Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach-Reference-Cited by-同舟云学术

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Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach

Published:2018-03 Issue: Volume:160 Page:126-140
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Hu Luoke,Tang Renzhong,Liu Ying,Cao Yanlong,Tiwari Ashutosh

Funder

National Natural Science Foundation of China

Nantaihu Innovation Program of Huzhou Zhejiang China

China Scholarship Council

EPSRC EXHUME Project

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference46 articles.

1. An investigation into minimising total energy consumption and total weighted tardiness in job shops;Liu;J Clean Prod,2014

2. Energy consumption monitoring for the order fulfilment in a ubiquitous manufacturing environment;Hu;Int J Adv Manuf Technol,2017

3. Energy consumption model and energy efficiency of machine tools: a comprehensive literature review;Zhou;J Clean Prod,2016

4. A method for predicting the energy consumption of the main driving system of a machine tool in a machining process;Liu;J Clean Prod,2015

5. An energy management approach for the mechanical manufacturing industry through developing a multi-objective energy benchmark;Cai;Energ Convers Manage,2017

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1. Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining;Energy Conversion and Management;2024-08

2. Joint optimization of feature sequences and toolpath strategies in multi-feature workpiece machining for minimizing energy consumption and processing time;Journal of Manufacturing Systems;2024-06

3. A real time condition based sustainable maintenance method for milling process;Journal of Cleaner Production;2024-01

4. Intelligent optimization strategy for electrochemical removal of ammonia nitrogen by neural network embedded in a non-dominated sorting genetic algorithm;Journal of Water Process Engineering;2023-12

5. A multi-dimension coupling model for energy-efficiency of a machining process;Energy;2023-07

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