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S K Choudhury - One of the best experts on this subject based on the ideXlab platform.

  • investigations on machinability aspects of hardened aisi 4340 steel at different levels of hardness using coated carbide tools
    International Journal of Refractory Metals & Hard Materials, 2013
    Co-Authors: Satish Chinchanikar, S K Choudhury
    Abstract:

    Abstract This study investigates the effect of workpiece hardness, cutting parameters and type of coating (coated tool) on different machinability aspects like, the tool Life, surface roughness, and cutting force and chip morphology during turning of hardened AISI 4340 steel at different levels of hardness. Cutting forces observed to be higher for harder workpiece and for CVD applied multi-layer MT-TiCN/Al 2 O 3 /TiN coated carbide tool. Better surface finish observed for harder workpiece and for PVD applied single-layer TiAlN coated carbide tool. However, better tool Life obtained by CVD coated tool can be attributed to its thick coating and the protective Al 2 O 3 oxide layer formed during cutting, which has protected the tool from severe abrasion at elevated temperatures. Modified Taylor tool Life Equation indicated that the workpiece hardness followed by the cutting speed and depth of cut as the most influencing factors on tool Life. The better performance of CVD coated tool under study is obtained by limiting the cutting speed to 300 and 180 m/min for workpiece hardness of 35 and 45 HRC, respectively. However, the upper limit is of 200 m/min when using PVD coated tool. It has been observed that the tool wear form and the wear mechanism(s) by which the tool wear occurred are influenced by the workpiece hardness, cutting conditions and the type of tool.

  • optimization of cutting parameters for maximizing tool Life
    International Journal of Machine Tools & Manufacture, 1999
    Co-Authors: S K Choudhury, I Appa V K Rao
    Abstract:

    Optimum use of the cutting tool is a growing need in modern industries since the cost of production is directly affected by this. This paper presents a new approach for improving the cutting tool Life by using optimal values of velocity and feed throughout the cutting process. A tool Life Equation has been established from experimental data and the adhesion wear model. Optimization techniques have been used to maximize the tool Life subject to practical constraints while maintaining a constant metal removal rate. The experimental results showed an improvement in tool Life by 30%.

I Appa V K Rao - One of the best experts on this subject based on the ideXlab platform.

  • optimization of cutting parameters for maximizing tool Life
    International Journal of Machine Tools & Manufacture, 1999
    Co-Authors: S K Choudhury, I Appa V K Rao
    Abstract:

    Optimum use of the cutting tool is a growing need in modern industries since the cost of production is directly affected by this. This paper presents a new approach for improving the cutting tool Life by using optimal values of velocity and feed throughout the cutting process. A tool Life Equation has been established from experimental data and the adhesion wear model. Optimization techniques have been used to maximize the tool Life subject to practical constraints while maintaining a constant metal removal rate. The experimental results showed an improvement in tool Life by 30%.

Wing Kam Liu - One of the best experts on this subject based on the ideXlab platform.

  • a physically short fatigue crack growth approach based on low cycle fatigue properties
    International Journal of Fatigue, 2017
    Co-Authors: Orion L Kafka, Wing Kam Liu
    Abstract:

    Abstract This work proposes an improved and simplified fatigue Life Equation called LAPS, formulated from low cycle fatigue data of smooth specimens and the Rice-Kujawski-Ellyin asymptotic field, with proper crack opening functions for closure effects. The model captures both long and physically short fatigue crack growth behavior, but the emphasis of this contribution is on the modifications for physically short cracks based on empirical models found in the literature. The predictions for physically short cracks from this model coincide well with experimental data for the railway axle used steel 25CrMo4. The predictions for long cracks match well with data from a variety of different metals. This makes the model a suitable alternative, e.g. to NASGRO, for engineering applications.

Tony L Schmitz - One of the best experts on this subject based on the ideXlab platform.

  • tool Life prediction using bayesian updating part 1 milling tool Life model using a discrete grid method
    Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology, 2014
    Co-Authors: Jaydeep Karandikar, Ali E Abbas, Tony L Schmitz
    Abstract:

    Abstract According to the Taylor tool Life Equation, tool Life reduces with increasing cutting speed following a power law. Additional factors can also be added, such as the feed rate, in Taylor-type models. Although these models are posed as deterministic Equations, there is inherent uncertainty in the empirical constants and tool Life is generally considered a stochastic process. In this work, Bayesian inference is applied to estimate model constants for both milling and turning operations while considering uncertainty. In Part 1 of the paper, a Taylor tool Life model for milling that uses an exponent, n , and a constant, C , is developed. Bayesian inference is applied to estimate the two model constants using a discrete grid method. Tool wear tests are performed using an uncoated carbide tool and 1018 steel work material. Test results are used to update initial beliefs about the constants and the updated beliefs are then used to predict tool Life using a probability density function. In Part 2, an extended form of the Taylor tool Life Equation is implemented that includes the dependence on both cutting speed and feed for a turning operation. The dependence on cutting speed is quantified by an exponent, p , and the dependence on feed by an exponent, q ; the model also includes a constant, C . Bayesian inference is applied to estimate these constants using the Metropolis–Hastings algorithm of the Markov Chain Monte Carlo (MCMC) approach. Turning tests are performed using a carbide tool and MS309 steel work material. The test results are again used to update initial beliefs about the Taylor tool Life constants and the updated beliefs are used to predict tool Life via a probability density function.

  • tool Life prediction using bayesian updating part 2 turning tool Life using a markov chain monte carlo approach
    Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology, 2014
    Co-Authors: Jaydeep Karandikar, Ali E Abbas, Tony L Schmitz
    Abstract:

    Abstract According to the Taylor tool Life Equation, tool Life reduces with increasing cutting speed following a power law. Additional factors can also be added, such as the feed rate, in Taylor-type models. Although these models are posed as deterministic Equations, there is inherent uncertainty in the empirical constants and tool Life is generally considered a stochastic process. In this work, Bayesian inference is applied to estimate model constants for both milling and turning operations while considering uncertainty. In Part 1 of the paper, a Taylor tool Life model for milling that uses an exponent, n, and a constant, C, is developed. Bayesian inference is applied to estimate the two model constants using a discrete grid method. Tool wear tests are performed using an uncoated carbide tool and 1018 steel work material. Test results are used to update initial beliefs about the constants and the updated beliefs are then used to predict tool Life using a probability density function. In Part 2, an extended form of the Taylor tool Life Equation is implemented that includes the dependence on both cutting speed and feed for a turning operation. The dependence on cutting speed is quantified by an exponent, p, and the dependence on feed by an exponent, q; the model also includes a constant, C. Bayesian inference is applied to estimate these constants using the Metropolis–Hastings algorithm of the Markov Chain Monte Carlo (MCMC) approach. Turning tests are performed using a carbide tool and MS309 steel work material. The test results are again used to update initial beliefs about the Taylor tool Life constants and the updated beliefs are used to predict tool Life via a probability density function.

  • spindle speed selection for tool Life testing using bayesian inference
    Journal of Manufacturing Systems, 2012
    Co-Authors: Jaydeep Karandikar, Tony L Schmitz, Ali E Abbas
    Abstract:

    Abstract According to the Taylor tool Life Equation, tool Life is dependent on cutting speed (or spindle speed for a selected tool diameter in milling) and their relationship is quantified empirically using a power law exponent, n , and a constant, C , which are tool-workpiece dependent. However, the Taylor tool Life model is deterministic and does not incorporate the inherent uncertainty in tool Life. In this work, Bayesian inference is applied to estimate tool Life. With this approach, tool Life is described using a probability distribution at each spindle speed. Random sample tool Life curves are then generated and the probability that a selected curve represents the true tool Life curve is updated using experimental results. Tool wear tests are performed using an inserted (uncoated) carbide endmill to machine AISI 1018 steel. The test point selection is based on the maximum value of information approach. The updated beliefs are then used to predict tool Life using a probability distribution function.

Mark W Verbrugge - One of the best experts on this subject based on the ideXlab platform.

  • degradation of lithium ion batteries employing graphite negatives and nickel cobalt manganese oxide spinel manganese oxide positives part 1 aging mechanisms and Life estimation
    Journal of Power Sources, 2014
    Co-Authors: John S Wang, Justin Purewal, Jocelyn Hicksgarner, Souren Soukazian, Elena Sherman, Adam Sorenson, Luan Vu, Harshad Tataria, Mark W Verbrugge
    Abstract:

    Abstract We examine the aging and degradation of graphite/composite metal oxide cells. Non-destructive electrochemical methods were used to monitor the capacity loss, voltage drop, resistance increase, lithium loss, and active material loss during the Life testing. The cycle Life results indicated that the capacity loss was strongly impacted by the rate, temperature, and depth of discharge (DOD). Lithium loss and active electrode material loss were studied by the differential voltage method; we find that lithium loss outpaces active material loss. A semi-empirical Life model was established to account for both calendar-Life loss and cycle-Life loss. For the calendar-Life Equation, we adopt a square root of time relation to account for the diffusion limited capacity loss, and an Arrhenius correlation is used to capture the influence of temperature. For the cycle Life, the dependence on rate is exponential while that for time (or charge throughput) is linear.