RESEARCH ON THE DETERMINATION OF THE ENERGY CHARACTERISTICS OF A MILLING MACHINE

Vlad GHEORGHIȚĂ

Abstract


The most important criteria on the basis of which the optimal cutting regimes are determined are the criterion of maximum productivity and the criterion of minimum processing cost. When determining the cutting regimes based on these criteria, the problem in the efficiency with which the machine-tool works and the consumption of energy for cutting is not often raised. The lack of knowledge of the connection between the working parameters of the machine-tool and its efficiency, as well as the effects of the operation of the machine-tools at the highest possible efficiency on the specific consumption of electrical energy and the productivity of cutting constitute an explanation of how to approach this problem. In this paper, the energy characteristics of a milling machine are determined, optimized by creating a loading device for the main shaft and analyzed by different types of regression.

Full Text:

PDF

References


Elbah, M., Fnides, B., Laouici, H., Yallese, M., Modelisation et Optimisation des Conditions de Coupe en Tournage Dur par la Technique de Taguchi en Utilisant la MSR, U.P.B. Sci. Bull., Series D, Vol. 83, Iss. 4, 2021, ISSN 1454-2358

Liu, F., A Method for Predicting the Energy Consumption of the Main Driving System of a Machine Tool in a Machining Process, Journal of Cleaner Production 105, pp. 171-177, 2015

Luo, W., Hua, T., Yee, Y., Zhanga, C., Weia, Y., A hybrid Predictive Maintenance Approach for CNC Machine Tool Driven by Digital Twin, Robotics and Computer Integrated Manufacturing 65, 2020

Nasir, V., Sassani, F., A Review on Deep Learning in Machining and Tool Monitoring: Methods, Opportunities, and Challenges, The International Journal of Advanced Manufacturing Technology 115, pp. 2683–2709, 2021

Naumann, C., Glänzel, J,, Dix, M., Ihlenfeldt, S., Klimant, P., Optimization of Characteristic Diagram based Thermal Error Compensation via Load Case Dependent Model Updates, Journal of Machine Engineering, Vol. 22, No. 2, pp. 43–56, 2022, ISSN 1895-7595

Pimenov, D., Bustillo, A., Wojciechowski, S., Vishal, S., Artificial Intelligence Systems for Tool Condition Monitoring in Machining: Analysis and Critical Review, Journal of Intelligent Manufacturing 34, pp. 2079–2121, 2023

Serin, G,. Sener, B., Ozbayoglu, A.M., Unver, H.O, Review of Tool Condition Monitoring in Machining and Opportunities for Deep Learning, The International Journal of Advanced Manufacturing Technology 109, pp. 953–974, 2020

Sihag, N., Sangwan, K., A Systematic Literature Review on Machine Tool Energy Consumption, Journal of Cleaner Production 275, 2020

Zhou, L., Li, J., Li, F., Meng, Q., Li, J., Xu, X., Energy Consumption Model and Energy Efficiency of Machine Tools: A Comprehensive Literature Review, Journal of Cleaner Production 112, pp. 3721-3734, 2016


Refbacks

  • There are currently no refbacks.


JOURNAL INDEXED IN :