machine (SVM), neural network (NN), and decision tree (DT) models. For this purpose, the optimum weights to each constituent model is identified by cosine similarity maximization. The developed models are validated against the experimental data. The prediction results with optimized weight are compared with equal weights and the root means square error (RMSE) for both methods are 1.8879 and 2.8978, respectively. The RMSE shows that the hybrid model produces good accuracy for both small and large data sets compared to individual models.
Keywords
Hybrid Model, Cosine Maximization, Thermal Error, Support Vector Machine, Linear Regression, Neural Network
References
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- Li, Z., Li, G., Xu, K., Tang, X., & Dong, X. (2021). Temperature-sensitive point selection and thermal error modeling of spindle based on synthetical temperature information. Intl. Journal of Advanced Manufacturing Technology, 113(3-4), 1029-1043. https://doi. org/10.1007/s00170-021-06680-9
- Lin, C.-J., Su, X.-Y., Hu, C.-H., Jian, B.-L., Wu, L.-W., & Yau, H.-T. (2020). A linear regression thermal displacement lathe spindle model. Energies, 13(4), 949. https://doi.org/10.3390/ en13040949
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- Liu, H., Miao, E., Zhang, L., Li, L., Hou, Y., & Tang, D. (2020). Thermal error modeling for machine tools: Mechanistic analysis and solution for the pseudocorrelation of temperature-sensitive points. IEEE Access, 8, 63497-63513. https://doi. org/10.1109/ACCESS.2020.2983471
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- Approach for Determining the Availability of Machine Tools Based on Skill Level of Operator and Service Personnel
Authors
1 Indian Institute of Technology Madras, Chennai, IN
Source
Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 67 - 72Abstract
Machine tools (MTs) availability is a critical measure for production scheduling. On the other hand, the machine tool availability depends on its reliability (mean time between failure, MTBF) and maintainability (mean time to repair, MTTR), which depends on multiple factors and uncertainties. One can consider the skill level of operators and service personnel that influence MTBF and MTTR of machine tools, which in turn can affect operating cost. The studies on the effect of the skill level of operators and service personnel on MT availability are limited. Generally, MT availability should be higher to maintain reasonable operating costs. In the current work, a multi-objective optimization (MOO) problem was formulated to maximize the MT availability and minimize the MT life cycle cost, considering the skill levels of the MT operators and service personnel. The results of this study help the management identify the skill level of operators and service engineers for maximum MT availability.
Keywords
Availability, Multi-Objective Optimization, Skill Level, Genetic AlgorithmReferences
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- Houshyar, A. (2005). Reliability and maintainability of machinery and equipment, part 2: Benchmarking, life-cycle cost, and predictive maintenance. International Journal of Modelling and Simulation, 25(1), 1-11. https://doi.org/10. 1080/02286203.2005.11442313
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