Influence of cutting conditions and tool wear on the cutting parameters for numerically controlled machine tools

2011 ◽  
Vol 31 (1) ◽  
pp. 69-73
Author(s):  
V. F. Makarov ◽  
A. V. Shokhrin ◽  
O. N. Potyagailo
Keyword(s):  
Tool Wear ◽  
Machine Tools ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Numerically Controlled Machine Tools

Turning of Inconel 718 by Cemented Carbides

2011 ◽  
Vol 496 ◽  
pp. 138-143 ◽  
Author(s):  
Ivan Mrkvica ◽  
Ryszard Konderla ◽  
Miroslav Faktor
Keyword(s):  
Tool Wear ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Nickel Superalloy ◽  
Cutting Conditions ◽  
Cutting Process ◽  
Cemented Carbides ◽  
Dry Turning ◽  
Cutting Inserts

This article deals with dry turning of nickel superalloy - Inconel 718. The different cemented carbides were applied for cutting process. These inserts were produced by Pramet Tools Ltd. company. This paper discusses durability of cutting inserts, the different intensity of tool wear at various cutting parameters. The most suitable cutting conditions are chosen in the scope of applied tools.

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.

Contribution to Milling of Materials on Ni Base

2012 ◽  
Vol 217-219 ◽  
pp. 2056-2059 ◽  
Author(s):  
Ivan Mrkvica ◽  
Miroslav Janoš ◽  
Petr Sysel
Keyword(s):  
Tool Wear ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Nickel Superalloy ◽  
Cutting Conditions ◽  
Cutting Process ◽  
Cemented Carbides ◽  
Cutting Inserts

This article deals with milling possibilities of nickel superalloy - Inconel 718. The different cemented carbides were applied for cutting process. These inserts were produced by Pramet Tools Ltd. company. This paper discusses durability of cutting inserts, the different intensity of tool wear at various cutting parameters. The most suitable cutting conditions are chosen in the scope of applied tools.

Tool-Wear Monitoring and Control

2005 ◽  
Author(s):  
J. Srinivas ◽  
Rao Dukkipati ◽  
V. Sreebalaji ◽  
K. Ramakotaih
Keyword(s):  
Tool Wear ◽  
Machine Tools ◽  
Flank Wear ◽  
Cutting Parameters ◽  
Monitoring And Control ◽  
Tool Wear Monitoring ◽  
Wear Monitoring ◽  
Trained Neural Network ◽  
And Control ◽  
Control Methodology

This paper presents, a control methodology based on experimental data of the tool wear as a function of cutting variables. In automatic machine tools there is strong need to control the tool wear by adjustment of the cutting parameters. In this connection, a control system, which can adjust the cutting parameters for a desired wear rate, is necessary. A regression relation is also established between the flank-wear and the cutting parameters. An inversely trained neural network model, which supplies the modified values of the cutting parameters, is used as a controller. The results are shown in the form of tables and graphs.

The Force and Wear of Carbide Mill in Milling Glass

2010 ◽  
Vol 44-47 ◽  
pp. 2617-2621
Author(s):  
Zhi Wei ◽  
Mei Lin Gu ◽  
Yu Tao Wang ◽  
Tong Hui Li
Keyword(s):  
Tool Wear ◽  
Quartz Glass ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
End Mill ◽  
Ball End Mill ◽  
Milling Force ◽  
Milling Parameters ◽  
The Relationship ◽  
Cutting Experiments

The force and wear of carbide ball-end mill when quartz glass is milled in the dry state has been studied. Cutting experiments have been investigated for different cutting parameters. The relationship between milling force and cutting conditions has been analyzed. Mechanism and patterns of tool wear has also been studied. A group of reasonable milling parameters have been achieved to mill quartz glass using carbide ball-end mill and tool wear can be delayed in the selected group of cutting parameters.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

Research Status of Subsequent Machining of Laser Cladding Layers

2020 ◽  
Vol 15 ◽  
Author(s):  
Lei Li ◽  
Yujun Cai ◽  
Guohe Li ◽  
Meng Liu
Keyword(s):  
Tool Wear ◽  
Surface Quality ◽  
Laser Cladding ◽  
Wear Surface ◽  
Chip Formation ◽  
Cutting Temperature ◽  
Dimensional Accuracy ◽  
Cutting Parameters ◽  
Cladding Layer ◽  
Cladding Layers

Background: As an important method of remanufacturing, laser cladding can be used to obtain the parts with specific shapes by stacking materials layer by layer. The formation mechanism of laser cladding determines the “Staircase effect”, which makes the surface quality can hardly meet the dimensional accuracy of the parts. Therefore, the subsequent machining must be performed to improve the dimensional accuracy and surface quality of cladding parts. Methods: In this paper, chip formation, cutting force, cutting temperature, tool wear, surface quality, and optimization of cutting parameters in the subsequent cutting of laser cladding layer are analyzed. Scholars have expounded and studied these five aspects but the cutting mechanism of laser cladding need further research. Results: The characteristics of cladding layer are similar to that of difficult to machine materials, and the change of parameters has a significant impact on the cutting performance. Conclusion: The research status of subsequent machining of cladding layers is summarized, mainly from the aspects of chip formation, cutting force, cutting temperature, tool wear, surface quality, and cutting parameters optimization. Besides, the existing problems and further developments of subsequent machining of cladding layers are pointed out. The efforts are helpful to promote the development and application of laser cladding remanufacturing technology.

scholarly journals Influence of Tool Wear on Form Deviations in Dry Machining of UNS A97075 Alloy

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 958
Author(s):  
Francisco Javier Trujillo Vilches ◽  
Sergio Martín Béjar ◽  
Carolina Bermudo Gamboa ◽  
Manuel Herrera Fernández ◽  
Lorenzo Sevilla Hurtado
Keyword(s):  
Tool Wear ◽  
General Trend ◽  
Cutting Speed ◽  
Main Tool ◽  
Cutting Parameters ◽  
Adhesion Wear ◽  
Tool Wear Mechanisms ◽  
Tool Wear Mechanism ◽  
High Feed ◽  
Dry Conditions

Geometrical tolerances play a very important role in the functionality and assembly of parts made of light alloys for aeronautical applications. These parts are frequently machined in dry conditions. Under these conditions, the tool wear becomes one of the most important variables that influence geometrical tolerances. In this work, the influence of tool wear on roundness, straightness and cylindricity of dry-turned UNS A97075 alloy has been analyzed. The tool wear and form deviations evolution as a function of the cutting parameters and the cutting time has been assessed. In addition, the predominant tool wear mechanisms have been checked. The experimental results revealed that the indirect adhesion wear (BUL and BUE) was the main tool-wear mechanism, with the feed being the most influential cutting parameter. The combination of high feed and low cutting speed values resulted in the highest tool wear. The analyzed form deviations showed a general trend to increase with both cutting parameters. The tool wear and the form deviations tend to increase with the cutting time only within the intermediate range of feed tested. As the main novelty, a relationship between the cutting parameters, the cutting time (and, indirectly, the tool wear) and the analyzed form deviations has been found.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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