An application of learning machine methods in prediction of wear rate of wear resistant casting parts

Slavković, Radovan; Jugovic, Zvonimir; Dragićević, Snežana; Jovičić, Aleksandar; Slavkovic, Vladimir

doi:10.1016/j.cie.2012.12.021

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…SVM as an alternative machine learning technique has emerged as a powerful learning machine algorithm and achieved accurate prediction results in many areas (Sanchez Lasheras et al, 2014;Slavkovic, Jugovic, Dragicevic, Jovicic, & Slavkovic, 2013;Lee et al, 2007;Aich & Banerjee, 2014;Kara et al, 2011;KavousiFard et al, 2014;Tang et al, 2009;Yuvaraj et al, 2013). Slavkovic et al (2013) proposed two machine learning techniques based on SVM for predicting wear rate of floatation balls made of white iron casting with low chromium content.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…Slavkovic et al (2013) proposed two machine learning techniques based on SVM for predicting wear rate of floatation balls made of white iron casting with low chromium content. Sanchez Lasheras et al (2014) used an evolutionary SVR algorithm combining SVM and evolutionary algorithm to predict the thickness of the chromium layer in a hard chromium plating process.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

Modeling of steelmaking process with effective machine learning techniques

Laha

Ren

Suganthan

2015

Expert Systems with Applications

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Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Modeling of steelmaking process with effective machine learning techniques

Laha

Ren

Suganthan

2015

Expert Systems with Applications

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“…The data that were used in regression was generated from a steady-state computational fluid dynamic (CFD) simulation. Slavkovic et al [99] used the data from wear rate of iron casting to train an SVM and improved support vector machine (ISVM) techniques. The errors of predicted results were 5.85% and 6.67%, respectively [100].…”

Section: Machine Learningmentioning

confidence: 99%

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Kurdi

Alhazmi

et al. 2020

Materials

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To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

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“…Beside the above, the participant authors found that the reported works in the literature have been presented in different format. For example, the wear performance of a metal have been presented in wear rate [5], weight loss [6], volume loss [7], specific wear rate and/or wear resistance [8,9]. Each form of these wear units introduces different understanding which in turn misleading the readers and establish confusing date for comparison purposes.…”

Section: Introductionmentioning

confidence: 99%

Wear and Frictional Behaviour of Metals

Alotaibi

Yousif

Yusaf

2014

AMR

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Abstract. In the current study, wear and frictional performances of different metals are investigated under different operating parameters against stainless steel counterface under dry contact conditions. The experiments performed using block on ring machine. Microscopy was used to examine the damage features on the worn surface and categorize the wear mechanism. Thermal imager was used to understand the thermal loading in the interface during the rubbing process. The results revealed that the operating parameters influence the wear and frictional behaviour of all the metals. Brass metal exhibited better wear and frictional behaviour compared to others. Three different wear mechanisms were observed, i.e. two body abrasion (Brass), three body abrasion (Aluminium) and adhesive (Mild Steel).

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An application of learning machine methods in prediction of wear rate of wear resistant casting parts

Cited by 11 publications

References 11 publications

Modeling of steelmaking process with effective machine learning techniques

Modeling of steelmaking process with effective machine learning techniques

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Wear and Frictional Behaviour of Metals

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