On Deterministic feature-based Surface Analysis
Licentiate thesis, 2020
Every manufacturing process generate surfaces which beholds certain signature features. Engineered surfaces consist of both, features that are of interest and features that are irrelevant. These features imparted on the manufactured part vary depending on the process, materials, tooling and manufacturing process variables. Characterization and analysis of deterministic features represented by significant surface parameters helps the understanding of the process and its influence on surface functional properties such as wettability, fluid retention, friction, wear and aesthetic properties such as gloss, matte. In this thesis, a general methodology with a statistical approach is proposed to extract the robust surface parameters that provides deterministic and valuable information on manufactured surfaces.
Surface features produced by turning, injection molding and Fused Deposition Modeling (FDM) are characterized by roughness profile parameters and areal surface parameters defined by ISO standards. Multiple regression statistics is used to resolve surfaces produced with multiple process variables and multiple levels. In addition, other statistical methods used to capture the relevant surface parameters for analysis are also discussed in this thesis. The selected significant parameters discriminate between the samples produced by different process variables and helps to identify the influence of each process variable. The discussed statistical approach provides valuable information on the surface function and further helps to interpret the surfaces for process optimization.
The research methods used in this study are found to be valid and applicable for different manufacturing processes and can be used to support guidelines for the manufacturing industry focusing on process optimization through surface analysis. With recent advancement in manufacturing technologies such as additive manufacturing, new methodologies like the statistical one used in this thesis is essential to explore new and future possibilities related to surface engineering.
Coherence Scanning Interferometer
Surface profile parameters.
Areal surface parameters
Vijeth Venkataram Reddy
Chalmers, Industrial and Materials Science, Materials and manufacture
Vijeth V Reddy, Amogh Vedantha Krishna, Fredrik Schultheiss and B-G Rosén, Surface Topography Characterization of Brass Alloys: Lead Brass (Cuzn39pb3) and Lead-free Brass (Cuzn21si3p), Surf. Topogr.: Metrol. Prop. 5 025001, 2017.
Vijeth Reddy, Olena Flys, Anish Chaparala, Chihab E Berrimi, Amogh V, BG Rosen, Study on surface texture of Fused Deposition Modeling, Procedia Manufacturing, Volume 25, Pages 389-396, ISSN 2351-9789, 2018.
Vijeth V Reddy, Amogh Vedantha Krishna, A Sjögren and B-G Rosén, Controlling the visual appearance and texture of injection molded automotive components.
Production Engineering, Human Work Science and Ergonomics
Manufacturing, Surface and Joining Technology
Other Engineering and Technologies not elsewhere specified
Areas of Advance
Chalmers University of Technology
Digital via Zoom
Opponent: Dr.Cecilia Anderberg, Molnlycke Health Care