Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

2020 ◽  
pp. 095440542097106
Author(s):  
Yu Su ◽  
Congbo Li ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Guangxi Zhao
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Tool Wear ◽  
Specific Energy ◽  
Machine Tools ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Support Vector

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

Prediction Models for Energy Consumption and Surface Quality in Stainless Steel Milling

2021 ◽  
Author(s):  
Shuo Yu ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Shuang Xu ◽  
Zhifu Zheng
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Tool Wear ◽  
Surface Quality ◽  
Specific Energy ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Surface Hardness ◽  
Machined Surface

Abstract Stainless steel is a kind of difficult-to-machine material, and the work hardening in milling easily leads to high energy consumption and poor surface quality. Thus, the influence of machined surface hardness on energy consumption and surface quality cannot be ignored. To solve this problem, the prediction models for machine tool specific energy consumption and surface roughness are developed with tool wear and machined surface hardness considered firstly. Then, the validity of the models is verified through AISI 304 stainless steel milling experiments. The results show that the prediction accuracy of the machine tool specific energy consumption model can reach 98.7%, and the roughness model can reach 96.8%. Later, according to the developed prediction models, the influence of milling parameters, surface hardness, and tool wear on the machine specific energy consumption and surface roughness is studied. Results show that in stainless steel milling, the most significant parameters for surface roughness is the machined surface hardness, while that for energy consumption is the feed per tooth. The machine specific energy consumption increases linearly with the increase of the tool wear and the machined surface hardness gradually. The proposed models are helpful to optimize the process parameters for high efficiency and high quality machining of stainless steel.

scholarly journals Prediction of Tool Lifetime and Surface Roughness for Nickel-Based Waspaloy

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shao-Hsien Chen ◽  
Chung-An Yu
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
High Speed ◽  
Cutting Speed ◽  
High Strength ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Predictive Values ◽  
Actual Surface ◽  
Wide Range

In recent years, most of nickel-based materials have been used in aircraft engines. Nickel-based materials applied in the aerospace industry are used in a wide range of applications because of their strength and rigidity at high temperature. However, the high temperatures and high strength caused by the nickel-based materials during cutting also reduce the tool lifetime. This research aims to investigate the tool wear and the surface roughness of Waspaloy during cutting with various cutting speeds, feed per tooth, cutting depth, and other cutting parameters. Then, it derives the formula for the tool lifetime based on the experimental results and explores the impacts of these cutting parameters on the cutting of Waspaloy. Since the impacts of cutting speed on the cutting of Waspaloy are most significant in accordance with the experimental results, the high-speed cutting is not recommended. In addition, the actual surface roughness of Waspaloy is worse than the theoretical surface roughness in case of more tool wear. Finally, a set of mathematical models can be established based on these results, in order to predict the surface roughness of Waspaloy cut with a worn tool. The errors between the predictive values and the actual values are 5.122%∼8.646%. If the surface roughness is within the tolerance, the model can be used to predict the residual tool lifetime before the tool is damaged completely. The errors between the predictive values and the actual values are 8.014%∼20.479%.

A Study on the Parameters in Hard Turning of High Speed Steel

2018 ◽  
Vol 5 (2) ◽  
pp. 1-12
Author(s):  
Krishnaraj Vijayan ◽  
N. Gouthaman ◽  
Tamilselvan Rathinam
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
High Speed ◽  
Hard Turning ◽  
White Layer ◽  
High Speed Steel ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Depth Of Cut ◽  
Contributing Factors

The objectives of hard turning of high speed steel (HSS-M2 Grade) are to investigate the effect of cutting parameters on cutting force, tool wear and surface integrity. This article presents the experimental results of heat treated high speed steel machined in a CNC lathe using cubic boron nitride (CBN) tools. Turing experiments were carried out using central composite design (CCD) method. From the experiments the influence of cutting parameters and their interactions on cutting forces, temperature and surface roughness (Ra) were analyzed. Following this, multi response optimization was done to find the best combination of parameters for minimum force, minimum temperature and minimum surface roughness. The experimental results showed that the most contributing factors were feed followed by depth of cut and spindle speed. A white layer formed during hard turning was also analyzed by scanning electron microscope (SEM) and the results showed that it was greatly influenced by the speed and depth of cut. Tool wear was experiments were conducted at the optimum cutting conditions and it was noted that the tool satisfactorily performed up to 10 minutes at dry condition.

scholarly journals A Generalized Analysis of Energy Saving Strategies Through Experiment for CNC Milling Machine Tools

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Xiang Chen ◽  
Jingyang Zhang ◽  
Yugui Huang
Keyword(s):  
Energy Consumption ◽  
Correlation Coefficient ◽  
Machine Tools ◽  
Tool Path ◽  
Energy Requirement ◽  
Piecewise Linear ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Linear Representation ◽  
Cnc Milling

Abstract This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding and rapid movement in X, Y, Z+ and Z- axially, and specific energy consumption (SEC) in the X and Y cutting directions respectively, which could not be considered completely in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in VMC850E machine, the results show that the SEC in the X and Y cutting directions are exactly different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of improved SEC model could reach to more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors.

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