A Cutting-Edge Survey of Tribological Behavior Evaluation Using Artificial and Computational Intelligence Models

Selvaraj, Senthil Kumaran; Raj, Aditya; Dharnidharka, Mohit; Chadha, Utkarsh; Sachdeva, Isha; Kapruan, Chinmay; Paramasivam, Velmurugan

doi:https://doi.org/10.1155/2021/9529199

Advances in Materials Science and Engineering

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Microstructure, Properties and Tribological Behaviour of Advanced Functional Materials

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Review Article | Open Access

Volume 2021 | Article ID 9529199 | https://doi.org/10.1155/2021/9529199

A Cutting-Edge Survey of Tribological Behavior Evaluation Using Artificial and Computational Intelligence Models

Senthil Kumaran Selvaraj,¹Aditya Raj,²Mohit Dharnidharka,¹Utkarsh Chadha,¹Isha Sachdeva,²Chinmay Kapruan,¹and Velmurugan Paramasivam³

Academic Editor: Ali Khorram

Received28 Oct 2021

Revised14 Dec 2021

Accepted16 Dec 2021

Published29 Dec 2021

Abstract

Any metal surface’s usefulness is essential in various applications such as machining and welding and aerospace and aerodynamic applications. There is a great deal of wear in metals, used widely in machines and appliances. The gradual loss of the upper metal layers in all metal parts is inevitable over the machine or component’s lifetime. Artificial intelligence implementations and computational models are being studied to evaluate different metals’ tribological behavior, as technological progress has been made in this field. Different neural networks were used for different metals. They are classified in this paper, together with a description of their benefits and inconveniences and an overview and use of the different types of wear. Artificial intelligence is a relatively new term that uses mechanical engineering. There is still no scientific progress to examine various metal wear cases and compare AI and computational models’ accuracy in wear behavior.

1. Introduction

Given the potential and technological developments we have experienced in an industrial revolution, we have a long path to cover as engineers. The wear behavior varies from metal to metal, mainly depending on its properties or the method used, and AI has helped companies better understand metals’ wearing behavior and deploy them in processes or machinery because the speed with precision is more critical in the industry, helping companies increase their response speed. Artificial intelligence is a computer science field dealing with the simulation of computer systems to imitate human intelligence. AI is a large field in computers and other areas such as economics, theory of control, probability, optimization, and bilingualism. AI is such a phenomenon that it can model and find patterns in complex inputs and outputs on the given data. It has been made an essential element of our lives without even realizing weather prediction, mechanical wear and tear, the probability of different diseases, and many more, as recommended by Netflix and YouTube. An AI process consists of data acquisition and correction to enhance its earlier forecasts over time. Mechanical engineering, as technology helps mechanical design or engineering works, is AI’s biggest consumer. All sections of mechanical engineering benefiting highly from AI are robotics, automation, and sensor technology.

Wear means that the substance is consistently removed from or deformed from a solid surface while moving about another substance or fluid. Wear is a natural phenomenon when two bodies are rubbed or slipped. Mechanical and chemical behavior and combinations of these factors, such as corrosion, erosion, and abrasion, cause wear on the solid surface of the material. Tribology is the wear science involving friction, lubrication, and wear applications and concepts. Wear is an essential characteristic of products that must be carefully examined before producing a product. Other processes such as fatigue, material failure, and loss of functionality cause surface degradation. In the manufacturing industry, wear is a constant inconvenience, and it is expensive because it is causing loss of part and wear deterioration. The wear of the active surfaces, near-surface compositions, and fragmentation leads to wear debris caused by the plastic deformation of metals. The wear waste produced varies between nanometers and thousands. Wear can be correlated with the help of the wear rate. The material mass or volume removed by the sliding distance of each unit is the ratio. The wear volume per unit is usually expressed as a dimensionless entity called the wear coefficient on the unit’s sliding distance (K). The wear mechanism is generally considered a negative feature and is unwanted in most practical contexts, but it has many applications. Wear, for example, is affected by processes such as filing, lapping, sanding, and polishing used to create finished surfaces.

They also collected datasets, if provided, software used, benefits, and drawbacks, and all studies referred to for that survey were fully applicable to explain the subject matter of the case studies cited beforehand and cover artificial intelligence and calculation models as shown in Figure 1.

2. Types of Wear

We must first understand the various types of wear before applying artificial intelligence principles to evaluate wear behavior. Wear can occur due to a single mechanism or a complex combination of mechanisms. To solve a wear problem, we must first understand the various wear mechanisms at work. Abrasion or surface deterioration occurs when the force acting on the surface is caused by load stress or friction. When chemical reactions alter a material body’s outer layer, the wear mechanisms responsible are adhesion and tribo-oxidation. The sections that follow describe the various types of clothing.

The most common wear process encountered in the industry is abrasive wear. According to reports, abrasion is to blame for 50% of all wear issues. Abrasive wear is the substance loss caused by hard particles being forced against and moved along a solid surface [1]. The wear mechanism that causes abrasive wear is referred to as abrasion (scraping off). Abrasion occurs when a solid body with a rough surface collides with a coupling part with a soft surface. Abrasive wear is classified into two types based on the type of contact and the contact environment.(a)Three-body abrasion: A third dimension is included in sliding two surfaces (as shown in Figure 2), hence blaming the third body for material removal from both surfaces (particles are usually assumed the third body).(b)Two-body abrasion: This occurs when the hard material on one surface absorbs material from the opposite surface. Two-body abrasion is always possible because the asperities that cause removal on a hard surface can never be removed entirely, even with the most advanced polishing. As a result, wear debris forms between the two sliding surfaces. Long-term two-body abrasive wear causes three-body abrasion, which causes more wear than two-body abrasion.

Three mechanisms commonly cause abrasive wear:(1)Ploughing: The displacement of particles away from the wear particles causes the formation of grooves. Ridges form on the edges of the grooves and are removed by abrasive materials moving through them.(2)Cutting is the removal of material from a solid surface in the form of primary debris or microchips. This method is similar to traditional machining.(3)Fragmentation occurs when the indenting material is removed from the surface, resulting in a localized fracture.

Adhesive wear: This occurs due to the interaction of asperities between two surfaces [2]. Formalized paraphrase adhesion is the wear mechanism that causes adhesive wear (stickiness). It occurs when the compositions of the two metals are incredibly similar. A bond can form because of this compatibility, allowing parts to seize or become cold-welded together (as seen in Figure 3). Because of these bonded sections’ swaying and sliding motion, abrasion occurs on the bordering surfaces. Adhesive wear is classified into two types:(a)Classifying wear due to relative motion/direct contact between two surfaces along with plastic deformation, leading to transfer of metal debris onto the other metal’s surface during wear.(b)Cohesive-adhesive forces hold two faces together even when a significant distance separates them. The actual transition could occur.

Surface fatigue: This occurs when the surface of a material is stressed. As a result of this phenomenon, which thermal or mechanical forces can cause, surfaces crack. The fatigue wear caused due to particle detachment is mainly because of cyclic increase of metal surface microcracks (as shown in Figure 4). Each period increases the crack by a small amount until a surface microcrack develops. As a result, large surface cracks develop over time, posing a direct threat to the components.

Corrosive wear/oxidation wear: This material deterioration combines corrosion and wear. It is defined as a wear phase in which materials slide against each other in a corrosive environment. It is a type of material degradation that combines corrosion and wear. It is defined as a corrosive wear process in which materials slide against each other. When there is no sliding, corrosion on the surfaces forms a micrometer-thick film layer, reducing or even preventing further corrosion. This film is chipped away during the sliding application, exposing the metal surface to further corrosion (as shown in Figure 5). This process of wear occurs in the presence of harmful or oxidizing metals. Oxidation, also known as rust, is a severe form of corrosive wear. Oxides create a decrease in the equilibrium of friction between surfaces or are often a more significant challenge to work with than the materials involved and can be used as excellent abrasives.

Cavitation wear: A liquid medium causes cavitation wear on metal surfaces. It happens when cavities in a liquid flowing near the material are nucleated, developed, and violently collapsed repeatedly. Because of the rapid changes in liquid pressure, small vapor-filled craters with low vapor pressure form. Cyclic stress occurs when these craters or voids collapse near a metal surface. It causes surface fatigue, which contributes to the wear of the base material over time.

3. Wear Tests

The wear rate is defined as the volume loss per unit sliding distance. It is a dimensionless quantity (K) that can assess wear damage. The wear rate is defined as the body’s height adjustment ratio to the relative sliding distance duration.

Under normal conditions, wear progresses through three stages, the first of which is the primary stage, during which the surfaces involved adjust to one another, and the wear rate can be high or low. The second level, also known as the mid-age process, follows the first and is distinguished by a consistent wear rate. This process consumes the majority of the component’s operating life. Finally, the component reaches the tertiary level, also known as the “old-age phase.” The surfaces involved experience rapid wear, resulting in the component’s premature failure [3–11].

Wear tests are classified as follows:(1)Pin-on-Disc Wear Test. This is one of the most common ways to test wear rates and wear resistance. It is popular due to its ability to simulate various wear modes including omnidirectional, bidirectional, unidirectional, and quasi-rotational wear. Many different materials can be tested for wear. A test of wear resistance between PTFE (polytetrafluoroethylene) and its composites [12] was done using a pin-on-disc wear test, and it was observed that as the load increased, the coefficient of friction decreased. Pure PTFE experienced maximum wear followed by PTFE with 17% GFR, PTFE with 25% bronze, and PTFE with 35% carbon which experienced minimum wear.(2)Block-on-Ring Wear Test. This is widely used to evaluate the sliding wear behavior of materials in various simulated conditions. It also helps in ranking material couples for specific tribological applications. A test of woven glass fibers is conducted on a block-on-ring wear testing machine [13], and it was found that aramid fiber-reinforced composites are less prone to wear than simple glass fabrics. Also, weaved 300 glass fabrics displayed better wear resistance than woven 500 glass fabrics.(3)Abrasion Wear Test. This is used to test the abrasive resistance of materials such as metals, composites, ceramics, thick thermal spray, and weld overlay coatings.(4)Cavitation Erosion Vibratory Test. The surface of the test sample is immersed in liquid, and the cavitation process is induced by vibrational erosion. Ultrasonic waves consisting of alternate expansions and compressions are transmitted into the liquid, which causes erosion (material loss) of the surface of the sample. This method is used to determine the relative wear resistance of test samples to cavitation erosion. In a test between HN steel and AISI 304 steel [14], the samples’ cavitation wear increased with the decrease in the pH value of the water. Also, AISI 304 steel was more resistant to wear than HN steel.(5)Ball-on-Flat Wear Test. This allows observing the wear tracks’ dynamic load, friction force, and depth. Three different teeth from three different young males were tested using this apparatus [15], and it was observed that, for all the three teeth, three different wear scars were observed. The enamel layer displayed better wear resistance and had a lower friction coefficient than the dentin region.

4. Wear Testing Case Studies

Tables 1 to 5 discuss various case studies that involve various wear tests, briefly discussing the test and the implementations or additions in the metal workpiece chosen along with the observed outcomes.

5. Computational and Artificial Intelligence Models to Detect Wear Behavior

Artificial neural networks are a subset of AI widely used in mechanical engineering. ANNs are modelled after the biological neural system like an animal brain and are made up of neurons linked to each other that perform complex computations in the same way that the brain does. Dr. Robert Hecht–Nielson defined ANNs as “a computing system composed of several simple, highly interconnected processing elements that process information through their dynamic state response to external inputs.” The networks are widely applicable in solving classification and optimization problems, predictions, pattern recognition, etc. Because ANNs are adaptable, they can imitate linear and nonlinear relationships since the data are divided into various layers, making them well generalizable. These are trained using the datasets defined for training and then further used to predict the output values with the help of different algorithms (Figure 6) [4, 5].(1)ANNs typically have three main layers. Input layer: The layer to which input data and patterns are fed is always a single input layer.(2)Hidden layers: There could be several of these layers. Behind the scenes, processing occurs, and the output is calculated based on “weights,” which determine the significance of a specific characteristic. These layers also remove inessential data from the input data before sending them to the hidden layer, next in line for processing.(3)The endmost hidden layer is linked to the output layer, which provides the final output value(s).

The center of NNs is backpropagation. It is an algorithm through which the neural network corrects itself with each iteration that relies on weights.

6. Summary and Conclusions

The research works discussed briefly in this review propose various systems for supervising the machining process, tool wear monitoring, determination of wear state for a tool, and many more. Significant research has been done involving ANNs with the LVM (as shown in Tables 6 and 7) algorithm training the models, resulting in highly generalized and fault-tolerant models; however, LVM can only provide a local optimum and may not respond to flat functions, producing unwanted results, and the starting point is way far from the optimal.

Some studies consider the ANFIS, adaptive neuro-fuzzy interface system, method that combines ANN and fluidic logic, specifically the “Takagi–Sugeno fuzzy interference system,” which can capture neural networks fumigating logic in one. However, this model may not perform well for many inputs, i.e., this model fails in a big data paradigm. The surface roughness and wear were predicted using RNNs, i.e., ANNs having memory; hence, they are more suitable for a constantly developing environment of such wear behavior of tools. Surface wear was detected using random forests and multilayer perceptrons based on surface isotropy levels. Random forests are superior because MLPs require parameter tuning, and their output is nearly identical to that of RFs. These methods for various processes are also discussed in some research that encompasses most of the approaches [80–92].

6.1. Accuracies Achieved in Recent Research Works

Using a two-hidden-layer neural network, Kumar and Singh [53] obtained a normalized standard error of 0.00085. At the same time, Çetinel et al. [54], who also used a two-hidden-layer network but with the addition of the Levenberg–Marquardt algorithm, found an average error of 2.461% for wear (in micrometers) and 0.245% error for microhardness (in HV). A least square support vector machine to predict wear behavior in [56] yielded an average of 1.2 percent better results on 52 runs than the RSM model. Kolodziejczyk [58] used PCA preprocessing and the LVM algorithm to achieve a mean relative error of 1.8 percent, three times lower than that in previous studies. A multilayer perceptron model was used in [64], which yielded 0.0186 and 0.0180 training and testing residual errors, respectively. The SOM model had a higher correlation coefficient than the ART2 model in [65], with 0.964 and 0.946 for the training and test sets. The ANN was combined with the Taguchi method in [55], and a 99.5 percent confidence level was observed between predicted and actual wear rates and coefficients of friction. In [93], the ANN with one hidden layer had a more significant sum of squares error (SSE) of 0.025 and 0.25 for training and testing, respectively, whereas the ANN with two hidden layers had 0.008 and 0.46 SSEs for training and testing. As a result of the lower SSE, the two-hidden-layer networks were chosen, with an RMSE of 2.64 percent on average. The ANFIS models—sigmoidal, triangular, Gaussian, and bell-shaped MFs—were used [59]. The most accurate model was sigmoidal MF, which had a regression coefficient of 0.96775. RFs and MLPs were used in [62], with RFs having a better accuracy of 33 to 44 percent and an error of 0.2457 micrometers than the MLP’s 0.4139. An ANFIS was used for various membership functions [70]. The RMSE was in the order of E-11, which was 0.557 for the ANN. The Sugeno-type ANFIS model had the best correlation coefficient of 97.74 percent with gbellmf membership. Nagaraj and Gopalakrishnan [66] reported an MSE of 0.0904 and an MAE of 0.1257. In [73], various ML techniques model various parameters, with MLPs better in 3/4 of them and RFs taking one of the parameters. MLPs were found to have a 52 percent accuracy rate. The ANFISs appear to have the least amount of error.

6.2. Open Issues

Multiple systems have been proposed in recent research to address the supervision process in machining, tool wear monitoring, tool wear detection, and so on. More researchers use ANNs with the LVM algorithm to train fault-tolerant and well-generalized models, but the LVM only provides a local optimum and may not work for flat functions. If the starting point is too far from the optimal, it may also produce undesirable results. The ANFIS, adaptive neuro-fuzzy interface system, is a combination of ANN and fuzzy logic used in a few papers, specifically the Takagi–Sugeno fuzzy interference system, which can capture the essence of both neural nets and fuzzy logic in one [94–102]. However, this model may not work well for many inputs, i.e., this model fails in a big data paradigm. RNNs, which are technically ANNs with memory and thus more suited for such ever-changing dynamic environments as tool wear, were also used to predict wear and surface roughness [103–106]. Surface wear was also predicted using random forests and multilayer perceptrons and surface isotropy levels. MLPs require parameter tuning, and their output is nearly identical to that of RFs, so random forests are preferable. These various processes are also discussed in [107], which encompasses most approaches.

6.3. Future Directions

Wear analysis using artificial intelligence is a relatively new concept. Formal result: Accordingly, it was discovered that there is less work on AI than aluminum (e.g., FGP grey-coated or NiCrBSi-coated aluminum) writable composites (e.g., polymer-reinforced glass), which indicates that it is to be expected since less work has been done on AI (e.g., plastic/FGP-NiCr alloyed glass) to grasp fully [80, 108].Further study is required to understand the full capabilities of using AI. This state-of-the-art technology for analyzing artificial neural networks is now being utilized for efficient and economical wear-resistant materials. Tool wear is one of the most common aspects of the machining process that needs to be analyzed. Research can be done on the tool metal’s wear behavior in the future, and the metal can be modified and tested for wear. New research opportunities can be found to find an ideal metal for machining processes. Artificial neural networks for wear analysis can help identify the most efficient coating materials for various substrates to increase the substrate’s wear resistance with accurate predictions, which is inefficient and time-consuming when identified using traditional methods. Artificial intelligence is currently limited to analyzing wear for various materials used in manufacturing and production. Still, the main benefit of using AI is studying a wide range of data and making accurate predictions. More experimentation is needed to make the most of this technology, which will allow industries to predict the time and type of wear that will occur on a material ahead of time, allowing them to continue operating without interruption [108–114].

Abbreviation

ANN:	Artificial neural network
NN:	Neural network
ML:	Machine learning
GD:	Gradient descent
LMA:	Levenberg–Marquardt algorithm
BP:	Backpropagation
GN:	Gaussian network
SVR:	Support vector regression
LSSVM:	Least square support vector machine
RSM:	Response surface methodology
RBF:	Radial basis function
MLP:	Multilayer perceptron
SOM:	Self-organizing map
GF:	Generalized feedforward
POD:	Proper orthogonal decomposition
BR:	Bayesian regulation
RP:	Resilient backpropagation
SCG:	Scaled conjugate gradient
FIS:	Fuzzy inference system
FZM:	Fuzzy clustering method
LR:	Linear regression
ELM:	Extreme learning method
RF:	Random forest
GBR:	Gradient boosting regression
GPR:	Gaussian process regression.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

Senthil Kumaran Selvaraj and Aditya Raj have Contributed Equally AR and SKS conceptualized the research idea and ran the software. SKS performed the methodology, validated the data, and administered the project. AR, MD, UC, IS, and CK were involved in formal analysis and wrote the original draft. AR, MD, and UC investigated the data and obtained the resources. AR, MD, UC, and IS curated the data. UC and CK visualized the data and reviewed and edited the paper. SKS and UC supervised the study. All the authors have read and agreed to the published version of the manuscript.

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