• Artificial Intelligence Based Approach for Predicting Fatigue Strength Using Composition and Process Parameters 

      Keprate, Arvind; Ratnayake Mudiyanselage, Chandima (International Conference on Offshore Mechanics and Arctic Engineering;Volume 3: Materials Technology, Chapter; Conference object; Peer reviewed, 2020-12-18)
      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 ...
    • Combining Computational Fluid Dynamics and Gradient Boosting Regressor for Predicting Force Distribution on Horizontal Axis Wind Turbine 

      Bagalkot, Nikhil; Keprate, Arvind; Orderløkken, Rune (Vibration;Volume 4 / Issue 1, Peer reviewed; Journal article, 2021-03-14)
      The blades of the horizontal axis wind turbine (HAWT) are generally subjected to significant forces resulting from the flow field around the blade. These forces are the main contributor of the flow-induced vibrations that ...
    • Exploratory Data Analysis of the N-CMAPSS Dataset for Prognostics 

      Keprate, Arvind; Chatterjee, Supratik (IEEE International Conference on Industrial Engineering and Engineering Management;2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Peer reviewed; Journal article, 2021-01-20)
      In the recent years, industries such as aeronautical, railway, and petroleum has transitioned from corrective/preventive maintenance to condition based maintenance (CBM). One of the enablers of CBM is Prognostics which ...
    • Kvasir-Instruments and Polyp Segmentation Using UNet 

      Keprate, Arvind (Nordic Machine Intelligence (NMI);Vol. 1 No. 1 (2021): MedAI: Transparency in Medical Image Segmentation, Peer reviewed; Journal article, 2021-11-01)
      This paper aims to describe the methodology used to develop, fine-tune and analyze a UNet model for creating masks for two datasets: Polyp Segmentation and Instrument Segmentation, which are part of MedAI challenge. For ...
    • Machine learning-based abnormality detection approach for vacuum pump assembly line 

      Garg, Paras; Patil, Amitkumar; Soni, Gunjan; Keprate, Arvind; Arora, Seemant (Reliability: Theory & Applications;Special Issue No 2(64), Volume 16, November 2021, Peer reviewed; Journal article, 2021-11-04)
      The fundamental basis of Industry 4.0 is to make the manufacturing sector more productive and autonomous. In the manufacturing sector, practitioners always long for product quality improvement, reducing reworking costs, ...
    • Multiscale Damage Modelling of Composite Materials Using Bayesian Network 

      Keprate, Arvind; Moslemian, Ramin (Lecture Notes in Civil Engineering;Volume 110, Chapter; Peer reviewed; Conference object, 2020-12-13)
      Fibre reinforced polymers or as they widely are known composite materials, have made it possible to develop and manufacture large wind turbine and tidal blades central to competitiveness of wind and tidal turbines against ...
    • Predicting Remaining Fatigue Life of Topside Piping Using Deep Learning 

      Keprate, Arvind; Chatterjee, Supratik (International Conference on Applied Artificial Intelligence (ICAPAI);2021 International Conference on Applied Artificial Intelligence (ICAPAI), Chapter; Peer reviewed; Conference object, 2021-06-29)
      Topside piping is the most commonly failed equipment in the Petroleum and Maritime industry. The prominent degradation mechanism causing piping failure is fatigue which results in unnecessary hydrocarbon release from these ...
    • Prediction of Stress Correction Factor for Welded Joints Using Response Surface Models 

      Keprate, Arvind; Donthi, Nikhil (ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering;Volume 2: Structures, Safety, and Reliability, Chapter; Peer reviewed; Conference object, 2021-10-11)
      While performing fatigue reliability analysis of the butt-welded joints it is vital to estimate the Stress Concentration Factor at these joints. A common approach adopted by industry to estimate the SCF at weld toes is to ...
    • Residual Stress Prediction Of Welded Joints Using Gradient Boosting Regression 

      Keprate, Arvind; Bhardwaj, Sachin; RATNAYAKE MUDIYANSELAGE, Chandima; Ratnayake Mudiyanselage, Chandima (Communications in Computer and Information Science;Volume 1616, Chapter; Peer reviewed; Conference object; Journal article, 2022-07-23)
      Welding residual stress (WRS) estimation is highly nonlinear process due to its association with high thermal gradients generated during welding. Accurate and fast estimation of welding induced residual stresses in critical ...
    • Review of Weld Quality Classification Standard and Post Weld Fatigue Life Improvement Methods for Welded Joints 

      Bhardwaj, Sachin; Keprate, Arvind; Chandima Ratnayake, Mudiyanselage (Lecture Notes in Civil Engineering;Volume 110, Chapter; Peer reviewed; Conference object, 2020-12-13)
      In typical fabricated structures, there are hundreds of joints, which acts as a potential location for fatigue cracking. Ageing and life extension (ALE) of such structures could be developed further by creating an optimal ...
    • Use of Analytical Hierarchy Process in Selecting the Optimum Equipment for Execution at a Construction Project 

      Keprate, Arvind; Kumar, Gaurav Thakur (IEEE International Conference on Industrial Engineering and Engineering Management; 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Chapter; Peer reviewed; Conference proceeding, 2020-01-12)
      There are plethora of issues associated with construction project management process, the most important being the selection of the optimum equipment for execution at a construction site. Different people have different ...