scholarly journals A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction

2021 ◽  
Author(s):  
Shao-Hsien Chen ◽  
Min-Sheng Gao
Keyword(s):  
Shear Zone ◽  
Tool Life ◽  
Life Prediction ◽  
Cutting Tool ◽  
Vibration Signal ◽  
Chip Color ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Voltage Signal ◽  
Sensor Signals

Often, engineers with machining experience often judge machining state and tool life according to chips’ features. Engineers' experience is digitized in this study. During the cutting process, the cutting tool coming in contact with the workpiece produces a shear zone, which causes plastic deformation and shear slip. The chips closest to the shear zone can directly show the state of the tool and workpiece when the material is SKD61. This study used chip color, vibration, and current signal integration for prediction of machining state and cutting tool life. When the cutting tool wears increased, the chip surface color changed in the following way: purpleè purple blueè blue ècyan, or even green and yellow. When the cutting tool was in the accelerating wear phase, the color change was particularly obvious. The Back-Propagation Levenberg–Marquardt (BP-LM) predictive methodology was used to compare the predictive ability of voltage, vibration signal, and chip color. The Mean Absolute Percentage Error (MAPE) for the voltage signal was 12.28%, for the vibration signal it was 11.38%, and for the chip color combined with multi-sensor characteristics it was 7.85%. The MAPE of the chip color was the smallest. Using the General Regression Neural Network (GRNN) methodology, the MAPE for the voltage signal was 10.74%, for the vibration signal 7.96%, and for the chip color combined with multi-sensor characteristics was 6.59%. The MAPE of the chip color was the smallest. Obviously, the chip color combined with multi-sensor signals provided better predictive results than the vibration signal or voltage signal alone. There is currently no research on the usefulness of monitoring chip color for tool life prediction.

Development of residual cutting tool life prediction algorithm by processing on CNC machine tool

2013 ◽  
Vol 44 (9) ◽  
pp. 790-796 ◽  
Author(s):  
S. N. Grigoriev ◽  
V. D. Gurin ◽  
M. A. Volosova ◽  
N. Y. Cherkasova
Keyword(s):  
Machine Tool ◽  
Tool Life ◽  
Life Prediction ◽  
Cutting Tool ◽  
Prediction Algorithm ◽  
Cnc Machine Tool ◽  
Cnc Machine

The Effect of an Embedded Heat Pipe in a Cutting Tool on Temperature and Wear

2003 ◽  
Author(s):  
Richard Y. Chiou ◽  
Jim S. J. Chen ◽  
Lin Lu ◽  
Mark T. North
Keyword(s):  
Heat Pipe ◽  
Tool Life ◽  
Cutting Tool ◽  
Tool Material ◽  
Dimensional Accuracy ◽  
Chip Color ◽  
Radius Of Curvature ◽  
Skin Exposure ◽  
Fundamental Understanding ◽  
Cutting Tool Insert

This paper presents the fundamental understanding of the effect of an embedded heat pipe in a cutting tool on temperature and wear in machining. In particular, the technique can effectively minimize pollution and contamination of the environment caused by cutting fluids and the health problems of skin exposure and particulate inhalation in manufacturing. The temperature of a tool plays an important role in thermal distortion and the machined part’s dimensional accuracy, as well as in tool life in machining. A new embedded heat pipe technology has been developed to effectively remove the heat generated at the tool-chip interface in machining, thereby reduce tool wear and prolong tool life. Experiments were carried out to characterize the temperature distributions when performing turning experiments using a cutting tool installed with an embedded heat pipe. The ANSYS simulations show that the temperature near the cutting edge drops significantly with an embedded heat pipe during machining. Temperature measurements at several locations on the cutting tool insert agree with the simulation results both with and without the heat pipe. The effect of the heat pipe on reducing the cutting tool temperature was further supported by the observations of cutting tool material color, chip color, and chip radius of curvature.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals Application of Cr,Mo-(Cr,Mo,Zr,Nb)N-(Cr,Mo,Zr,Nb,Al)N multilayered composite multicomponent coating to increase the cutting tool life in turning steel

Procedia CIRP ◽  
2021 ◽  
Vol 101 ◽  
pp. 274-277
Author(s):  
Alexey Vereschaka ◽  
Marina Volosova ◽  
Nikolay Sitnikov ◽  
Filipp Milovich ◽  
Nikolay Andreev ◽  
...  
Keyword(s):  
Tool Life ◽  
Cutting Tool ◽  
Multilayered Composite ◽  
Multicomponent Coating

Model for Optimization of the Tool Life and the Cutting Speed for Maximum Productivity at Drilling of the Steel 2NiCr185

2015 ◽  
Vol 760 ◽  
pp. 433-438 ◽  
Author(s):  
Ovidiu Blăjină ◽  
Aurelian Vlase ◽  
Marius Iacob
Keyword(s):  
Mathematical Model ◽  
Stainless Steel ◽  
Tool Life ◽  
Stainless Steels ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Research Directions ◽  
Maximum Productivity ◽  
Optimum Cutting ◽  
New Research

The research in the last decade regarding their cutting machinability have highlighted the insufficiency of the data for establishing of the optimum cutting processing conditions and the optimum cutting regime. The purpose of this paper is the optimization of the tool life and the cutting speed at the drilling of the stainless steels in terms of the maximum productivity. A nonlinear programming mathematical model to maximize the productivity at the drilling of a stainless steel is developed in this paper. The optimum cutting tool life and the associated cutting tool speed are obtained by solving the proposed mathematical model. The use of this productivity model allows greater accuracy in the prediction of the productivity for the drilling of a certain stainless steel and getting the optimum tool life and the optimum cutting speed for the maximum productivity. The obtained results can be used in production activity, in order to increase the productivity of the stainless steels machining. Finally the paper suggests new research directions for the specialists interested in this field.

Current Progresses of Laser Assisted Machining of Aerospace Materials for Enhancing Tool Life

2013 ◽  
Vol 690-693 ◽  
pp. 3359-3364
Author(s):  
Shou Jin Sun ◽  
Milan Brandt ◽  
John P.T. Mo
Keyword(s):  
Tool Life ◽  
Cutting Tool ◽  
State Of The Art ◽  
Aerospace Industry ◽  
Cutting Temperature ◽  
Machining Process ◽  
Metallic Materials ◽  
Aerospace Materials ◽  
Beam Position ◽  
Laser Assisted Machining

A higher strength and heat resistance are increasingly demanded from the advanced engineering materials with high temperature applications in the aerospace industry. These properties make machining these materials very difficult because of the high cutting forces, cutting temperature and short tool life present. Laser assisted machining uses a laser beam to heat and soften the workpiece locally in front of the cutting tool. The temperature rise at the shear zone reduces the yield strength and work hardening of the workpiece, which make the plastic deformation of the hard-to-machine materials easier during machining. The state-of-the-art, benefits and challenges in laser assisted machining of metallic materials are summarized in this paper, and the improvement of tool life is discussed in relation to laser power, beam position and machining process parameters.

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