The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Agrawal, Rohit; Mukhopadhyay, Arkadeb

doi:10.1177/13506501221094416

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…The AISI 1040 steel containing 0.35-0.45 wt% C, 0.15-0.4 wt% Si, 0.6-0.9 wt% Mn, 0.05 wt% P and 0.04 wt% S [32,33] were used as base material. The AISI 1040 steel specimens were chosen because of their wide industrial usage [32,33]. Specimen preparation is an important aspect prior to electroless deposition.…”

Section: Experimental Details 21 Substrate Preparation and Coating De...mentioning

confidence: 99%

Development of Ni-B electroless coating from stabilizer free bath and characterization of high temperature tribological behaviour, scratch and corrosion resistance

Agrawal¹,

Mukhopadhyay²

2022

Surf. Topogr.: Metrol. Prop.

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The current work investigates the effects of variation of coating bath temperature on friction and wear behaviour of electroless Ni–B (ENB) coatings developed from stabilizer free bath. Coating is applied to specimens made up of AISI 1040 steel. Coatings were deposited at three different coating bath temperatures (85°C, 90°C and 95°C). Field emission scanning electron microscopy, inductively coupled plasma-optical emission spectrometer, and X-ray diffraction were used to characterize the coating for surface morphology, chemical composition, and phase structure respectively. Pin-on-disc tribo-tester was used to estimate the friction and wear behaviour of ENB coatings at room temperature (25ºC), 100ºC, 200ºC and 300ºC. The coefficient of friction was higher at high temperature due to higher roughness of the coatings obtained from stabilizer free bath, adhesion and ploughing. The wear rate at 200°C or 300°C was lower compared to 100°C. Additionally, the ENB coatings were subjected to thermogravimetric analysis which reveals higher thermal stability of coatings obtained at 95°C. A scratch tester at constant (6 N) and progressive load (5-24 N) was used to estimate the coatings scratch hardness and adhesion. The corrosion behaviour of ENB coatings in 3.5 % NaCl was studied using potentiodynamic polarization tests. The Ni-B coated specimens could efficiently provide barrier protection to steel substrate. But the corrosion potential was lower compared to lead stabilized bath.

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Section: Experimental Details 21 Substrate Preparation and Coating De...mentioning

confidence: 99%

Development of Ni-B electroless coating from stabilizer free bath and characterization of high temperature tribological behaviour, scratch and corrosion resistance

Agrawal¹,

Mukhopadhyay²

2022

Surf. Topogr.: Metrol. Prop.

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“…Existing forecasting models, such as simple artificial neural networks (ANNs), have shown some success in this area [22][23][24][25], but there is a pressing need to explore new and potentially more accurate computational modeling approaches for predicting wear rates in these Al/SiC metal matrix composites (MMCs). The previous literature has focused on the implementation of using conventional machine learning models for predicting wear rate [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

Mishra

Jatti

2023

Lubricants

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This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with various weight percentages of SiC (0%, 3%, and 5%), employing comprehensive spectroscopic analysis. The effect of SiC integration on the compositional distribution and ratio of elements within the composite is meticulously examined. In a novel move for this field of research, the study introduces and applies neurosymbolic programming as a novel computational modeling approach. The performance of this cutting-edge methodology is compared to a traditional simple artificial neural network (ANN). The neurosymbolic algorithm exhibits superior performance, providing lower mean squared error (MSE) values and higher R-squared (R2) values across both training and validation datasets. This highlights its potential for delivering more precise and resilient predictions, marking a significant development in the field. Despite the promising results, the study recognizes that the performance of the model might vary based on specific characteristics of the composite material and operational conditions. Thus, it encourages future studies to authenticate and expand these innovative findings across a wider spectrum of materials and conditions. This research represents a substantial advancement towards a more profound understanding of wear rates in Al/SiC MMCs and emphasizes the potential of the novel neurosymbolic programming in predictive modeling of complex material systems.

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“…They utilized experimental data from various coatings under different operating conditions to train several ML algorithms, including artificial neural networks (ANN) and support vector machines (SVM), in order to predict the coefficient of friction. In their study, Agrawal and Mukhopadhyay 58 utilized ML techniques and metaheuristic algorithms to explore the corrosion resistance and optimize the wear performance of AISI 1040 steel under dry operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of friction coefficient and torque in self-lubricating polymer radial bearings produced by additive manufacturing: A machine learning approach

Baş,

Karabacak

2023

Proceedings of the Institution of Mechanical Engineers, Part J:

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Additive manufacturing is a rapidly developing technology that enables the production of complex parts with intricate geometries. Self-lubricating radial bearings are one of the machine elements that can be produced using additive manufacturing. In this research, we present a machine learning-based approach to model the friction coefficient and friction torque in self-lubricating radial bearings manufactured by additive manufacturing using polyether ether ketone (PEEK), polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and nylon. The proposed approach includes different machine learning models (artificial neural networks, support vector machines, regression trees, linear regression models) that utilize experimental data to predict the coefficient of friction and friction torque of different types of radial bearings. Experimental data were obtained by performing tribological tests on self-lubricating radial bearings under various operating conditions. The results reveal that the machine learning models are successful in predicting the friction coefficient and friction torque in self-lubricating radial bearings with high accuracy. The approach can be utilized to optimize the design and performance of self-lubricating radial bearings manufactured using additive manufacturing.

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The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Cited by 9 publications

References 34 publications

Development of Ni-B electroless coating from stabilizer free bath and characterization of high temperature tribological behaviour, scratch and corrosion resistance

Development of Ni-B electroless coating from stabilizer free bath and characterization of high temperature tribological behaviour, scratch and corrosion resistance

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

Prediction of friction coefficient and torque in self-lubricating polymer radial bearings produced by additive manufacturing: A machine learning approach

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