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.

Peanut Drying Energy Consumption

1979 ◽  
Vol 6 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Paul D. Blankenship ◽  
Victor Chew
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Temperature Control ◽  
Energy Requirements ◽  
Total Energy Consumption ◽  
Control Constant

Abstract Total energy consumption and drying times were determined for drying peanuts with various equipment and procedures commonly used by the peanut industry. Total peanut drying times were significantly shorter with 3.73 kW, single-trailer (ST) dryers than with 7.46 kW, double-trailer (DT) dryers. Total energy consumption was significantly higher per tonne of peanuts dried for drying with ST dryers. Total energy consumption and drying times were not significantly different for drying in side-air-entry or rear-air-entry trailers. Precleaning reduced energy requirements for drying and slightly reduced total drying times. Drying peanuts at 40.56°C decreased drying times but required considerably more energy than drying at 35°C. Type of temperature control, constant (Co) or cycling (Cy), had no effect on drying times or energy consumption.

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.

Researched on the Technology of Machining Simulation

2014 ◽  
Vol 1039 ◽  
pp. 390-396
Author(s):  
Bao Rui Li ◽  
Wen Hua Zhu ◽  
Benoit Eynard ◽  
Matthieu Bricogne
Keyword(s):  
Machine Tool ◽  
Manufacturing Industry ◽  
Production Costs ◽  
Machining Process ◽  
Simulation Test ◽  
Processing Efficiency ◽  
Simulation Engine ◽  
Blade Machining ◽  
Modern Manufacturing ◽  
Simulation Systems

The machine tool machining is a major processing method in manufacturing industry. Achieved the simulation for the machining process will greatly improved the processing efficiency, perfected the processing quality and reduced the production costs. It has important significance for the development of modern manufacturing industry. This paper studied the constitute of the processing verification and simulation systems. And focuses on the principle of the machine tool system driven by the common simulation engine and driven by the virtual NC controller. Finally, the simulation system was built and use the blade machining as an example to the simulation test and verify.

scholarly journals Rebound Effect and Technical Learning of Energy-environmental Evaluation in Manufacturing Industry

2019 ◽  
Author(s):  
Jiang Wang ◽  
Kunyue Wang ◽  
Lijuan Wang
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Environmental Effect ◽  
Manufacturing Industry ◽  
Rebound Effect ◽  
Learning By Doing ◽  
Learning Curves ◽  
Production Environment ◽  
Environmental Evaluation ◽  
Technical Learning

We introduce a total energy and environmental evaluation method in the manufacturing industry. The method gives us a series of descriptive indexes to assess the overall environmental effect level on materials, energy, wastes and products in the life cycle process. Meanwhile, the method uses partial indexes, environmental effect factors, and correction offsets into the quantitative model to analyze the learning process and rebound effect on the energy and environment at each procedure. In this work, we choose S-shaped learning curves to describe how to decrease energy consumption and improve the technical learning by doing for recent 30 years. Also, we draw the different rebound effect curves of the total energy-environmental evaluation with technical learning method, which use the annual industrial production growth rate to show that it's significant to estimate its effect on technology changes. The ideas about the interaction of energy and production environment from material flows to energy consumption, direct us to build an example to estimate quantitatively the results with different condition factors, and realize the process improvement and develop new products.

scholarly journals Design of Regenerative Flutter Stability Detection System in Machine Tool Cutting

2021 ◽  
Vol 2074 (1) ◽  
pp. 012071
Author(s):  
Liyao Li
Keyword(s):  
Machine Tool ◽  
Manufacturing Industry ◽  
Cutting Tool ◽  
Detection System ◽  
Machining Process ◽  
Advanced Technology ◽  
Practical Significance ◽  
Work Piece ◽  
Excited Vibration ◽  
Cutting Chatter

Abstract Cutting chatter is a strong relative vibration between cutting tool and work piece in machining process, which will reduce cutting quality and cutting efficiency, and shorten the service life of cutting tool and machine tool. As long as cutting is carried out in production, vibration will occur, and chatter is a strong self-excited vibration between machining work piece and cutting tool. Flutter problem will occur in almost all cutting processes, which will cause a series of problems such as the reduction of dimensional accuracy of machined work pieces, tool damage and so on. In the dynamic design and dynamic analysis of machine tool structure, in order to evaluate and improve the ability of machine tool to resist chatter, and to select the cutting conditions without chatter, it is necessary to judge the cutting stability of machine tool. How to improve the advanced technology of manufacturing industry is an important topic for manufacturing researchers, and the research on the detection of cutting chatter stability has important practical significance for promoting the development of cutting manufacturing industry to high-end technology.

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 The application of machine learning to sensor signals for machine tool and process health assessment

2020 ◽  
pp. 095440542096089
Author(s):  
James Moore ◽  
Jon Stammers ◽  
Javier Dominguez-Caballero
Keyword(s):  
Machine Learning ◽  
Machine Tool ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Test Procedure ◽  
Machining Process ◽  
Sensor Data ◽  
Custom Made ◽  
Production Environments ◽  
Sensor Signals

Due to the latest advancements in monitoring technologies, interest in the possibility of early-detection of quality issues in components has grown considerably in the manufacturing industry. However, implementation of such techniques has been limited outside of the research environment due to the more demanding scenarios posed by production environments. This paper proposes a method of assessing the health of a machining process and the machine tool itself by applying a range of machine learning (ML) techniques to sensor data. The aim of this work is not to provide complete diagnosis of a condition, but to provide a rapid indication that the machine tool or process has changed beyond acceptable limits; making for a more realistic solution for production environments. Prior research by the authors found good visibility of simulated failure modes in a number of machining operations and machine tool fingerprint routines, through the defined sensor suite. The current research set out to utilise this system, and streamline the test procedure to obtain a large dataset to test ML techniques upon. Various supervised and unsupervised ML techniques were implemented using a range of features extracted from the raw sensor signals, principal component analysis and continuous wavelet transform. The latter were classified using convolutional neural networks (CNN); both custom-made networks, and pre-trained networks through transfer learning. The detection and classification accuracies of the simulated failure modes across all classical ML and CNN techniques tested were promising, with all approaching 100% under certain conditions.

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.

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