A method for predicting the energy consumption of the main driving system of a machine tool in a machining process

2015 ◽  
Vol 105 ◽  
pp. 171-177 ◽  
Author(s):  
Fei Liu ◽  
Jun Xie ◽  
Shuang Liu
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Machining Process ◽  
Driving System

Mechatronical Product Design Combined with Optimized Dynamic Behavior of Components

2014 ◽  
Vol 903 ◽  
pp. 252-258
Author(s):  
Paul Helmut Nebeling
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Product Design ◽  
Dynamic Behavior ◽  
Machining Process ◽  
Control Technique ◽  
Characteristic Parameters ◽  
Design Basis ◽  
Influencing Parameters ◽  
Technical Features

This paper introduces some closer aspects of mechatronical and process oriented machine tool design. Basis of the considerations are the demands of the machining process and the workpiece dimensions and material. The machining area, the axis and the technical features are designed on characteristic parameters. The forces, torque and speed are derived out of these parameters. Control technique is adapted to the process specific demands of the machine and the workpiece. The machine tool behavior is adapted to the process demands so that the costs and energy consumption are minimized. Some examples are included to present properties concerning accuracy, dynamic behavior and influencing parameters.

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.

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.

Methodology for Calculating Energy Consumption of a Machining Process

2012 ◽  
Vol 472-475 ◽  
pp. 2736-2743
Author(s):  
Jing Xiang Lv ◽  
Ren Zhong Tang ◽  
Shun Jia
Keyword(s):  
Energy Consumption ◽  
Environmental Impact ◽  
Machine Tool ◽  
Total Energy ◽  
Manufacturing Industry ◽  
Machining Process ◽  
Energy Requirements ◽  
Energy Behavior

Due to significant environmental impact and constantly rising prices, energy consumption gets more and more attention by governments and companies. Understanding and calculating the total energy requirements as well as detailed energy breakdown of a machining process are essential tasks as machining is responsible for a large amount of energy consumption in manufacturing industry. The aim of the work reported in this paper is to develop a methodology to estimate and analyze energy consumption of a machining process. This methodology is based on the representation of a machining process as a series of activities. The energy consumption of activities is calculated combined with the energy behavior of machine tool components, and formulas for calculating total as well as detailed breakdown of energy consumption for a machining process are given. The application of the methodology is demonstrated on the turning of a simple shaft.

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.

Numerically Constructed Energy Consumption Mapping for the Orthogonal Machining of Ti6Al4V

2019 ◽  
Author(s):  
Ibrahim Nouzil ◽  
Salman Pervaiz ◽  
Sathish Kannan
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Energy Demand ◽  
Manufacturing Sector ◽  
Cutting Parameters ◽  
Machining Process ◽  
Shop Floor ◽  
Cutting Power ◽  
Orthogonal Machining

Abstract Energy efficiency is an important aspect of all industrial processes. Sustainability in manufacturing sector can be improved by reducing and optimizing the energy requirements of the machine tool during the machining process. Growing energy demand and shortages in conventional energy sources have prompted research into energy conservation and sustainable development. Machining of difficult-to-cut materials such as titanium-based alloys involve high consumption of energy and hence it is compulsory to study the optimal combinations of process parameters that yield the best result with highest efficiency in terms of energy. This paper aims to develop a finite element (FE) assisted virtual energy consumption map for the machining of Ti6Al4V in orthogonal turning operation. The cutting power for the machining process is simulated in AdvantEdge specially designed machining software for specific cutting parameters. The map developed is universally applicable and independent of the machine tool as only cutting power is used. The visual energy map can be utilized at the shop floor level, as it is easily understandable to the operator. The information enabled through the map can help the operator select the optimal combination of process parameters for the machining process and maximize the energy based efficiency of the cutting process.

scholarly journals Energy efficient cutting parameter optimization

2021 ◽  
Author(s):  
Xingzheng Chen ◽  
Congbo Li ◽  
Ying Tang ◽  
Li Li ◽  
Hongcheng Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Parameter Optimization ◽  
Energy Efficient ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Current Status ◽  
Cutting Parameter ◽  
Cutting Parameter Optimization

AbstractMechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

New Development in High Efficiency Deep Grinding

2012 ◽  
Author(s):  
Andre D. L. Batako ◽  
Valery V. Kuzin ◽  
Brian Rowe
Keyword(s):  
Machine Tool ◽  
High Efficiency ◽  
Machining Process ◽  
Depth Of Cut ◽  
Removal Rates ◽  
Workpiece Surface ◽  
New Development ◽  
Cutting Processes ◽  
Selection Of ◽  
Made In

High Efficiency Deep Grinding (HEDG) has been known to secure high removal rates in grinding processes at high wheel speed, relatively large depth of cut and moderately high work speed. High removal rates in HEDG are associated with very efficient grinding and secure very low specific energy comparable to conventional cutting processes. Though there exist HEDG-enabled machine tools, the wide spread of HEDG has been very limited due to the requirement for the machine tool and process design to ensure workpiece surface integrity. HEDG is an aggressive machining process that requires an adequate selection of grinding parameters in order to be successful within a given machine tool and workpiece configuration. This paper presents progress made in the development of a specialised HEDG machine. Results of HEDG processes obtained from the designed machine tool are presented to illustrate achievable high specific removal rates. Specific grinding energies are shown alongside with measured contact arc temperatures. An enhanced single-pole thermocouple technique was used to measure the actual contact temperatures in deep cutting. The performance of conventional wheels is depicted together with the performance of a CBN wheel obtained from actual industrial tests.

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