Optimization strategy for turning operation accounting for cutting tool wear history

The paper reports on a methodology for monitoring and evaluating the progress of cutting tool wear and an optimization strategy for turning operations in which the progress of cutting tool wear is taken into account in optimizing process parameters. The methodology evaluates tool wear history’ when a cutting tool is used under different process conditions (e.g. change in workplace material and range of cutting parameters) which a cutting tool is likely to encounter in small batch production operations. The optimization strategy proposed attempts to maximize material removal rate taking into account surface roughness, system stability and cutting tool failure as its technical constraints. One of the novel aspects of this work is that it enables a more complete tool failure avoidance strategy to be developed, taking into account tool wear progression under a variety of conditions. The machining strategy is aimed at optimizing batch production in turning where it is likely that the same cutting tool edge is used to process parts made of different workpiece materials and therefore the cutting tool reaches its limiting flank wear under variable sets of cutting conditions.

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System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing

Sensors ◽

10.3390/s21248431 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8431

Author(s):

Arturo Yosimar Jaen-Cuellar ◽

Roque Alfredo Osornio-Ríos ◽

Miguel Trejo-Hernández ◽

Israel Zamudio-Ramírez ◽

Geovanni Díaz-Saldaña ◽

...

Keyword(s):

Tool Wear ◽

Condition Monitoring ◽

Feed Rate ◽

Cutting Tool ◽

Cutting Speed ◽

Cutting Parameters ◽

Economic Benefits ◽

Numerical Control ◽

Cnc Machines ◽

Cutting Tool Wear

The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values.

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Optimisation of Cutting Tool and Cutting Parameters in Face Milling of Custom 450 through the Taguchi Method

Advances in Materials Science and Engineering ◽

10.1155/2019/5868132 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Harun Gokce

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Tool Wear ◽

Taguchi Method ◽

Cutting Tool ◽

Cutting Tools ◽

Cutting Speed ◽

Cutting Parameters ◽

Cutting Tool Wear ◽

Wide Range

Stainless steels with unique corrosion resistance are used in applications with a wide range of fields, especially in the medical, food, and chemical sectors, to maritime and nuclear power plants. The low heat conduction coefficient and the high mechanical properties make the workability of stainless steel materials difficult and cause these materials to be in the class of hard-to-process materials. In this study, suitable cutting tools and cutting parameters were determined by the Taguchi method taking surface roughness and cutting tool wear into milling of Custom 450 martensitic stainless steel. Four different carbide cutting tools, with 40, 80, 120, and 160 m/min cutting speeds and 0.05, 0.1, 0.15, and 0.2 mm/rev feed rates, were selected as cutting parameters for the experiments. Surface roughness values and cutting tool wear amount were determined as a result of the empirical studies. ANOVA was performed to determine the significance levels of the cutting parameters on the measured values. According to ANOVA, while the most effective cutting parameter on surface roughness was the feed rate (% 50.38), the cutting speed (% 81.15) for tool wear was calculated.

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Experimental Research on Dry Cutting 34CrNiMo6

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.188.319 ◽

2011 ◽

Vol 188 ◽

pp. 319-324

Author(s):

Hao Yang ◽

Xi Bin Wang ◽

Li Jing Xie ◽

J.J. Pei ◽

T. Wang

Keyword(s):

Tool Wear ◽

Cutting Force ◽

Cutting Tool ◽

Cutting Speed ◽

Cutting Parameters ◽

Cutting Depth ◽

Dry Cutting ◽

Cutting Tool Wear ◽

Ceramic Cutting Tool ◽

Carbide Cutting Tool

By dry cutting 34CrNiMo6, hard-machined material, with carbide cutting tool and ceramic cutting tool, experimental analysis was carried out in tool wear and cutting force. Cutting tool wear in the process is researched in order to obtain the cutting tool wear patterns and analyze the influencing factors. Meanwhile, it is found that the cutting force with the cutting parameters (cutting speed, cutting depth, feed) changes regularly, and then the experience formula of cutting force can be established.

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Validity Analysis of Generalized Taylor Regression Formula by Tool Wear Test for Difficult-to-Machine Materials

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.589-590.342 ◽

2013 ◽

Vol 589-590 ◽

pp. 342-350

Author(s):

Hong Tao Ye ◽

Yi Wan ◽

Xiao Ping Ren ◽

Zhan Qiang Liu

Keyword(s):

Tool Wear ◽

Cutting Tool ◽

Hypothesis Test ◽

Wear Test ◽

Cutting Parameters ◽

Test Methods ◽

Taylor Formula ◽

Validity Analysis ◽

Cutting Tool Wear ◽

The Common

Talyor formula is widely accepted to describe tool wear and is employed to optimize cutting parameters. The common test methods and processes of cutting tool wear test are firstly investigated in this paper. The effects of interference factors on different test methods are analyzed by hypothesis test to find the reason that regression generalized Taylor formula is partly invalid or not effective. The meaning of exponentials in generalized Taylor formula is discussed and tool wear test principle for difficult-to-machine materials is proposed at last.

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

Engineering and Technology Journal ◽

10.30684/etj.v38i10a.1124 ◽

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.

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A First Step towards a Tribological Approach to Investigate Cutting Tool Wear

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.611-612.452 ◽

2014 ◽

Vol 611-612 ◽

pp. 452-459 ◽

Cited By ~ 1

Author(s):

Giovenco Axel ◽

Frédéric Valiorgue ◽

Cédric Courbon ◽

Joël Rech ◽

Ugo Masciantonio

Keyword(s):

Tool Wear ◽

Cutting Tool ◽

304L Stainless Steel ◽

Fe Modelling ◽

Wear Model ◽

Cutting Tool Wear ◽

The Will ◽

Preliminary Study ◽

The Impact ◽

Macroscopic Parameters

The present work is motivated by the will to improve Finite Element (FE) Modelling of cutting tool wear. As a ﬁrst step, the characterisation of wear mechanisms and identiﬁcation of a wear model appear to be fundamental. The key idea of this work consists in using a dedicated tribometer, able to simulate relevant tribological conditions encountered in cutting (pressure, velocity). The tribometer can be used to estimate the evolution of wear versus time for various tribological conditions (pressure, velocity, temperature). Based on this design of experiments, it becomes possible to identify analytically a wear model. As a preliminary study this paper will be focused on the impact of sliding speed at the contact interface between 304L stainless steel and tungsten carbide (WC) coated with titanium nitride (TiN) pin. This experiment enables to observe a modiﬁcation of wear phenomena between sliding speeds of 60 m/min and 180 m/min. Finally, the impact on macroscopic parameters has been observed.

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Tool Wear Detection Using Time Series Analysis of Acoustic Emission

Journal of Engineering for Industry ◽

10.1115/1.3188750 ◽

1989 ◽

Vol 111 (3) ◽

pp. 199-205 ◽

Cited By ~ 125

Author(s):

S. Y. Liang ◽

D. A. Dornfeld

Keyword(s):

Time Series ◽

Acoustic Emission ◽

Tool Wear ◽

Time Series Analysis ◽

Cutting Tool ◽

Shear Zones ◽

Parameter Vector ◽

Model Parameter ◽

Cutting Tool Wear ◽

Series Analysis

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

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