V
Vigneashwara Pandiyan
Researcher at Swiss Federal Laboratories for Materials Science and Technology
Publications - 26
Citations - 617
Vigneashwara Pandiyan is an academic researcher from Swiss Federal Laboratories for Materials Science and Technology. The author has contributed to research in topics: Belt grinding & Computer science. The author has an hindex of 8, co-authored 18 publications receiving 249 citations. Previous affiliations of Vigneashwara Pandiyan include Katholieke Universiteit Leuven & Nanyang Technological University.
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Journal ArticleDOI
In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm
TL;DR: In this paper, the authors proposed a tool condition monitoring predicting system, which not only helps to optimise the utilisation of the tool's life cycle but also secures the surface quality of finished components.
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Predictive Modelling and Analysis of Process Parameters on Material Removal Characteristics in Abrasive Belt Grinding Process
TL;DR: In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model is used to determine material removal in a compliant abrasive belt grinding process and the results showed that the removed material from a surface due to the belt grinding process has a non-linear relationship with the process variables.
Journal ArticleDOI
Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review
Vigneashwara Pandiyan,Vigneashwara Pandiyan,Sergey Shevchik,Kilian Wasmer,Sylvie Castagne,Tegoeh Tjahjowidodo +5 more
TL;DR: It is reported that most of the Artificial Intelligence algorithms available are not fully exploited for monitoring and modelling in abrasive finishing and emphasizes on bridging this gap.
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In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning
Vigneashwara Pandiyan,Pushparaja Murugan,Tegoeh Tjahjowidodo,Wahyu Caesarendra,Wahyu Caesarendra,Omey Mohan Manyar,David Jin Hong Then +6 more
TL;DR: The first investigative stage of semantic segmentation of weld seam removal states using encoder-decoder convolutional neural networks (EDCNN) is presented, able to monitor weld profile geometry evolution taking into account the varying belt grinding parameters during machining which will allow further process optimisation.
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Use of Acoustic Emissions to detect change in contact mechanisms caused by tool wear in abrasive belt grinding process
TL;DR: In this article, a single grain scratch test with different abrasive grain wear conditions is conducted to explore the three contact mechanisms, i.e. rubbing, ploughing and cutting, where their nature is not fully understood.