Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

2020 ◽  
Vol 234 (10) ◽  
pp. 1346-1354 ◽  
Author(s):  
Guoyong Zhao ◽  
Yu Su ◽  
Guangming Zheng ◽  
Yugang Zhao ◽  
Chunxiao Li
Keyword(s):  
Tool Wear ◽  
Prediction Model ◽  
Specific Energy ◽  
Machine Tools ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Cutting Depth ◽  
Energy Prediction ◽  
Aisi 1045 Steel ◽  
1045 Steel

Most of the existing energy-consumption models of machine tools are related to specific machine components and hence cannot be applied to other machine tools with different specifications. In order to help operators optimize machining parameters for improving energy efficiency, the tool tip cutting specific energy prediction model based on machining parameters and tool wear in milling is developed, which is independent of the standby power of machine tools and the spindle no-load power. Then, the prediction accuracy of the proposed model is verified with dry milling AISI 1045 steel experiments. Finally, the influence of machining parameters and tool wear on tool tip cutting specific energy is studied. The developed model is independent of machine components, so it can reveal the influence of machining parameters and tool wear on tool tip cutting specific energy. The tool tip cutting specific energy reduces with the increase in the cutting depth, side cutting depth, feed rate, and cutting speed, while increases linearly as the tool wears gradually. The research results are helpful to formulate efficient and energy-saving processing schemes on various milling machines.

Study of the Relationship between Cutting Speed, Tool Wear and Machining Forces in Turning with Carbide Tool

2014 ◽  
Vol 902 ◽  
pp. 88-94
Author(s):  
Heraldo J. Amorim ◽  
Augusto O. Kunrath Neto
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Cutting Speed ◽  
Average Error ◽  
Machining Forces ◽  
Feed Force ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel ◽  
The Relationship

The understanding of machining processes comprises the study of phenomena such as: chip formation, cutting forces, tool wear mechanisms and the influence of the cutting parameters and machined materials on them. The aim of this work is to analyze the tool wear effects on machining forces during machining of AISI 1040 and 1045 carbon steels with carbide tool. Long-term machinability tests were performed, in which cutting force, feed force and tool wear were measured. Tool life results were analyzed, with best tool lives found for the AISI 1040 steel for all tested speeds. The other variables were analyzed as function of both time and tool wear. On the time domain, strong dependencies were found for both materials for tool wear, cutting force and feed force. The relationship between cutting force and tool wear showed good correlation for both materials, and the same was observed for feed force and tool wear relationship. Weak influence of cutting speed was observed on the relationship between tool wear and machining forces, which suggest that a single equation can describe them for all studied conditions with reasonable accuracy. The regression results are able to predict cutting forces as a function of tool wear with an average error of about 2.6 % during machining of AISI 1040 and 5.2 % for AISI 1045 steel. For the prediction of feed force as a function of tool wear, the average error is about 5.6 % for AISI 1040 and 7.0 % for the AISI 1045 steel, since a restricted domain is established. Data analysis showed a discontinuity in the behavior of feed force as a function of tool wear near the end of the life of the tools for most tests performed with AISI 1045 and some tests with AISI 1040 that suggest backwall wear, which was further evidenced by sudden change of chip form near the end of tool life in AISI 1040 steel.

scholarly journals Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes

2021 ◽  
Author(s):  
Mourad NOUIOUA ◽  
Mohamed Lamine BOUHALAIS
Keyword(s):  
Tool Wear ◽  
Tool Life ◽  
Laser Doppler Vibrometer ◽  
Machining Parameters ◽  
Machining Processes ◽  
Limit Value ◽  
Aisi 1045 Steel ◽  
Wear Monitoring ◽  
Artificial Neural ◽  
1045 Steel

Abstract In machining processes various phenomena occur during cutting operation. These phenomena can disturb the production through the reduction of part quality and accuracy. Therefore, a mastery of this cutting phenomena is needed to define the machining parameters and take full advantage of manufacturing process. An easy way to classify these phenomena is by monitoring incontrollable parameters, such as generated temperature and vibration. The acquired vibration signals can provide information regarding tool life, cutting performances and workpiece defects. This paper evaluates the possibility of monitoring the tool life during the turning process of AISI 1045 steel using Laser Doppler Vibrometer (LDV), the surface roughness has been measured along with the tool-wear until reaching its limit value of 300µm. Furthermore, this paper also outlines the application of CEEMDAN technique to process the acquired signals for the monitoring processes. RMS and SCI indicators have been used to describe the wear progress, then, the artificial neural network has been adopted to achieve a real time wear monitoring. The obtained results qualified the SCI indicator and ANN for online monitoring.

Modeling of Stresses and Temperature in Turning Using Finite Element Method

2013 ◽  
Vol 307 ◽  
pp. 174-177 ◽  
Author(s):  
Kuldip Singh Sangwan ◽  
Girish Kant ◽  
Aditya Deshpande ◽  
Pankaj Sharma
Keyword(s):  
Finite Element ◽  
Cutting Forces ◽  
Orthogonal Cutting ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Effective Stresses ◽  
Wide Range ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

This paper focuses on finite element modeling of orthogonal cutting process of AISI 1045 steel using Modified Johnson Cook (MJC) as constitutive material flow model under various machining parameters. Finite element solutions of cutting forces, effective stresses and temperature are obtained for a wide range of cutting speeds and feeds. The effect of feed and cutting speed on cutting forces, effective stresses and temperature has been studied over a wide range of values. Percentage variation of each is also studied to predict co-relation with the different machining parameters.

scholarly journals Development of a Transient Energy Prediction Model for Machine Tools

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 678-683
Author(s):  
Shailendra Pawanr ◽  
Girish Kant Garg ◽  
Srikanta Routroy
Keyword(s):  
Prediction Model ◽  
Machine Tools ◽  
Energy Prediction ◽  
Transient Energy

Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Rajesh Kumar Bhushan
Keyword(s):  
Tool Wear ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Nose Radius ◽  
Multiresponse Optimization

Optimization in turning means determination of the optimal set of the machining parameters to satisfy the objectives within the operational constraints. These objectives may be the minimum tool wear, the maximum metal removal rate (MRR), or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are the cutting speed, feed rate, depth of cut, and nose radius. The optimum set of these four input parameters is determined for a particular job-tool combination of 7075Al alloy-15 wt. % SiC (20–40 μm) composite and tungsten carbide tool during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate. The regression models, developed for the minimum tool wear and the maximum MRR were used for finding the multiresponse optimization solutions. To obtain a trade-off between the tool wear and MRR the, a method for simultaneous optimization of the multiple responses based on an overall desirability function was used. The research deals with the optimization of multiple surface roughness parameters along with MRR in search of an optimal parametric combination (favorable process environment) capable of producing desired surface quality of the turned product in a relatively lesser time (enhancement in productivity). The multi-objective optimization resulted in a cutting speed of 210 m/min, a feed of 0.16 mm/rev, a depth of cut of 0.42 mm, and a nose radius of 0.40 mm. These machining conditions are expected to respond with the minimum tool wear and maximum the MRR, which correspond to a satisfactory overall desirability.

scholarly journals Investigation of AlCrN-Coated Inserts on Cryogenic Turning of Ti-6Al-4V Alloy

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1338
Author(s):  
Lakshmanan Selvam ◽  
Pradeep Kumar Murugesan ◽  
Dhananchezian Mani ◽  
Yuvaraj Natarajan
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Physical Vapor Deposition ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machined Surface ◽  
Coated Carbide ◽  
Cryogenic Conditions

Over the past decade, the focus of the metal cutting industry has been on the improvement of tool life for achieving higher productivity and better finish. Researchers are attempting to reduce tool failure in several ways such as modified coating characteristics of a cutting tool, conventional coolant, cryogenic coolant, and cryogenic treated insert. In this study, a single layer coating was made on cutting carbide inserts with newly determined thickness. Coating thickness, presence of coating materials, and coated insert hardness were observed. This investigation also dealt with the effect of machining parameters on the cutting force, surface finish, and tool wear when turning Ti-6Al-4V alloy without coating and Physical Vapor Deposition (PVD)-AlCrN coated carbide cutting inserts under cryogenic conditions. The experimental results showed that AlCrN-based coated tools with cryogenic conditions developed reduced tool wear and surface roughness on the machined surface, and cutting force reductions were observed when a comparison was made with the uncoated carbide insert. The best optimal parameters of a cutting speed (Vc) of 215 m/min, feed rate (f) of 0.102 mm/rev, and depth of cut (doc) of 0.5 mm are recommended for turning titanium alloy using the multi-response TOPSIS technique.

Prediction of Tool Wear Mechanisms in Face Milling AISI 1045 Steel

2011 ◽  
Vol 21 (6) ◽  
pp. 797-808 ◽  
Author(s):  
Patricia Muñoz-Escalona ◽  
Nayarit Díaz ◽  
Zulay Cassier
Keyword(s):  
Tool Wear ◽  
Wear Mechanisms ◽  
Face Milling ◽  
Tool Wear Mechanisms ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

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.

A Prediction Model of Surface Roughness in Micro End Milling

2015 ◽  
Vol 727-728 ◽  
pp. 354-357
Author(s):  
Mei Xia Yuan ◽  
Xi Bin Wang ◽  
Li Jiao ◽  
Yan Li
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Feed Rate ◽  
End Milling ◽  
Orthogonal Experiment ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Cutting Depth ◽  
Micro End Milling

Micro-milling orthogonal experiment of micro plane was done in mesoscale. Probability statistics and multiple regression principle were used to establish the surface roughness prediction model about cutting speed, feed rate and cutting depth, and the significant test of regression equation was done. On the basis of successfully building the prediction model of surface roughness, the diagram of surface roughness and cutting parameters was intuitively built, and then the effect of the cutting speed, feed rate and cutting depth on the small structure surface roughness was obtained.

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