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 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.

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.

scholarly journals Assessment of the safety of use of portable machine tools

2018 ◽  
Vol 189 (3) ◽  
pp. 192-205
Author(s):  
Monika Nowak ◽  
Agnieszka Terelak-Tymczyna
Keyword(s):  
Machine Tool ◽  
Working Conditions ◽  
Machine Tools ◽  
Numerical Control ◽  
Milling Machine ◽  
Safety Issues ◽  
Large Size ◽  
Score Method ◽  
Characteristic Features ◽  
Risk Reducing

The article presents safety issues related to on-site machining with the use of portable machine tools. Their advantage is the possibility of machining elements at places in which they are used. This especially refers to large-size constructions, welded elements and any items whose disassembly is technically difficult. The authors present tasks performed by the operators of portable machining equipment, working conditions, construction and characteristic features of portable machine tools on the example of a portable boring machine, milling machine and flange facing machine. The presented characteristics can influence the safety of work with these machines. The information given in the article were used to asses risk at the position of a portable machine tool operator. The assessment was conducted using the Risk Score method taking into account four stages of using portable machine tools, i.e. transport, assembly/disassembly, machining and maintenance. The result of the conducted risk analysis is the proposal of possible risk reducing actions. Due to the specificity of the operation of portable machine tools which significantly impedes the development of a machine tool which would be safe in and of itself, the proposed actions refer mainly to organisational solutions. The work presents also the thesis that it is possible to decrease the risk at this position thanks to the use of numerical control in a portable machine tool. Such a solution may reduce exposure to some identified threats. The issue is presented on the example of a prototype of a portable flange facing machine developed in the Institute of Mechanical Technology ZUT in Szczecin.

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 Modeling and Optimization for Piercing Efficiency and Energy Consumption Based on Mean Value Substaged KELM-PLS and GA Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Dong Xiao ◽  
Jichun Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Optimization Method ◽  
Mean Value ◽  
Genetic Optimization ◽  
Simulation And Experiment ◽  
Coupling Characteristics ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Piercing manufacture of seamless tubes is the process that pierces solid blank into tube hollow. Piercing efficiency and energy consumption are the important indexes in the production of seamless tubes. Piercing process has the multivariate, nonlinear, cross-coupling characteristics. The complex factors that affect efficiency and consumption make it difficult to establish the mechanism models for optimization. Based on the production process, this paper divides the piercing process into three parts and proposes the piercing efficiency and energy consumption prediction models based on mean value staged KELM-PLS method. On the basis of mean value staged KELM-PLS prediction model, the minimum piercing energy consumption and maximum piercing efficiency are calculated by genetic optimization algorithm. Simulation and experiment prove that the optimization method based on the piercing efficiency and energy consumption prediction model can obtain the optimal process parameters effectively and also provide reliable evidences for practical production.

Building Energy Prediction Using Artificial Neural Networks (LSTM)

2020 ◽  
Author(s):  
Sankhanil Goswami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Prediction Models ◽  
Short Term Memory ◽  
Significant Proportion ◽  
Cooling Load ◽  
Energy Prediction ◽  
Artificial Neural

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

2020 ◽  
Vol 234 (10) ◽  
pp. 1346-1354 ◽  
Author(s):  
Guoyong Zhao ◽  
Yu Su ◽  
Guangming Zheng ◽  
Yugang Zhao ◽  
Chunxiao Li
Keyword(s):  
Tool Wear ◽  
Prediction Model ◽  
Specific Energy ◽  
Machine Tools ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Cutting Depth ◽  
Energy Prediction ◽  
Aisi 1045 Steel ◽  
1045 Steel

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.

scholarly journals Optimization Model Based on Multivariable Grey Model and Its Application to Energy Consumption in China

2021 ◽  
Author(s):  
Kai Xu ◽  
Xilin Luo ◽  
Xinyu Pang
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Prediction Model ◽  
Prediction Models ◽  
Clean Energy ◽  
Energy Development ◽  
The Other ◽  
The Novel ◽  
Grey Prediction ◽  
Novel Model

Abstract Currently, the energy development in China is in a critical period of transformation and reform, facing unprecedented opportunities and challenges. Accurate energy consumption forecast is conducive to promoting the diversification of energy development and utilization, and ensuring the healthy and rapid development of China's economy. Based on the existing multivariable grey prediction model, a nonlinear multivariable grey prediction model with parameter optimization is established in this paper, which used the genetic algorithms to find the optimal parameters, and the modelling steps are obtained. Then, the novel model takes the oil natural gas, coal and clean energy in China as the research objects, and the results are compared with the other four grey prediction models. The novel model has higher simulation and prediction accuracy, which is better than the other four grey prediction models. Finally, the novel model is used to predict those four energy consumption forecasts in China from 2020 to 2024. The results show that various energy consumption will further increase, while the fastest growing is clean energy and natural gas, which provides effective information for the Chinese government to formulate energy economic policies.

Sign in / Sign up

Export Citation Format

Share Document