Influence of Cutting Conditions on Tool Life, Tool Wear and Surface Finish in the Face Milling Process

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

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Research and Optimization of Surface Roughness in Milling of SLM Semi-Finished Parts Manufactured by Using the Different Laser Scanning Speed

Materials ◽

10.3390/ma13010009 ◽

2019 ◽

Vol 13 (1) ◽

pp. 9 ◽

Cited By ~ 2

Author(s):

Andrzej Matras

Keyword(s):

Surface Roughness ◽

Feed Rate ◽

Laser Scanning ◽

Cutting Tool ◽

Scanning Speed ◽

Milling Process ◽

Face Milling ◽

Machining Parameters ◽

The Face ◽

Laser Scanning Speed

The paper studies the potential to improve the surface roughness in parts manufactured in the Selective Laser Melting (SLM) process by using additional milling. The studied process was machining of samples made of the AlSi10Mg alloy powder. The simultaneous impacts of the laser scanning speed of the SLM process and the machining parameters of the milling process (such as the feed rate and milling width) on the surface roughness were analyzed. A mathematical model was created as a basis for optimizing the parameters of the studied processes and for selecting the sets of optimum solutions. As a result of the research, surface with low roughness (Ra = 0.14 μm, Rz = 1.1 μm) was obtained after the face milling. The performed milling allowed to reduce more than 20-fold the roughness of the SLM sample surfaces. The feed rate and the cutting width increase resulted in the surface roughness deterioration. Some milled surfaces were damaged by the chip adjoining to the rake face of the cutting tool back tooth.

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FPGA Based Real Time Simulations of the Face Milling Process

IEEE Access ◽

10.1109/access.2020.3041177 ◽

2020 ◽

Vol 8 ◽

pp. 215987-216002

Author(s):

Michal R. Mazur ◽

Marek A. Galewski ◽

Krzysztof J. Kalinski

Keyword(s):

Real Time ◽

Milling Process ◽

Face Milling ◽

The Face ◽

Real Time Simulations

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Analysis of the Face Milling Process Based on the Imitation Modelling

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/126/1/012001 ◽

2016 ◽

Vol 126 ◽

pp. 012001 ◽

Cited By ~ 1

Author(s):

V A Khomenko ◽

A O Cherdancev ◽

P O Cherdancev ◽

V D Goncharov ◽

A Kulawik

Keyword(s):

Milling Process ◽

Face Milling ◽

The Face

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Chaotic Tool Wear During Machining of Titanium Metal Matrix Composite (TiMMCs)

Volume 2B: Advanced Manufacturing ◽

10.1115/imece2014-36494 ◽

2014 ◽

Cited By ~ 3

Author(s):

Xuan-Truong Duong ◽

Marek Balazinski ◽

René Mayer

Keyword(s):

Mathematical Model ◽

Tool Wear ◽

Metal Matrix Composite ◽

Matrix Composite ◽

Tool Life ◽

Metal Matrix ◽

Cutting Speed ◽

Cutting Conditions ◽

Cutting Condition ◽

Titanium Metal

The initial tool wear during machining of titanium metal matrix composite (TiMMCs) is the result of several wear mechanisms: tool layer damage, friction - tribological wear, adhesion, diffusion and brace wear. This phenomenon occurs at the first instant and extends to only ten seconds at most. In this case the adhesive wear is the most important mechanism while the brace wear is considered as a resistance wear layer at the beginning of the steady wear period. In this paper, the effect of the initial tool wear and initial cutting conditions on tool wear progression and tool life is investigated. We proposed herein a new mathematical model based on the scatter wear and Lyapunov exponent to study quantitatively the “chaotic tool wear”. The Chaos theory, which has proved efficient in explaining how something changes in time, was used to demonstrate the dependence of the tool life on the initial cutting conditions and thus contribute to a better understanding of the influence of the initial cutting condition on the tool life. Based on the chaotic tool wear model, the scatter wear dimension and Lyapunov exponents were found to be positive in all case of the initial cutting conditions such as initial speed, feed rate and depth of cut. The initial cutting speed appears however to have the most significant impact on tool life. In particular, the mathematical model was successfully applied to the case of machining TiMMCs. It was clearly shown that changing the initial cutting speed by 20 m/min for the first two seconds of machining instead of keeping it constant at 60 m/min during the whole cutting process leads to an increase in the tool life (up to 24%).

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Prediction of Surface Roughness Profiles for Milling Process with Fractal Parameters Based on LS-SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.97-101.1186 ◽

2010 ◽

Vol 97-101 ◽

pp. 1186-1193 ◽

Cited By ~ 2

Author(s):

Ben Gan ◽

Yi Jian Huang ◽

Gui Xia Zheng

Keyword(s):

Surface Roughness ◽

Cutting Parameters ◽

Milling Process ◽

Face Milling ◽

Cutting Conditions ◽

Vertical Scaling ◽

Support Vector ◽

Fractal Function ◽

Milling Operations ◽

Fractal Parameters

Least squares support vector machines (LS-SVM) were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the LS-SVM are the cutting parameters: rotational speed, feed, depth of milling. The output parameters of the LS-SVM are the corresponding calculated fractal parameters: fractal dimension D and vertical scaling parameter G. The LS-SVM were utilized successfully for training and predicting the fractal parameters D and G in face milling operations. Moreover, Weierstrass-Mandelbrot（W–M ）fractal function was integrated with the LS-SVM in order to generate an artificially fractal predicted profiles at different milling conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens and there is a relationship between the scale-independent fractal coefficients(D and G).

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Machinability Improvement by Workpiece Preheating during End Milling AISI H13 Hardened Steel

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.264-265.894 ◽

2011 ◽

Vol 264-265 ◽

pp. 894-900 ◽

Cited By ~ 1

Author(s):

Mokhtar Suhaily ◽

A.K.M. Nurul Amin ◽

Anayet Ullah Patwari ◽

Nurhayati Ab. Razak

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Tool Life ◽

Room Temperature ◽

End Milling ◽

Hardened Steel ◽

Cutting Conditions ◽

Preheating Temperature ◽

Aisi H13 ◽

Coated Carbide

Hardened materials like AISI H13 steel are generally regarded as s difficult to cut materials because of their hardness due to intense of carbon content, which however allows them to be used extensively in the hot working tools, dies and moulds. The challenges in machining steels at their hardened state led the way to many research works in amelioration its machinability. In this paper, preheating technique has been used to improve the machinability of H13 hardened steel for different cutting conditions. An experimental study has been performed to assess the effect of workpiece preheating using induction heating system to enhance the machinability of AISI H13. The preheated machining of AISI H13 for two different cutting conditions with TiAlN coated carbide tool is evaluated by examining tool wear, surface roughness and vibration. The advantages of preheated machining are demonstrated by a much extended tool life and stable cut as lower vibration/chatter amplitudes. The effects of preheating temperature were also investigated on the chip morphology during the end milling of AISI H13 tool steel, which resulted in reduction of chip serration frequency. The preheating temperature was maintained below the phase change temperature of AISI H13. The experimental results show that preheated machining led to appreciable increasing tool life compared to room temperature machining. Abrasive wear, attrition wear and diffusion wear are found to be a very prominent mechanism of tool wear. It has been also observed that preheated machining of the material lead to better surface roughness values as compared to room temperature machining.

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