Tool wear prediction in turning

Choudhury, S.K.; Srinivas, Peri Subrahmanya

doi:10.1016/j.jmatprotec.2004.04.296

Cited by 66 publications

(23 citation statements)

References 9 publications

(10 reference statements)

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“…Usui et al's [218] equation is very practical for the implementation of tool wear estimation using the finite element method (FEM). Choudhury and Srinivas [37] developed a mathematical model to estimate the flank wear rate by means of the index of the diffusion coefficient and other cutting parameters, such as rubbing velocity and clearance and rake angles.…”

Section: Fuzzy Theorymentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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Section: Fuzzy Theorymentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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“…Improving the tool life is one of the usual topics in this area, what is obtained by reducing tool wear. Choudhury and Srinivas [7] and M. Murua [1] predicted tool wear using some regression models, while Tugrul and Yigit [8] used also neural networks for tool wear and surface roughness, which is another prediction topic in machining processes. Tool wear also depends on the alloy hardness, cutting parameters as Sardinas [6], Sahu [3] and Bonilla exposed on their articles, the cooling conditions [4], where A.Suarez concludes that HPC produces less wear than conventional lubrication, grain size [2,5] where Olovsjo demonstrate notch wear predominance for materials with large grains (LG) against materials with small grain (SG).…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Radial Turning Process for Superalloys

Jiménez

Boto

Irigoien

et al. 2017

Management Systems in Production Engineering

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Abstract:Stability detection in machining processes is an essential component for the design of efficient machining processes. Automatic methods are able to determine when instability is happening and prevent possible machine failures. In this work a variety of methods are proposed for detecting stability anomalies based on the measured forces in the radial turning process of superalloys. Two different methods are proposed to determine instabilities. Each one is tested on real data obtained in the machining of Waspalloy, Haynes 282 and Inconel 718. Experimental data, in both Conventional and High Pressure Coolant (HPC) environments, are set in four different states depending on materials grain size and Hardness (LGA, LGS, SGA and SGS). Results reveal that PCA method is useful for visualization of the process and detection of anomalies in online processes.

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“…Decreasing tool nose radius and clearance angle can simultaneously decrease dimensional error and it is clear that the production of dimensional error in turning is possible. Choudhury and Srinivas [15] determined flank wear in turning operation. Huang and Liang [16] developed a model for tool flank wear in finish hard turning.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Machining Techniques in CNC Turning Centre Using Genetic Algorithm

Ganesan¹,

Mohankumar

2013

Arab J Sci Eng

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In a manufacturing industry, machining process is to shape the metal parts by removing unwanted material. During the machining process of any part given quality specifications such as surface finish, accuracy with minimum production cost or machining time are to be considered. Economy of machining operation plays a key role in competitiveness in the market. This paper presents a multi-objective optimization technique, based on genetic algorithms, to optimize the cutting parameters in turning processes: cutting depth, feed and speed. Optimization of cutting parameters is one of the most important elements in any process planning of metal parts. In this paper the three objective functions, minimum operating time and minimum production cost and minimum tool wear are simultaneously optimized. The proposed model uses a genetic algorithm in order to obtain the nondominated sorting genetic algorithm (NSGA-II) and build the Pareto front graph. An application sample is developed and its results are analyzed for several different production conditions. This paper also remarks the advantages of multiobjective optimization approach over the single-objective one.

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Tool wear prediction in turning

Cited by 66 publications

References 9 publications

Cloud-enabled prognosis for manufacturing

Cloud-enabled prognosis for manufacturing

Stability Analysis of Radial Turning Process for Superalloys

Optimization of Machining Techniques in CNC Turning Centre Using Genetic Algorithm

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