Yazar "Vashishtha, Govind" seçeneğine göre listele

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    Configuration of tool wear and its mechanism in sustainable machining of titanium alloys with energy signals
    (Springer London Ltd, 2024) Vashishtha, Govind; Chauhan, Sumika; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Ross, Nimel Sworna; Zimroz, Radoslaw; Krolczyk, Grzegorz M.
    Surface quality, machining efficiency, and tool life are all significantly impacted by tool wear in metal cutting machining. Research priorities and areas of focus in tool wear are shifting as intelligent machining becomes the norm. Unfortunately, there are currently no acknowledged most effective ways for analyzing tool based on the energy signals specially in the machining of titanium and its alloys. In the present work, the titanium machining was performed under different lubrication conditions such as dry, minimum quantity lubrication (MQL), liquid nitrogen and hybrid, etc. Then, the spectrograms are used to transform the acquired energy data into time-frequency features. Starting with a set of randomly generated hyper parameters (HPs), the long short-term memory (LSTM) model is fine-tuned using sine cosine algorithm (SCA) with loss serving as the fitness function. The confusion matrix provides additional validation of the 98.08% classification accuracy. Additional evaluations of the suggested method's superiority include its specificity, sensitivity, F1-score, and area under the curve (AUC).
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    Parallel structure of crayfish optimization with arithmetic optimization for classifying the friction behaviour of Ti-6Al-4V alloy for complex machinery applications
    (Elsevier, 2024) Chauhan, Sumika; Vashishtha, Govind; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Demirsoz, Recep; Noman, Khandaker; Kolesnyk, Vitalii
    Intelligent techniques play a vital role in predicting the friction force during the wear of Ti-6Al-4V alloy under different lubricating conditions. The effective assessment of friction forces and lubricating conditions allows for the replacement of the material before catastrophic failure. However, it remains challenging to utilise friction forces under different lubrication conditions to predict the wear through intelligent techniques. In this work, an advanced technique based on artificial intelligence has been proposed to address this issue. Intially parallel structure of crayfish optimization and arithmetic optimization algorithm (PSCOAAOA) is developed to duly address the issues of slow convergence, stucking in local optima and quality of the solution. The PSCOAAOA is further implemented for finding the optimal parameters (regularization parameter and kernel function) of the Support Vector Machine (SVM). The quantitative and qualitative analysis of PSCOAAOA is carried out on CEC2014 benchmark functions to validate its efficacy and robustness. The friction force generated during wear testing under different lubricating conditions is bifurcated into training and test data. Out of which, training data trains the SVM at an optimal combination of parameters. The overall accuracy of the built SVM model is found to be 95.85% with a computation time of 26.85 s.
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    Prediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning models
    (Springer London Ltd, 2024) Yurtkuran, Hakan; Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Yilmaz, Hakan; Gunay, Mustafa; Vashishtha, Govind
    Due to extensive distribution and huge demand of energy efficient processes, the energy-saving of machining processes draws more and more attention, and a significant variety of methods have evolved to prognosis or optimise the energy consumption in machining operations. Similarly, the estimation of power consumption-cutting conditions relationships is of great importance for optimizing processing costs and for cleaner machining. Compared to traditional methods, machine learning (ML) approach is one of the effective analysis options to model machinability indicators such as cutting force, tool wear, power consumption and surface quality. In this study, PH13-8Mo stainless steel was machined with coated carbide inserts using primarily Dry, MQL, nano-Graphene + MQL, nano-hBN + MQL, Cryo, Cryo + MQL cutting environments. Power consumption and its signals during milling were measured and different machine learning models were applied to estimate the Pc. To develop the Pc model based on the ML algorithm, 70% of the power consumption data is reserved for training and 30% for testing. In all cutting environments, power consumption increased by an average of 3.14% as feed speed increased. The reduction in Pc compared to the dry cutting was calculated as an average of 2.2%, 3.17%, 2.57%, 4.88% and 5.45% for MQL, nano-Graphen + MQL, nano-hBN + MQL, Cryo, Cryo + MQL, respectively. It is seen that the developed prediction model can reflect the power consumption-parameter relationships at high accuracy.
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    A review on microstructure, mechanical behavior and post processing of additively manufactured Ni-based superalloys
    (Emerald Group Publishing Ltd, 2024) Kuntoglu, Mustafa; Salur, Emin; Gupta, Munish Kumar; Waqar, Saad; Szczotkarz, Natalia; Vashishtha, Govind; Korkmaz, Mehmet Erdi
    PurposeThe nickel-based alloys Inconel 625 and Inconel 718 stand out due to their high strength and corrosion resistance in important industries like aerospace, aviation and automotive. Even though they are widely used, current techniques of producing materials that are difficult to cut pose several problems from a financial, ecological and even health perspective. To handle these problems and acquire improved mechanical and structural qualities, laser powder bed fusion (LPBF) has been widely used as one of the most essential additive manufacturing techniques. The purpose of this article is to focus on the state of the art on LPBF parts of Inconel 625 and Inconel 718 for microstructure, mechanical behavior and postprocessing.Design/methodology/approachThe mechanical behavior of LPBF-fabricated Inconel is described, including hardness, surface morphology and wear, as well as the influence of fabrication orientation on surface quality, biocompatibility and resultant mechanical properties, particularly tensile strength, fatigue performance and tribological behaviors.FindingsThe postprocessing techniques such as thermal treatments, polishing techniques for surface enhancement, mechanical and laser-induced peening and physical operations are summarized.Originality/valueThe highlighted topic presents the critical aspects of the advantages and challenges of the LPBF parts produced by Inconel 718 and 625, which can be a guideline for manufacturers and academia in practical applications.
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    Studies on newly developed hBN/graphene-based nano-fluids supported by cryogenic cooling conditions in improving the tribological performance of Ti6Al4V alloy
    (Elsevier, 2024) Korkmaz, Mehmet Erdi; Rai, Ritu; Demirsoz, Recep; Picak, Sezer; Vashishtha, Govind; Gunay, Mustafa
    Hexagonal Boron Nitride (hBN) is often referred to as a soft material due to its layered structure and properties that distinguish it from conventional hard materials like ceramics. The layered structure of hBN imparts lamellar lubrication characteristics and the weak van der Waals forces between adjacent layers allow for easy sliding, leading to low frictional resistance. The softness of hBN allows for ease of processing into various forms, facilitating its incorporation into lubricants, coatings, and composite materials. Therefore, the aim of this work is to enhance the lubricating capabilities of the nano-fluids and optimize the frictional behavior of Ti6Al4V alloy against tungsten carbide (WC) abrasive ball for potential biomedical applications, especially for combination of Ti6Al4V femoral stem and carbide femoral head. The newly formulated nano-fluids combine the unique properties of hBN and graphene to create a synergistic lubricating environment. The tribology and advanced characterization techniques were used to analyze the wear behavior of Ti6Al4V alloy surfaces. The results demonstrated that the applciaiton of cryogenically cooled lubricants with nanoparticles exhibited the lowest wear depth and friction forces, as a result of their increased viscosity.
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    A study on friction induced tribological characteristics of steel 316 L against 100 cr6 alloy under different lubricating conditions with machine learning model
    (Elsevier Sci Ltd, 2024) Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Karolczuk, Aleksander; Ross, Nimel Sworna; Vashishtha, Govind; Krolczyk, Jolanta B.; Demirsoz, Recep
    The material steadily wears away from touching surfaces when two solid entities are constantly moving against one other. When more parameters and extreme materials are involved in tribological testing, then it is very difficult to analyze and observe the working phenomena. With this aim, this study uses the gaussian process regression (GPR) approach to estimate friction forces when testing SS 316 L against 100 Cr6 alloy under cryogenic and cryo + minimum amount lubrication conditions. The friction forces from ball -on test experiments were used to develop the prediction models. Then, the wear surfaces and surface morphology are analyzed under cryo and cryo +MQL conditions. The results demonstrated that the combination of MQL and CRYO cooling reduced the friction forces more than 10 times for sliding distances above -30 m and loads below -25 n. Hence, the cryo +MQL conditions are beneficial in enhancing the tribological features due to the dual cooling and lubricating effects.