The Role of Machine Learning in Tribology: A Systematic Review

The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review

Virtual reality (VR) and augmented reality (AR) continue to play an important role in vocational training in the current pandemic and Industrial Revolution 4.0 era. Welding is one of the highly demanded vocational skills for various manufacturing and construction industries. Students need to undergo many practical sessions to become skilful welders. However, conventional training is very costly in terms of material, time, and infrastructure. Hence, we explore the intervention of VR and AR in welding training, which includes the research purposes, VR and AR technologies, and welding concepts and activities. We performed a comprehensive search of articles from the year 2000 to 2021. After filtering through inclusion criteria and full-text assessment, a total of 42 articles were coded and evaluated. While there has been growth in VR and AR welding training research, there is little discussion in their effectiveness for supporting distance learning, and most studies targeted entry-level students. Our main contributions are classifying primary functions in the virtual welding workshops and their adaptation to the psychomotor domain. We hope these results can empower the research community to develop and improve the VR and AR system and evaluation instruments to support vocational training, especially during this pandemic. 

/pdf/vr-and-ar-virtual-welding-for-psychomotor-skills-a-31dvv37q.pdf

VR and AR virtual welding for psychomotor skills: a systematic review

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.

A review of recent advances and applications of machine learning in tribology.

Artificial neural networks in the evaluation of the influence of the type and content of carrier on selected quality parameters of spray dried raspberry powders

Microstructural measurement and artificial neural network analysis for adhesion of tribolayer during sliding wear of powder-chip reinforcement based composites

Reality based skills development approach in welding technology: An overview

Investigation on the effects of cooling rate on surface Texture, corrosion behaviour and hardness of pure copper

In the present investigation, effect of ceramic/graphite addition on the dry sliding wear behavior of copper-based hybrid composites have been assessed at three different normal loads of 9.81, 19.62 and 29.43 N. Wear test is performed by using pin-on-disc test rig at sliding speeds of 1.30 and 1.84 m/s under ambient conditions. The copper-based hybrid composites are successfully synthesized by using stir casting route. The samples are characterized by various techniques such as X-rays diffraction (XRD), high resolution-scanning electron microscopy (HR-SEM), scanning electron microscope (SEM) equipped with energy dispersive analysis of X-ray spectroscopy (EDAX). Microstructural investigations reveal the presence of the WC, TiC and graphite particles in the hybrid composites. It is observed that graphite reinforced hybrid composite shows better wear resistance than hybrid composite without graphite addition. In all the cases cast copper specimen shows highest wear rate. The observed friction and wear behavior have been explained on the basis of the presence tribofilm on the worn surface. Microstructural characterization of the worn surface and wear debris show that the mechanism of wear is primarily adhesive and oxidative in case of cast copper, where as it is a mix of adhesive and abrasive wear in case of hybrid composites. Wear debris analysis also helps to understand the wear mechanism involved during dry sliding.

/pdf/effect-of-ceramic-graphite-reinforcement-on-dry-sliding-wear-4pdtg3qq8o.pdf

Effect of Ceramic/Graphite Reinforcement on Dry Sliding Wear Behavior of Copper Metal Matrix Hybrid Composites

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Microstructural measurement and artificial neural network analysis for adhesion of tribolayer during sliding wear of powder-chip reinforcement based composites

Reality based skills development approach in welding technology: An overview

Investigation on the effects of cooling rate on surface Texture, corrosion behaviour and hardness of pure copper

Effect of Ceramic/Graphite Reinforcement on Dry Sliding Wear Behavior of Copper Metal Matrix Hybrid Composites