A novel energy consumption model for milling process considering tool wear progression

能源消耗 刀具磨损 机床 过程(计算) 机械加工 能量(信号处理) 功率(物理) 高效能源利用 消费(社会学) 功率消耗 机械工程 工艺工程 工程类 计算机科学 汽车工程 可靠性工程 工业工程 数学 统计 操作系统 电气工程 物理 社会学 量子力学 社会科学
作者
Kan Shi,Dian Zhang,Ning Liu,Sibao Wang,Junxue Ren,Shuo Wang
出处
期刊:Journal of Cleaner Production [Elsevier]
卷期号:184: 152-159 被引量:62
标识
DOI:10.1016/j.jclepro.2018.02.239
摘要

Energy crisis, climate change, and stringent legislations are imposing great pressure on enterprises, especially manufacturing sectors, to improve their energy efficiency. To achieve higher energy efficiency in manufacturing, reliable energy consumption modelling is the prerequisite since it offers fundamental basis for any energy efficiency-related optimization. Although tool wear is inevitable, traditional energy consumption models fail to take tool wear effects into consideration. To address this issue, this study proposes an energy consumption model with tool wear progression for 3-axis milling process. Based on modern machining theory and recent achievements in energy consumption modelling, the proposed model is firstly derived as an expression with unknown coefficients. Subsequently, the involved coefficients are calibrated based on cutting experiments. With the explicit energy consumption model, power consumption with a given tool wear under new cutting conditions can be predicted with a high accuracy. In addition, as the model reveals a one-to-one correspondence between the power consumption and tool wear, the tool wear can also be effectively estimated from the measured power consumption. Compared with other tool wear monitoring methods such as acoustic emission and vibration, this power consumption-based tool wear estimation method is not only straightforward but also cost-effective. To the best of the authors' knowledge, the proposed energy consumption model with tool wear progression is the first model that was experimentally validated in terms of total power prediction and tool wear prediction, respectively. As such, the proposed model can be a significant supplement to existing energy consumption modelling in machining process, and may provide a more accurate and comprehensive platform for energy efficiency optimization.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
恬227发布了新的文献求助10
2秒前
3秒前
科研通AI6应助循循采纳,获得10
3秒前
hydrogen完成签到,获得积分10
3秒前
量子星尘发布了新的文献求助10
5秒前
6秒前
混日子呢完成签到,获得积分10
6秒前
李祖华发布了新的文献求助10
7秒前
7秒前
小硕土川完成签到,获得积分10
8秒前
凶狠的寄风完成签到 ,获得积分10
9秒前
仇悦完成签到,获得积分10
10秒前
论文中发布了新的文献求助10
12秒前
14秒前
年轻的孤晴完成签到 ,获得积分10
15秒前
雪妃完成签到 ,获得积分10
15秒前
15秒前
听话的醉冬完成签到 ,获得积分10
16秒前
孔院发布了新的文献求助10
17秒前
saber发布了新的文献求助10
18秒前
18秒前
18秒前
18秒前
IyGnauH完成签到 ,获得积分10
19秒前
叫滚滚发布了新的文献求助10
22秒前
22秒前
Flz发布了新的文献求助10
23秒前
BeBrave1028完成签到,获得积分10
25秒前
量子星尘发布了新的文献求助10
26秒前
郑智韩发布了新的文献求助30
28秒前
28秒前
29秒前
saber完成签到,获得积分10
31秒前
33秒前
35秒前
wefun完成签到,获得积分10
36秒前
37秒前
傲娇的蛋挞完成签到,获得积分10
37秒前
37秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Social Work Ethics Casebook: Cases and Commentary (revised 2nd ed.).. Frederic G. Reamer 1070
Introduction to Early Childhood Education 1000
2025-2031年中国兽用抗生素行业发展深度调研与未来趋势报告 1000
List of 1,091 Public Pension Profiles by Region 871
Alloy Phase Diagrams 500
A Guide to Genetic Counseling, 3rd Edition 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5419555
求助须知:如何正确求助?哪些是违规求助? 4534806
关于积分的说明 14146897
捐赠科研通 4451460
什么是DOI,文献DOI怎么找? 2441744
邀请新用户注册赠送积分活动 1433363
关于科研通互助平台的介绍 1410589