Faculty Publications

Now showing 1 - 3 of 3

Exploring grinding and burnishing as surface post-treatment options for electron beam additive manufactured Alloy 718
(Elsevier B.V., 2020) Karthick Raaj, R.; Vijay Anirudh, P.; Karunakaran, C.; Kannan, C.; Jahagirdar, A.; Joshi, S.; Balan, A.S.S.
Numerous additive manufacturing (AM) techniques have been developed over the past decade. Features like immense freedom of intricate part design and shorter lead time make AM routes promising for a wide range of applications spanning aerospace, marine and automobile sectors. Among the various metal AM processes, Electron Beam Additive Manufacturing (EBAM) is being widely explored to realise the potential of Ni-based superalloys and Ti alloys for varied high-performance applications. A novel attempt has been made in this paper to assess the surface integrity of as-built EBAM nickel-based superalloy 718 (AB) subjected to grinding (G), Low Plasticity Burnishing (LPB) and their sequential combination. Apart from their influence on sub-surface microstructures, the effect of process variables during the above post-treatments on the residual stress profiles was also investigated. Results revealed that G + LPB results in about 0.6 ?m lower surface roughness, 17% improved microhardness compared to AB + LPB, and higher compressive surface residual stress as compared to LPB processed EBAM samples. The sequential grinding and LPB - improved microhardness, was also found to extend about 500 ?m more when compared to the LPB process. The G + LPB, which is greatly influenced by the prior grinding, smoothens the surface and thus results in a better surface finish. Highest hardness, superior surface finish, reduced porosity and improved compressive residual stress were observed in samples that adopted the AB + G + LPB sequence over other samples, with the LPB step at 40 MPa yielding the best results. © 2020 Elsevier B.V.
Effect of cryogenic grinding on fatigue life of additively manufactured maraging steel
(MDPI AG, 2021) Balan, A.S.S.; Kannan, C.; Kumar, A.V.; Hariharan, H.; Pimenov, D.Y.; Giasin, K.; Nadolny, K.
Additive manufacturing (AM) is replacing conventional manufacturing techniques due to its ability to manufacture complex structures with near?net shape and reduced material wastage. However, the poor surface integrity of the AM parts deteriorates the service life of the components. The AM parts should be subjected to post?processing treatment for improving surface integrity and fatigue life. In this research, maraging steel is printed using direct metal laser sintering (DMLS) process and the influence of grinding on the fatigue life of this additively manufactured material was investigated. For this purpose, the grinding experiments were performed under two different grinding environments such as dry and cryogenic conditions using a cubic boron nitride (CBN) grinding wheel. The results revealed that surface roughness could be reduced by about 87% under cryogenic condition over dry grinding. The fatigue tests carried out on the additive manufactured materials exposed a substantial increase of about 170% in their fatigue life when subjected to cryogenic grinding. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Grinding parameters prediction under different cooling environments using machine learning techniques
(Taylor and Francis Ltd., 2023) Prashanth, G.S.; Sekar, P.; Bontha, S.; Balan, A.S.S.
Selection of optimum process parameters is vital for performing a sound grinding operation on Inconel 751 alloy. This paper co-relates the relationship between the most influential input parameters like cutting velocity, depth of cut, feed rate, and environmental conditions to the output parameters, namely, tangential grinding forces, normal grinding forces, temperature, and surface roughness. Three types of machine-learning (ML) algorithms such as support vector machine (SVM), Gaussian process regression (GPR), and boosted tree ensemble techniques are employed to develop a ML model for predicting the output variables during grinding operation of Inconel 751. In order to develop a better ML model, K-fold technique is employed on a total of 81 datasets which are extracted from experimental studies. ML models developed from different algorithms are compared based on performance metrics like R2 score and root-mean-square error (RMSE). GPR algorithm exhibits best results with relatively better R2 score and RMSE value in predicting grinding forces and temperature at wheel work interface. From analyzing the ML models, it is found that cooling environments determined the output grinding parameters to a greater extent when compared with the input grinding parameters. © 2022 Taylor & Francis.

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