Prediction models for energy consumption and surface quality in stainless steel milling

Yu, Shuo; Zhao, Guoyong; Li, Chunxiao; Xu, Shuang; Zheng, Zhifu

doi:10.1007/s00170-021-07971-x

Cited by 15 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [16], Pawar et al developed a model for circular geometries that achieved deviations below 5%. Yu et al developed a model for the prediction of energy consumption, as well as the surface quality, when milling stainless steel [17]. In a validation, an accuracy for the prediction of the energy consumption of 98.7% could be achieved.…”

Section: State Of the Artmentioning

confidence: 99%

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

Ströbel,

Probst,

Deucker

et al. 2023

Machines

View full text Add to dashboard Cite

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

Ströbel,

Probst,

Deucker

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…Pawanr et al [18] establish an empirical model between material removal rate and energy consumption in the turning process by the response surface method. Based on the study of energy consumption in the milling process of AISI 304 stainless steel, Yu et al [19] establish an empirical model of specific energy consumption of machine tools considering tool wear and workpiece surface hardness. Pawanr et al [20] establish an empirical model to predict the transient energy consumption of machine tools.…”

Section: Introductionmentioning

confidence: 99%

Cutting Energy Consumption Modelling of End Milling Cutter Coated with AlTiCrN

Meng

Sun

et al. 2023

Coatings

View full text Add to dashboard Cite

As an indispensable piece of equipment in the manufacturing industry, the machine tool is low-energy-efficiency and high-energy-consumption in operation. Therefore, it is urgent to establish a cutting energy consumption model to guide production and reduce the energy consumption of the machining process. In this paper, the AlTiCrN-coated cutting tool is taken as the object of study, and the cutting energy consumption model is established. The cutting energy consumption model is composed of a machining time model and a cutting power model. The cutting power model can be divided into the shear deformation power model of the workpiece, the friction power model of the flank surface and the friction power model of the rake surface. The influence of the edge shape is taken into account in the establishment of the friction power model of the flank surface. The machining time model considering the S-type acceleration and deceleration stage is established. The accuracy of the model was verified by experiments. Experimental results show that the model has high accuracy. The Taguchi method was used to carry out the numerical experiment with the cutting energy consumption of the machine tool as the response. The influences of cutting parameters on energy consumption are analyzed. Cutting width is the most important factor, followed by cutting depth, then feed rate and spindle speed. The physical principle of the influence of cutting parameters on cutting energy consumption is revealed.

show abstract

“…An energy consumption model for overall machining process is proposed by Gu et al [12] by decomposing the full machining process into a series of activities and activity transitions for accurate estimation of energy demand. Recently, an improved energy consumption model is developed by Yu et al [13] for CNC milling of stainless steel in addition to prediction of surface roughness of the part. The MSM models proposed by various researchers are able to predict energy consumption with decent accuracy for various machining operations.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption modelling in milling of variable curved geometry

Pawar

Bera

Sangwan

2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The accurate estimation of energy consumption is beneficial to manufacturing enterprises economically as well as to overcome global energy crisis. The present work concentrates on developing an energy consumption model in milling of variable curved geometries where magnitudes and directions of workpiece curvature vary along tool contact path of a component.The current work deals with estimation and analysis of energy consumption in peripheral milling of variable curved surfaces where cutting forces differ along tool contact path in the presence of workpiece curvature. The proposed hybrid model developed in MATLAB involves process mechanics, cutting forces and energy consumption and have modules for idle, auxiliary and cutting power. The proposed model is validated by the experimental work. The model is generic and versatile in nature and is useful for milling of straight, circular and curved surfaces. In addition to it, the influence of workpiece curvature on power consumption has been investigated to realize the variation of power consumption along the tool contact path. The developed model offers a basic platform to understand and characterize the energy consumption for general peripheral milling considering workpiece geometry. The comparison of predicted and measured results indicate that the model is capable to estimate the power consumption accurately. The proposed model will be used by the practitioners to find the optimum cutting conditions to reduce power consumption during the machining of curved geometries; a pragmatic condition but not much researched condition in machining.

show abstract

Prediction models for energy consumption and surface quality in stainless steel milling

Cited by 15 publications

References 23 publications

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

Cutting Energy Consumption Modelling of End Milling Cutter Coated with AlTiCrN

Energy consumption modelling in milling of variable curved geometry

Contact Info

Product

Resources

About