Inducing a Realistic Surface Roughness onto 3D Mesh Data Using Conditional Generative Adversarial Network (cGAN)

Bisma Mutiargo, Shan Lou, Zheng Zheng Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In the age of machine learning, data-driven approaches with hybrid data (a mixture of real images and simulation images) are getting increasingly popular. One major issue with creating a realistic simulation for surface engineering is that the surface of the mesh model used in the simulation is smooth. Often, this mesh does not contain information on surface texture; thus, simulating an object based on these meshes may not represent an actual surface texture of a real component. This article presents a novel technique for introducing surface roughness onto a smooth mesh object to facilitate engineering simulation by using a conditional Generative-Adversarial Network (cGAN) that is trained on real height maps to generate random 2D height maps that represents a realistic texture of a typical upskin and downskin surface of an additively manufactured (AM) part. This approach extracted the past scans of AM components from the Focus Variation microscopy. The 3D surface deviation is extracted as height maps and used as the training data for the generative network. This paper will also discuss the structural similarities between the synthetic and real data using standard descriptors for surface texture characterisation, such as Sa, Sq and Sdq.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Advanced Surface Enhancement (INCASE) 2023 - Surface Engineering for Sustainability
EditorsNiroj Maharjan, Wei He
PublisherSpringer Singapore
Number of pages12
ISBN (Electronic)9789819986439
ISBN (Print)9789819986422
Publication statusPublished - 7 May 2024
Event3rd International Conference on Advanced Surface Enhancement - , Singapore
Duration: 26 Sep 202328 Sep 2023
Conference number: 3

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364


Conference3rd International Conference on Advanced Surface Enhancement
Abbreviated titleINCASE2023
Internet address

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