Tool wear and chip analysis after the hard turning of AISI D6 steel assisted by LN2

Titanium alloys are extensively applied in aerospace, automotive, biomedical, and chemical industries owing to their excellent performance combining high strength-to-density ratio, good corrosion resistance, and high strength at elevated temperature. Ti-6.5Al-3.5Mo-1.5Zr-0.3Si (TC11) alloys are used to replace the most common Ti-6Al-4V in some important applications such as some parts in aerospace engine. The purpose of this paper is to evaluate the machinability of TC11 alloys in the finish hard turning conditions. The paper presents the machinability results of TC11 alloys compared with Ti-6Al-4V, and analyzes the variables such as cutting force, surface integrity, and tool wear mechanism in the experiments.

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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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Investigations on Machining Performances and Tool Wear Surface Characterization During CNC Hard Turning of AISI 4140 Alloy Steel

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686721500335 ◽

2021 ◽

Author(s):

Shuvajit Ghosh ◽

Nirmal Kumar Mandal ◽

Tuhin Kar

Keyword(s):

Tool Wear ◽

Alloy Steel ◽

Wear Surface ◽

Surface Characterization ◽

Hard Turning ◽

Aisi 4140

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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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An experimental study on the effect of tool geometry on tool wear and surface roughness in hard turning

Advances in Mechanical Engineering ◽

10.1177/1687814020959885 ◽

2020 ◽

Vol 12 (9) ◽

pp. 168781402095988

Author(s):

Pham Minh Duc ◽

Le Hieu Giang ◽

Mai Duc Dai ◽

Do Tien Sy

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Inclination Angle ◽

Hard Turning ◽

Rake Angle ◽

Machining Process ◽

Tool Geometry ◽

Standard Tool ◽

Inclination Angles ◽

Mixed Ceramic

The main purpose of this study is to investigate the influence of tool geometry (cutting edge angle, rake angle, and inclination angle) and to optimize tool wear and surface roughness in hard turning of AISI 1055 (52HRC) hardened steel by using TiN coated mixed ceramic inserts. The results show that the inclination angle is the major factor affecting the tool wear and the surface roughness in hard turning. With the increase in negative rake and inclination angles, the tool wear decreases, and the surface roughness increases. However, the surface roughness will decrease when the inclination angle increases to overpass a certain limit. This is a new and significant point in the research of the hard turning process. From this result, the large negative inclination angle (λ = −10°) should be applied to reduce the surface roughness and the tool wear simultaneously. With the optimal cutting tool angles in the research, the hard machining process is improved remarkably with decreases of surface roughness and tool wear 8.3% and 41.3%, respectively in comparison with the standard tool angles. And the proposed tool-post design approach brings an effective method to change the tool insert angles using standard tool-holders to improve hard or other difficult-to-cut materials turning quality.

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