Towards Topography Characterization of Additive Manufacturing Surfaces
The challenge with AM is that it generates surfaces that are different compared to conventional manufacturing techniques and varies with respect to different materials, geometries and process parameters. Therefore, AM surfaces often require novel characterization approaches to fully explain the manufacturing process. Most of the previously published work has been broadly based on two-dimensional parametric measurements. Some researchers have already addressed the AM surfaces with areal surface texture parameters but mostly used average parameters for characterization which is still distant from a full surface and functional interpretation. There has been a continual effort in improving the characterization of AM surfaces using different methods and one such effort is presented in this thesis.
The primary focus of this thesis is to get a better understanding of AM surfaces to facilitate process control and optimization. For this purpose, the surface texture of Fused Deposition Modeling (FDM) and Laser-based Powder Bed Fusion of Metals (PBF-LB/M) have been characterized using various tools such as Power Spectral Density (PSD), Scale-sensitive fractal analysis based on area-scale relations, feature-based characterization and quantitative characterization by both profile and areal surface texture parameters. A methodology was developed using a Linear multiple regression and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces and also to understand the manufacturing process. The results suggest that the developed approaches can be used as a guideline for AM users who are looking to optimize the process for gaining better surface quality and component functionality, as it works effectively in finding the significant parameters representing the unique signatures of the manufacturing process. Future work involves improving the accuracy of the results by implementing improved statistical models and testing other characterization methods to enhance the quality and function of the parts produced by the AM process.
Laser-based Powder bed fusion
Fused deposition modeling
Structured light projection
Areal surface texture parameters
Power spectral density
Scale-sensitive fractal analysis
AMOGH VEDANTHA KRISHNA
Chalmers, Industri- och materialvetenskap, Material och tillverkning
Amogh V. Krishna, O. Flys, Vijeth V. Reddy, A. Leicht, L. Hammar and B.-G. Rosén. (2018) Potential approach towards effective topography characterization of 316L stainless steel components produced by selective laser melting process. In: European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 18th International Conference and Exhibition, EUSPEN (pp. 259-260).
Amogh V. Krishna, O. Flys, Vijeth V. Reddy, J. Berglund and B.-G. Rosén. (2020) Areal surface topography representation of as-built and post-processed samples produced by powder bed fusion using laser beam melting. Journal of Surface Topography: Metrology and Properties, Volume 8, 024012.
Amogh V. Krishna, M. Faulcon, B. Timmers, Vijeth V. Reddy, H. Barth, G. Nilsson and B.-G. Rosén. (2020) Influence of different post-processing methods on surface topography of fused deposition modelling samples. Journal of Surface Topography: Metrology and Properties, Volume 8, 014001.
Vijeth V. Reddy, O. Flys, A. Chaparala, C. E. Berrimi, Amogh V. Krishna and B.-G. Rosén. (2018) Study on surface texture of Fused Deposition Modeling. Procedia Manufacturing, Volume 25, Pages 389-396, ISSN 2351-9789, 2018.
Produktionsteknik, arbetsvetenskap och ergonomi
Bearbetnings-, yt- och fogningsteknik
Övrig annan teknik
Chalmers tekniska högskola
Chalmers or Digital via ZOOM
Opponent: Dr. Frédéric Cabanettes, Laboratory of Tribology and Dyanmics of Systems (LTDS) – ENISE, France