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

Shi, Kan; Zhang, D.H.; Liu, Ning; Wang, S.B.; Ren, Junxue; Wang, S.L.

doi:10.1016/j.jclepro.2018.02.239

Cited by 57 publications

(30 citation statements)

References 22 publications

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“…And at the constant feed per tooth fz =0. 25 Spindle rotation speed n=630 rpm) after two passes of the mill VB is 5.0 and 2.2 mm respectively, with Rz increased by 59% and 34% respectively as compared to a sharp mill. Milling at a higher speed leads to faster buildup of the flank wear of the mill tooth as the sliding distance increases significantly (see Fig.…”

Section: Investigationmentioning

confidence: 97%

Optimization of Face Milling Parameters Considering Wear and Tool Life of Cutters to Improve the Quality, Cost and Power Consumption

Pimenov¹,

Gupta²,

Ердаков³

et al. 2018

Preprint

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Face milling is a well known commercial process highly used in heavy industries that consumes high amount of power. Besides power issue, modern manufacturing industries are aiming for per part cost reduction keeping the product quality unimpaired. Unexpectedly if the part is rejected in any stage of manufacturing, the cost of manufacturing dramatically increases. Major cause of part rejection is excessive tool wear that imparts poor surface profile or catastrophic tool failure that causes adherence of broken tool debris onto machined surface. Furthermore, the tool wear is associated with sliding distance (frictional distance) and the tool life quantifies the cost of tools. As such, from the perspective of manufacturing industries it is imperative to optimize the surface quality parameter, cost of part, power consumption, and material removal – this is exactly what is accomplished here. By this work, it is possible to conserve power consumption, produce parts with lower cost, manufacture with uncompromising surface quality and enhanced material removal rate. Moreover, as intermediate factors of interest, the influences of sliding distance, tool life and tool flank wear on the overall machining performance are evaluated. The multi-objective optimization by Grey Relational Analysis (GRA) revealed that for improved product performance and fast manufacturing (case 1) optimum results are: feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min and cutting length l = 0.5 mm; for resource conservation (case 2) the optimum results are: feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, cutting length l = 0.5 mm.

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Section: Investigationmentioning

confidence: 97%

Optimization of Face Milling Parameters Considering Wear and Tool Life of Cutters to Improve the Quality, Cost and Power Consumption

Pimenov¹,

Gupta²,

Ердаков³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, tool wear is an important factor affecting directly machining efficiency and work-product quality, and even result in system failure and unnecessary downtime [4][5][6]. The conventional regular manual tool inspection method reduces manufacturing efficiency by shutdown periods to facilitate inspection, and the resulting evaluations are not sufficiently accurate to ensure adequate efficiency and work-product quality for machining processes [7,8]. Thus, the development of effective quantitative and accurate tool wear estimation methods has been a subject of intense practical interest over the past few years [9,10].…”

Section: Introductionmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time-frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

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“…Shi et al [25] proposed a novel approach to modeling the energy consumption in milling machining by taking into account the influence of tool wear on the energy consumption. The author reported that the tool wear can be estimated from the power consumption and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Investigations of surface quality and energy consumption associated with costs and material removal rate during face milling of AISI 1045 steel

Pimenov

Abbas

Gupta

et al. 2020

Int J Adv Manuf Technol

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Face milling is a well known commercial process highly used in heavy industries that consumes high amount of power. Besides power issue, modern manufacturing industries are aiming for per part cost reduction keeping the product quality unimpaired. Unexpectedly if the part is rejected in any stage of manufacturing, the cost of manufacturing dramatically increases. Major cause of part rejection is excessive tool wear that imparts poor surface profile or catastrophic tool failure that causes adherence of broken tool debris onto machined surface. Furthermore, the tool wear is associated with sliding distance (frictional distance) and the tool life quantifies the cost of tools. As such, from the perspective of manufacturing industries it is imperative to optimize the surface quality parameter, cost of part, power consumption, and material removal -this is exactly what is accomplished here. By this work, it is possible to conserve power consumption, produce parts with lower cost, manufacture with uncompromising surface quality and enhanced material removal rate. Moreover, as intermediate factors of interest, the influences of sliding distance, tool life and tool flank wear on the overall machining performance are evaluated. The multi-objective optimization by Grey Relational Analysis (GRA) revealed that for improved product performance and fast manufacturing (case 1) optimum results are: feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min and cutting length l = 0.5 mm; for resource conservation (case 2) the optimum results are: feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, cutting length l = 0.5 mm.

show abstract

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

Cited by 57 publications

References 22 publications

Optimization of Face Milling Parameters Considering Wear and Tool Life of Cutters to Improve the Quality, Cost and Power Consumption

Optimization of Face Milling Parameters Considering Wear and Tool Life of Cutters to Improve the Quality, Cost and Power Consumption

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Investigations of surface quality and energy consumption associated with costs and material removal rate during face milling of AISI 1045 steel

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