Cutting Tool Failure and Surface Finish Analysis in Pulsating MQL-Assisted Hard Turning

There is a difference in machining by the cutting tool with defined geometry and undefined geometry. That is one of the reasons of implementation of hard turning into the machining process. In current manufacturing processes is hard turning many times used as a fine finish operation. It has many advantages – machining by single point cutting tool, high productivity, flexibility, ability to produce parts with complex shapes at one clamping. Very important is to solve machined surface quality. There is a possibility to use wiper geometry in hard turning process to achieve 3 – 4 times lower surface roughness values. Cutting parameters influence cutting process as well as cutting tool geometry. It is necessary to take into consideration cutting force components as well. Issue of the use of wiper geometry has been still insufficiently researched.

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Cutting Tool Wear and Mechanisms of Chip Formation During High-Speed Machining of Compacted Graphite Iron

ASME/STLE 2007 International Joint Tribology Conference, Parts A and B ◽

10.1115/ijtc2007-44026 ◽

2007 ◽

Author(s):

Niniza S. P. Dlamini ◽

Iakovos Sigalas ◽

Andreas Koursaris

Keyword(s):

Tool Wear ◽

Surface Finish ◽

Failure Criterion ◽

Cutting Tool ◽

Flank Wear ◽

Cutting Tools ◽

Compacted Graphite Iron ◽

Crater Wear ◽

Compacted Graphite ◽

Cutting Speeds

Cutting tool wear of polycrystalline cubic boron nitride (PcBN) tools was investigated in oblique turning experiments when machining compacted graphite iron at high cutting speeds, with the intention of elucidating the failure mechanisms of the cutting tools and presenting an analysis of the chip formation process. Dry finish turning experiments were conducted in a CNC lathe at cutting speeds in the range of 500–800m/min, at a feed rate of 0.05mm/rev and depth of cut of 0.2mm. Two different tool end-of-life criteria were used: a maximum flank wear scar size of 0.3mm (flank wear failure criterion) or loss of cutting edge due to rapid crater wear to a point where the cutting tool cannot machine with an acceptable surface finish (surface finish criterion). At high cutting speeds, the cutting tools failed prior to reaching the flank wear failure criterion due to rapid crater wear on the rake face of the cutting tools. Chip analysis, using SEM, revealed shear localized chips, with adiabatic shear bands produced in the primary and secondary shear zones.

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Effect of work hardening of cobalt in sintered carbide cutting tool on tool failure during interrupted cutting

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-016-8765-3 ◽

2016 ◽

Vol 88 (1-4) ◽

pp. 359-367

Author(s):

Erhan Altan ◽

Alper Uysal ◽

Şeref Han Anaç ◽

Sabri Öztürk

Keyword(s):

Work Hardening ◽

Cutting Tool ◽

Tool Failure ◽

Sintered Carbide ◽

Carbide Cutting Tool ◽

Interrupted Cutting

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Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.1515/bpasts-2017-0060 ◽

2017 ◽

Vol 65 (4) ◽

pp. 553-559 ◽

Cited By ~ 1

Author(s):

D. Rajeev ◽

D. Dinakaran ◽

S.C.E. Singh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tool Wear ◽

Hard Turning ◽

Cutting Tool ◽

Carbide Tool ◽

Online Tool ◽

Coated Carbide Tool ◽

Tool Wear Estimation ◽

Coated Carbide

AbstractNowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45–55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.

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Designing and implementation of a novel online adaptive control with optimization technique in hard turning

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820952197 ◽

2020 ◽

pp. 095965182095219

Author(s):

Vahid Pourmostaghimi ◽

Mohammad Zadshakoyan

Keyword(s):

Adaptive Control ◽

Tool Wear ◽

Hard Turning ◽

Cutting Tool ◽

Optimization Technique ◽

Cutting Parameters ◽

Numerical Control ◽

Machining Process ◽

Machining Parameters ◽

Optimization Methodology

Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.

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