scholarly journals Development of a Transient Energy Prediction Model for Machine Tools

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 678-683
Author(s):  
Shailendra Pawanr ◽  
Girish Kant Garg ◽  
Srikanta Routroy
Author(s):  
Guoyong Zhao ◽  
Yu Su ◽  
Guangming Zheng ◽  
Yugang Zhao ◽  
Chunxiao Li

Most of the existing energy-consumption models of machine tools are related to specific machine components and hence cannot be applied to other machine tools with different specifications. In order to help operators optimize machining parameters for improving energy efficiency, the tool tip cutting specific energy prediction model based on machining parameters and tool wear in milling is developed, which is independent of the standby power of machine tools and the spindle no-load power. Then, the prediction accuracy of the proposed model is verified with dry milling AISI 1045 steel experiments. Finally, the influence of machining parameters and tool wear on tool tip cutting specific energy is studied. The developed model is independent of machine components, so it can reveal the influence of machining parameters and tool wear on tool tip cutting specific energy. The tool tip cutting specific energy reduces with the increase in the cutting depth, side cutting depth, feed rate, and cutting speed, while increases linearly as the tool wears gradually. The research results are helpful to formulate efficient and energy-saving processing schemes on various milling machines.


Author(s):  
Raunak Bhinge ◽  
Jinkyoo Park ◽  
Kincho H. Law ◽  
David A. Dornfeld ◽  
Moneer Helu ◽  
...  

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.


2020 ◽  
Vol 275 ◽  
pp. 115402 ◽  
Author(s):  
Yan He ◽  
Pengcheng Wu ◽  
Yufeng Li ◽  
Yulin Wang ◽  
Fei Tao ◽  
...  

2020 ◽  
Vol 2020 (0) ◽  
pp. S13107
Author(s):  
Hozumi KANABE ◽  
Shumpei IKUSHIMA ◽  
Jumpei KUSUYAMA ◽  
Yohichi NAKAO

Author(s):  
Takuya Ogawa ◽  
J. Brian Hall ◽  
Benjamin E. Mays ◽  
Timothy C. Hardin

Current USA regulations in 10 CFR 50, Appendices G & H ensure adequate fracture toughness and provide for the monitoring of radiation embrittlement of the ferritic components of the reactor pressure vessel (RV). Regulatory Guide (RG) 1.99, Rev. 2 provides guidance on acceptable methods for predicting the effects of neutron irradiation in order to meet the requirements of Appendix G. Specifically, RG 1.99, Rev. 2 provides an embrittlement prediction model for Charpy transition temperature shift (TTS) and a prediction model for decreased Charpy upper shelf energy (USE). The prediction model for USE decrease has remained unchanged since introduction of RG 1.99 in 1975. The objective of this study is to present new USE prediction model(s) developed using an international light water reactor database similar to the effort behind the recently-updated ASTM E900-15 TTS prediction model. A database of ASME and similar specification USE decrease information was developed from USA and select international light water reactor surveillance capsule data, including the latest surveillance capsule fluence, irradiation temperature, material chemistry and other information. The USE database has more than 1,500 USE change measurements of irradiated RV steels. Several best estimation models to predict irradiated USE of materials were developed based on data fitting. Two types of best estimation models were investigated; one model type uses the ASTM E900-15 predicted TTS as a primary input parameter, while the other does not, so that a USE prediction could be made independently of the ASTM E900-15 TTS prediction. By using the ASTM E900-15 TTS as a primary input, the models of the first type implicitly considered the embrittlement mechanisms of matrix damage and copper rich precipitation. In the non-TTS models, the effect of copper was expressed by a hyperbolic tangent curve that has both an upper value and lower value in order to consider the effect of copper saturation. Associated standard deviations as a function of predicted USE were also established so that bounding predictions could be made. Bounding models from each type that conservatively predict irradiated USE by bounding at least 95% of the USE decrease data in the database were identified. These bounding models are estimated to have relatively low impact on the number of USA plants that are projected to have RV steels that drop below 50 ft-lbs (68 J) relative to RG 1.99, Rev. 2. Finally, a non-TTS model was selected as the recommended model, because it does not require calculation of TTS by ASTM E900-15 and thus is simpler to implement.


2021 ◽  
Author(s):  
Damian Burzyński

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (<i>RUE<sub>c</sub></i>) that can be transfered during a full cycle is variable, and also three different types of evolution of changes in <i>RUE<sub>c</sub></i> were identified. The paper presents a new non-parametric <i>RUE<sub>c</sub></i> prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the <i>RUE<sub>c</sub></i> prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied, for the first time, to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on <i>RUE<sub>c</sub></i> – the accumulated local effects model of the first and second order. Not only is the <i>RUE<sub>c</sub></i> prediction methodology presented in the paper characterised by high prediction accuracy when using small learning datasets, but it also shows high application potential in all kinds of battery management systems.


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