I Dewa Made Oka Dharmawan

About

I Dewa Made Oka Dharmawan is a researcher advancing the field of nondestructive evaluation (NDE) through deep learning and magneto-optic imaging. His primary research areas include automated defect detection, transfer learning, and intelligent inspection systems for industrial robotics. Dharmawan’s most cited work, “Defect Shape Classification Using Transfer Learning in Deep Convolutional Neural Network on Magneto-Optical Nondestructive Inspection” (2022, 13 citations), introduces a novel approach to classify defect shapes in magneto-optic nondestructive inspection (MONDI) images. By leveraging transfer learning with deep convolutional neural networks, his method enables quantitative assessments of defect presence, location, shape, and size—critical capabilities for training autonomous NDT robots. This contribution directly addresses the challenge of real-time, robot-based inspection in manufacturing and infrastructure maintenance. Dharmawan’s work bridges computer vision and nondestructive testing, offering a scalable solution for automated quality control. His research holds promise for reducing human error and inspection time in industrial settings, positioning him as a key contributor to the next generation of intelligent NDE systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Defect Shape Classification Using Transfer Learning in Deep Convolutional Neural Network on Magneto-Optical Nondestructive Inspection

   13 citations · 2022

Key Collaborators

Contact & Links