On Characterization and Optimization of Engineering Surfaces
Doctoral thesis, 2023
Swedish manufacturing industry in collaboration with academia is exploring innovative ways to manufacture eco-efficient and resource efficient products. Consequently, improving manufacturing efficiency and quality has become the priority for the manufacturing sector to remain competitive in a sustainable way. To achieve this, control and optimization of manufacturing process and product’s performance are necessary. This has led to increase in demand for functional surfaces, which are engineering surfaces tailored to different applications. With new advancements in manufacturing and surface metrology, investigations are steadily progressing towards re-defining quality and meeting dynamic customer demands. In this thesis, surfaces produced by different manufacturing systems are investigated, and methods are proposed to improve specification and optimization.
The definition and interpretation of surface roughness vary across the manufacturing industry and academia. It is well known that surface characterization helps to understand the manufacturing process and its influence on surface functional properties such as wear, friction, adhesivity, wettability, fluid retention and aesthetic properties such as gloss. Manufactured surfaces consist of features that are relevant and features that are not of interest. To be able to produce the intended function, it is important to identify and quantify the features of relevance. Use of surface texture parameters helps in quantifying these surface features with respect to type, region, spacing and distribution. Currently, surface parameters Ra or Sa that represent average roughness are widely used in the industry, but they may not provide adequate information on the surface. In this thesis, a general methodology, based on the standard surface parameters and statistical approach, is proposed to improve the specification for surface roughness and identify the combination of significant surface texture parameters that best describe the surface and extract valuable surface information.
Surface topography generated by additive, subtractive and formative processes is investigated with the developed research approach. The roughness profile parameters and areal surface parameters defined in ISO, along with power spectral density and scale sensitive fractal analysis, are used for surface characterization and analysis. In this thesis, the application of regression statistics to identify the set of significant surface parameters that improve the specification for surface roughness is shown. These surface parameters are used to discriminate between the surfaces produced by multiple process variables at multiple levels. By analyzing the influence of process variables on the surface topography, the research methodology helps to understand the underlying physical phenomenon and enhance the domain-specific knowledge with respect to surface topography. Subsequently, it helps to interpret processing conditions for process and surface function optimization.
The research methods employed in this study are valid and applicable for different manufacturing processes. This thesis can support the guidelines for manufacturing industry focusing on process and functional optimization through surface analysis. With increase in use of machine learning and artificial intelligence in automation, methodologies such as the one proposed in this thesis are vital in exploring and extracting new possibilities in functional surfaces.
The definition and interpretation of surface roughness vary across the manufacturing industry and academia. It is well known that surface characterization helps to understand the manufacturing process and its influence on surface functional properties such as wear, friction, adhesivity, wettability, fluid retention and aesthetic properties such as gloss. Manufactured surfaces consist of features that are relevant and features that are not of interest. To be able to produce the intended function, it is important to identify and quantify the features of relevance. Use of surface texture parameters helps in quantifying these surface features with respect to type, region, spacing and distribution. Currently, surface parameters Ra or Sa that represent average roughness are widely used in the industry, but they may not provide adequate information on the surface. In this thesis, a general methodology, based on the standard surface parameters and statistical approach, is proposed to improve the specification for surface roughness and identify the combination of significant surface texture parameters that best describe the surface and extract valuable surface information.
Surface topography generated by additive, subtractive and formative processes is investigated with the developed research approach. The roughness profile parameters and areal surface parameters defined in ISO, along with power spectral density and scale sensitive fractal analysis, are used for surface characterization and analysis. In this thesis, the application of regression statistics to identify the set of significant surface parameters that improve the specification for surface roughness is shown. These surface parameters are used to discriminate between the surfaces produced by multiple process variables at multiple levels. By analyzing the influence of process variables on the surface topography, the research methodology helps to understand the underlying physical phenomenon and enhance the domain-specific knowledge with respect to surface topography. Subsequently, it helps to interpret processing conditions for process and surface function optimization.
The research methods employed in this study are valid and applicable for different manufacturing processes. This thesis can support the guidelines for manufacturing industry focusing on process and functional optimization through surface analysis. With increase in use of machine learning and artificial intelligence in automation, methodologies such as the one proposed in this thesis are vital in exploring and extracting new possibilities in functional surfaces.
Stylus Profilometer
Manufacturing
Coherence Scanning Interferometer
Regression
Areal surface parameters
Characterization
Functional surfaces
Surface profile parameters
Virtual Development Laboratory (VDL), Chalmers Tvärgata 4C, Campus Johanneberg, Göteborg
Opponent: Professor Mohamed El Mansori, Ecole Nationale Supérieure d'Arts et Métiers (ENSAM) ParisTech, France
Author
Vijeth Venkataram Reddy
Chalmers, Industrial and Materials Science, Materials and manufacture
Vijeth V Reddy, Amogh Vedantha Krishna, Fredrik Schultheiss and BG Rosén, Surface Topography Characterization of Brass Alloys: Lead Brass (Cuzn39pb3) and Lead-free Brass (Cuzn21si3p), Surf. Topogr.: Metrol. Prop. 5 025001, 2017.
Vijeth V Reddy, Olena Flys, Amogh Vedantha Krishna, B-G Rosen, 2017, Topography characterization of Fused Deposition Modeling surfaces, Special Interest Group Meeting: Additive Manufacturing, Leuven, Belgium.
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.
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, Amogh V Krishna, Anders Sjögren, & B.-G Rosén, (2023). Surface characterization and analysis of textured injection molded PC-ABS automotive interior components. Surface Topography: Metrology and Properties, 11(1), 014003.
Vijeth V Reddy, Amogh Vedantha Krishna, A Sjögren and B-G Rosén, Controlling the surface texture of injection moulded automotive interior components. Poster at Met & Props 2022. Scotland. Submitted to Journal
Surface roughness is an attribute of the surface that acts as the boundary between a part or product and its surroundings. It is present everywhere, and its importance varies depending on the intended purpose. For example, smoother surfaces may be preferable for applications such as skating or sealing, while rougher surfaces are desired for generating friction when walking/running or in brake pads. Therefore, amount of surface roughness required is an open question and depends on the purpose. Surface metrology helps in understanding and predicting the material’s response concerning machining and surface functional behavior such as appearance, adhesion, heat exchange, friction, wetting, fatigue, adsorption, absorption, drying, reflectivity, and scattering.
Engineering surfaces are produced to perform certain function. Controlling the manufacturing process and the resulting surface function is crucial to producing cost-effective products that performs well. This can be achieved by two ways: first, by understanding and controlling the physical mechanisms behind the manufacturing process that generates the surface, and second, by understanding and controlling the relationship between the generated surface and the resulting surface functional behavior. This requires experimentation, characterization, and analysis of the surface topographical features. Surface parameters are used to characterize the various features and its distribution on the surface, providing objective information that can be used to control the surface.
Surface parameters provide quantitative information on the surface topographical features, but the number of surface parameters available in ISO standard that quantify different aspect of the surface features can be overwhelming. This thesis aims to identify significant surface parameters that can be used to improve the specification of surface roughness. Experimental investigations were carried out on surfaces produced using additive manufacturing by fused deposition modeling, subtractive manufacturing by turning operation and formative process by injection molding, and critical process variables are identified. Statistical models were used to understand the variation caused by the manufacturing process and interpret surface functional behavior. This thesis provides a framework and guidance to streamline the specification of surface roughness, which can be used as input to optimize manufacturing process and develop application-specific surfaces.
Engineering surfaces are produced to perform certain function. Controlling the manufacturing process and the resulting surface function is crucial to producing cost-effective products that performs well. This can be achieved by two ways: first, by understanding and controlling the physical mechanisms behind the manufacturing process that generates the surface, and second, by understanding and controlling the relationship between the generated surface and the resulting surface functional behavior. This requires experimentation, characterization, and analysis of the surface topographical features. Surface parameters are used to characterize the various features and its distribution on the surface, providing objective information that can be used to control the surface.
Surface parameters provide quantitative information on the surface topographical features, but the number of surface parameters available in ISO standard that quantify different aspect of the surface features can be overwhelming. This thesis aims to identify significant surface parameters that can be used to improve the specification of surface roughness. Experimental investigations were carried out on surfaces produced using additive manufacturing by fused deposition modeling, subtractive manufacturing by turning operation and formative process by injection molding, and critical process variables are identified. Statistical models were used to understand the variation caused by the manufacturing process and interpret surface functional behavior. This thesis provides a framework and guidance to streamline the specification of surface roughness, which can be used as input to optimize manufacturing process and develop application-specific surfaces.
Driving Forces
Sustainable development
Areas of Advance
Production
Materials Science
Subject Categories
Manufacturing, Surface and Joining Technology
Infrastructure Engineering
ISBN
978-91-7905-858-6
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5324
Publisher
Chalmers
Virtual Development Laboratory (VDL), Chalmers Tvärgata 4C, Campus Johanneberg, Göteborg
Opponent: Professor Mohamed El Mansori, Ecole Nationale Supérieure d'Arts et Métiers (ENSAM) ParisTech, France