The influence of coating processes and process parameters on surface erosion resistance and substrate fatigue strength

1988 ◽  
Vol 36 (1-2) ◽  
pp. 433-444 ◽  
Author(s):  
Javaid I. Qureshi ◽  
W. Tabakoff
2018 ◽  
Vol 144 (7) ◽  
pp. 06018004 ◽  
Author(s):  
Soo-Min Ham ◽  
Ilhan Chang ◽  
Dong-Hwa Noh ◽  
Tae-Hyuk Kwon ◽  
Balasingam Muhunthan

2020 ◽  
Author(s):  
Xijin Zhang ◽  
Xudong Fan ◽  
Chanjuan Han ◽  
Chen Wang ◽  
Xiong (Bill) Yu

Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake

Abstract Accurate prediction of the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and the often fatal consequences of fatigue failures. The work presented in this paper is an extension of the previous paper submitted to OMAE 2019. The main objective of this manuscript is to utilize Artificial Intelligence (AI) to predict fatigue strength, based on composition and process parameters, using the fatigue dataset for carbon and low alloy steel available from the National Institute of Material Science (NIMS) database, MatNavi. A deep learning framework Keras is used to build a Neural Network (NN), which is trained and tested on the data set obtained from MatNavi. The fatigue strength values estimated using NN are compared to the values predicted by the gradient boosting algorithm, which was the most accurate model in the OMAE 2019 paper. The comparison is done using metrics such as root mean square error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2) and Explained Variance Score (EVS). Thereafter, the trained NN model is used to make predictions of fatigue strength for the simulated data (1 million samples) of input parameters, which is then used to generate conditional probability tables for the Bayesian Network (BN). The main advantage of using BN over previously used machine learning algorithms is that BN can be used to make both forward and backward propagation during the Bayesian inference. A case study illustrating the applicability of the proposed approach is also presented. Furthermore, a dashboard is developed using PowerBI, which can be used by practicing engineers to estimate fatigue strength based on composition and process parameters.


Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake

Abstract Accurately estimating the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and often fatal consequences of fatigue failures. The main objective of this manuscript is to perform data mining on the fatigue dataset for steel available from the National Institute of Material Science (NIMS) MatNavi. The cross-industry process for data mining (CRISP-DM) approach was followed in the paper, in order to gain meaningful insights from the dataset and to estimate the fatigue strength of carbon and low alloy steels, using composition and processing parameters. Of the six steps of the CRISP-DM approach, special emphasis has been placed on steps 2 to 5 (i.e. data understanding, data preparation, modeling and evaluation). In step 4 (i.e. modeling), a range of machine learning (parametric and non-parametric) is explored to predict the fatigue strength, based on the composition and process parameters. Various algorithms were trained and tested on the dataset and finally evaluated, using metrics such as root mean square error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2) and Explained Variance Score (EVS).


2019 ◽  
Vol 52 (9-10) ◽  
pp. 1252-1263 ◽  
Author(s):  
Abhishek Kumar Tiwari ◽  
Amit Kumar ◽  
Navin Kumar ◽  
Chander Prakash

Residual stresses are induced in the material during manufacturing operations, which considerably affect the fatigue performance and the lifespan of a mechanical work piece. The nature, magnitude, and distribution of residual stresses decide their beneficial or detrimental effects. Past research efforts concluded that mechanical process parameters influence residual stress nature, distribution, and the magnitude. Nevertheless, how residual stress generation depends on the process parameters, is not well investigated especially in the case of a drilling operation. In fact, the residual stress field is required to be regulated near drilled holes to improve the fatigue strength of structural joints, especially in the aircraft industry. Accordingly, this work attempts to estimate the drilling-induced micro-residual stress distribution near the drilled hole. In addition, the effect of drilling speed on residual stress distribution has also been studied. A nanoindentation technique is used to follow-up precise distribution of micro-residual stresses near the holes drilled at three different drilling speeds of 700, 900, and 1100 r/min. The outcomes indicate the presence of compressive residual stresses near the hole. In addition, an increase in residual stress level is noticed with an increase in the drilling speed up to 900 r/min. A uniform distribution of residual stresses is observed near the hole when drilled at a higher drilling speed of 1100 r/min. These findings may be useful in planning an improved drilling operation to produce beneficial residual stress distribution. This may ultimately improve the fatigue strength and the service life of mechanical components or structures with drilled holes.


Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1544
Author(s):  
Mirosław Szala ◽  
Leszek Łatka ◽  
Michał Awtoniuk ◽  
Marcin Winnicki ◽  
Monika Michalak

The study aims to elaborate a neural model and algorithm for optimizing hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13 wt% TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN) were employed to elaborate the model, and the multi-objectives genetic algorithm (MOGA) was used to optimize both properties and cavitation erosion resistance of the coatings. Results were analyzed with MATLAB software by Neural Network Toolbox and Global Optimization Toolbox. The fusion of artificial intelligence methods (ANN + MOGA) is essential for future selection of thermal spray process parameters, especially for the design of ceramic coatings with specified functional properties. Selection of these parameters is a multicriteria decision problem. The proposed method made it possible to find a Pareto front, i.e., trade-offs between several conflicting objectives—maximizing the hardness and cavitation erosion resistance of Al2O3-13 wt% TiO2 coatings and, at the same time, minimizing their porosity.


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