scholarly journals Toward a Generalized Energy Prediction Model for Machine Tools

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Raunak Bhinge ◽  
Jinkyoo Park ◽  
Kincho H. Law ◽  
David A. Dornfeld ◽  
Moneer Helu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
Prediction Models ◽  
Machining Process ◽  
Data Driven ◽  
Process Data ◽  
Energy Prediction

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.

A Generalized Data-Driven Energy Prediction Model With Uncertainty for a Milling Machine Tool Using Gaussian Process

2015 ◽  
Author(s):  
Jinkyoo Park ◽  
Kincho H. Law ◽  
Raunak Bhinge ◽  
Nishant Biswas ◽  
Amrita Srinivasan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Gaussian Process ◽  
Prediction Models ◽  
Numerical Control ◽  
Data Driven ◽  
Machining Parameters ◽  
Milling Machine ◽  
Energy Prediction

Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with minimum energy. Data-driven prediction models, built upon a nonlinear regression approach, can be used to gain an understanding of the effects of machining parameters on energy consumption. In this study, we use the Gaussian Process to construct the energy prediction model for a computer numerical control (CNC) milling machine tool. Energy prediction models for different machining operations are constructed based on collected data. With the collected data sets, optimum input features for model selection are identified. We demonstrate how the energy prediction models can be used to compare the energy consumption for the different operations and to estimate the total energy usage for machining a generic part. We also present an uncertainty analysis to develop confidence bounds for the prediction model and to provide insight into the vast parameter space and training required to improve the accuracy of the model. Generic parts are machined to test and validate the prediction model constructed using the Gaussian Process and we consistently achieve an accuracy of over 95 % on the total predicted energy.

scholarly journals Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

2018 ◽  
Vol 232 ◽  
pp. 01006
Author(s):  
Sanping Wang ◽  
Junwen Chen ◽  
Wei Yan
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Machine Tools ◽  
Machining Process ◽  
Machining Parameters ◽  
Nc Machine Tool ◽  
Nc Machine

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

Ensemble Neural Network Model for Predicting the Energy Consumption of a Milling Machine

2015 ◽  
Author(s):  
Ronay Ak ◽  
Moneer M. Helu ◽  
Sudarsan Rachuri
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Production Systems ◽  
Face Milling ◽  
Model Parameters ◽  
Efficient Production ◽  
Milling Machine ◽  
Energy Prediction

Accurate prediction of the energy consumption is critical for energy-efficient production systems. However, the majority of existing prediction models aim at providing only point predictions and can be affected by uncertainties in the model parameters and input data. In this paper, a prediction model that generates prediction intervals (PIs) for estimating energy consumption of a milling machine is proposed. PIs are used to provide information on the confidence in the prediction by accounting for the uncertainty in both the model parameters and the noise in the input variables. An ensemble model of neural networks (NNs) is used to estimate PIs. A k-nearest-neighbors (k-nn) approach is applied to identify similar patterns between training and testing sets to increase the accuracy of the results by using local information from the closest patterns of the training sets. Finally, a case study that uses a dataset obtained by machining 18 parts through face-milling, contouring, slotting and pocketing, spiraling, and drilling operations is presented. Of these six operations, the case study focuses on face milling to demonstrate the effectiveness of the proposed energy prediction model.

Research and Application of NC Machine Tool Energy Consumption Control Optimization

2013 ◽  
Vol 579-580 ◽  
pp. 314-319
Author(s):  
Xiu Ling Xu ◽  
Hong Liang Wang ◽  
Tian Biao Yu
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Machine Tool ◽  
Machine Tools ◽  
Numerical Control ◽  
Machining Process ◽  
Numerical Control System ◽  
Control Logic ◽  
Nc Machine Tool ◽  
Nc Machine

The numerical control machine tool, which is a typical energy-using product, because there are all sorts of energy loss and auxiliary functions, has low efficiency in machining process. Author makes a study of NC machine tool energy consumption controlling. With simens840Dsl numerical control system as an example, based on the analysis of CNC system control principle, according to processing characteristic of the part, the macro program is wrote to optimize dynamic characteristics of NC machine tool. According to the machine idle time in different work condition, three energy-saving control profiles are formulated, including machine standby, NC standby, auto shut off. By the formulation of the corresponding PLC control logic, low energy consumption of intelligent control of NC machine tool is realized. Integrate PAC3200/4200 multi-function meter in NC machine tools, quantitative measurement and analysis of energy consumption for numerical control system and ancillary equipment can be implemented, and machine tool real-time energy data can be obtained. This scheme has been successfully applied in long-men portable CNC machine tools, and obtained the ideal effect: to realize the optimize control of energy consumption, improve the performance of the machine tool, become more market competitiveness.

scholarly journals Sustainability Evaluation of Process Planning for Single CNC Machine Tool under the Consideration of Energy-Efficient Control Strategies Using Random Forests

2019 ◽  
Vol 11 (11) ◽  
pp. 3060 ◽  
Author(s):  
Chaoyang Zhang ◽  
Pingyu Jiang
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Process Planning ◽  
Energy Efficient ◽  
Machine Tools ◽  
Control Strategies ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Sustainability Evaluation ◽  
Efficient Control

As an important part of industrialized society, manufacturing consumes a large amount of raw materials and energy, which motivates decision-makers to tackle this problem in different manners. Process planning is an important optimization method to realize the object, and energy consumption, carbon emission, or sustainability evaluation is the basis for the optimization stage. Although the evaluation research has drawn a great deal of attention, most of it neglects the influence of state control of machine tools on the energy consumption of machining processes. To address the above issue, a sustainability evaluation method of process planning for single computer numerical control (CNC) machine tool considering energy-efficient control strategies has been developed. First, four energy-efficient control strategies of CNC machine tools are constructed to reduce their energy consumption. Second, a bi-level energy-efficient decision-making mechanism using random forests is established to select appropriate control strategies for different occasions. Then, three indicators are adopted to evaluate the sustainability of process planning under the consideration of energy-efficient control strategies, i.e., energy consumption, relative delay time, and machining costs. Finally, a pedestal part machined by a 3-axis vertical milling machine tool is used to verify the proposed methods. The results show that the reduction in energy consumption considering energy-efficient control strategies reaches 25%.

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
Keyword(s):  
Prediction Model ◽  
Machine Tools ◽  
Energy Prediction ◽  
Transient Energy

scholarly journals Predicting Building Energy Consumption using Engineering and Data Driven Approaches: A Review

2017 ◽  
Vol 2 (5) ◽  
pp. 44 ◽  
Author(s):  
Aulon Shabani ◽  
Orion Zavalani
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Building Energy ◽  
Data Driven ◽  
World Population ◽  
Design Phase ◽  
Energy Prediction ◽  
Important Concern ◽  
Paper Review ◽  
Second Category

Rapid growth of world population has higher impact on increasing buildings energy consumption. Therefore, improving energy consumption is an important concern for building engineers and operators. Energy management through forecasting approaches as one of most effective methods is in focus of this paper. Review of most elaborated methods is in our focus, where we investigate two main directions of energy prediction approaches. First category of approaches focuses on engineering methods mainly very reliable on building early operation stages and design phase, meanwhile second category go through data driven methods. Existing research works focused on these two models are introduced emphasizing advantages and relevant applications of methods.

scholarly journals On the Application of Impeller in Multi Axis CNC Machine Tools

2021 ◽  
Vol 2066 (1) ◽  
pp. 012113
Author(s):  
Weiwen Ye
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Complex Surface ◽  
Machining Process ◽  
Cnc Machine Tools ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Surface Processing ◽  
The Third ◽  
Machining Characteristics

Abstract Multi axis CNC machine tool has good linkage processing effect. Through the application of integral impeller in CNC machine tools, to improve the adaptability of CNC machine tools to complex surface processing parts, to improve the accuracy of multi axis CNC machine tools. The first part of this paper introduces the integral impeller and its machining characteristics; the second part introduces the basic NC machining process of integral impeller; the third part discusses the application of impeller in multi axis CNC machine tools from the creation of guide track, the simulation of integral impeller, software processing and generation. The purpose is to provide some reference for the processing and production of integral impeller.

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