On Characterization and Optimization of Surface Topography in Additive Manufacturing Processes
Doctoral thesis, 2022
With its ability to construct components through the layer-by-layer deposition of material, Additive Manufacturing (AM), more commonly known as "3D printing", has revolutionized the manufacturing industries. Not only can AM produce complex lightweight designs, but it can also streamline the supply chain, allowing businesses to more quickly and easily meet customer demand. Additionally, with the rising demand for low-volume customized products, and sustainable production, manufacturers are increasingly compelled to adopt AM to remain competitive in the global economy. Despite its popularity, AM has several significant drawbacks, one of the most notable being its poor surface topography quality. Most product failures can be traced back to the initial surface conditions, making the surface texture a crucial factor in determining how well a product will perform. Hence, this thesis presents a study on the surface topography of various AM processes mainly to understand the surface behavior in relation to the factors affecting it.
Every manufacturing process including AM generates distinct surface features referred to as “footprints” or process signatures which substantially affect the surface quality and function. These process signatures vary based on changes in AM processes and their process settings, materials, and geometrical design. The accuracy of identifying and analyzing these features becomes crucial in defining their relationship with manufacturing process variables. Usually, the best practice for defining surface quality is through parametric characterization which provides a quantitative description of either the stochastic or deterministic nature of manufactured surfaces. However, the challenge with AM is that it generates surfaces that often contain both the aforementioned surface features which make it particularly difficult to identify the manufacturing “footprints” through the parametric description. Therefore, the surface topography of AM may often require novel characterization methods to fully interpret the manufacturing process and thereby predict and optimize its product performance.
The overall goal of this thesis is to provide an optimal approach toward the characterization of AM surfaces so that it gives a better understanding of the manufacturing process and also assists in process optimization to control the surface quality of the printed products. To realize this goal, the surface texture of AM processes was studied particularly Material Extrusion (MEX), Vat photopolymerization (VPP), and Powder Bed Fusion (PBF). These processes present topographical features that cover most of the surface scenarios in AM. Hence to explain these varied surface features, a diverse range of surface characterization tools such as Power Spectral Density (PSD), Scale-sensitive fractal analysis, feature-based characterization, and quantitative characterization by both profile and areal surface texture parameters were included in the analysis. Additionally, a methodology was developed using a statistical approach (Linear multiple regression) and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces. Finally, the knowledge gained through the above-mentioned measurements and analysis is put to use to optimize the AM process to achieve enhanced surface quality. 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.
Every manufacturing process including AM generates distinct surface features referred to as “footprints” or process signatures which substantially affect the surface quality and function. These process signatures vary based on changes in AM processes and their process settings, materials, and geometrical design. The accuracy of identifying and analyzing these features becomes crucial in defining their relationship with manufacturing process variables. Usually, the best practice for defining surface quality is through parametric characterization which provides a quantitative description of either the stochastic or deterministic nature of manufactured surfaces. However, the challenge with AM is that it generates surfaces that often contain both the aforementioned surface features which make it particularly difficult to identify the manufacturing “footprints” through the parametric description. Therefore, the surface topography of AM may often require novel characterization methods to fully interpret the manufacturing process and thereby predict and optimize its product performance.
The overall goal of this thesis is to provide an optimal approach toward the characterization of AM surfaces so that it gives a better understanding of the manufacturing process and also assists in process optimization to control the surface quality of the printed products. To realize this goal, the surface texture of AM processes was studied particularly Material Extrusion (MEX), Vat photopolymerization (VPP), and Powder Bed Fusion (PBF). These processes present topographical features that cover most of the surface scenarios in AM. Hence to explain these varied surface features, a diverse range of surface characterization tools such as Power Spectral Density (PSD), Scale-sensitive fractal analysis, feature-based characterization, and quantitative characterization by both profile and areal surface texture parameters were included in the analysis. Additionally, a methodology was developed using a statistical approach (Linear multiple regression) and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces. Finally, the knowledge gained through the above-mentioned measurements and analysis is put to use to optimize the AM process to achieve enhanced surface quality. 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.
areal surface texture parameters
additive manufacturing
power spectral density
vat-photopolymerization
laser-based Powder bed fusion
fused deposition modeling
profile parameters
feature-based characterization
and multiple regression.
scale-sensitive fractal analysis
surface metrology
Virtual Development Lab (VDL) and Online on Zoom
Opponent: Professor Mohamed El Mansori, ENSAM Paris Tech, France
Author
AMOGH VEDANTHA KRISHNA
Chalmers, Industrial and Materials Science, Materials and manufacture
Amogh V. Krishna, O. Flys, V. V. Reddy, and B. G. Rosén, “Surface topography characterization using 3D stereoscopic reconstruction of SEM images,” Surf. Topogr. Metrol. Prop., vol. 6, no. 2, 2018, doi: 10.1088/2051-672X/aabde1.
Potential approach towards effective topography characterization of 316L stainless steel components produced by selective laser melting process
European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 18th International Conference and Exhibition, EUSPEN 2018,;(2018)p. 259-260
Paper in proceeding
Amogh V. Krishna, M. Faulcon, B. Timmers, Vijeth V. Reddy, H. Barth, G. Nilsson and B.-G. Rosén, “Influence of different post-processing methods on surface topography of fused deposition modelling samples,” Surf. Topogr. Metrol. Prop., vol. 8, no. 1, p. 014001, Feb. 2020, doi: 10.1088/2051-672X/AB77D7.
Amogh V. Krishna, O. Flys, V. V. Reddy, J. Berglund, and B. G. Rosen, “Areal surface topography representation of as-built and post-processed samples produced by powder bed fusion using laser beam melting,” Surf. Topogr. Metrol. Prop., vol. 8, no. 2, p. 024012, Jun. 2020, doi: 10.1088/2051-672X/ab9b73.
V. Reddy, O. Flys, A. Chaparala, C. E. Berrimi, Amogh V. Krishna, and B. G. Rosen, “Study on surface texture of Fused Deposition Modeling,” Procedia Manuf., vol. 25, pp. 389–396, 2018, doi: 10.1016/j.promfg.2018.06.108.
S. Dizdar and Amogh V. Krishna, “Microstructural and Mechanical Properties of Polylactic Acid/Tin Bronze Tensile Strength Bars Additive Manufactured by Fused Deposition Modelling,” pp. 566–579, 2022, doi: 10.3233/atde220175.
Amogh V. Krishna, Vijeth V. Reddy, Dyall W Dexter, Dan-Åke Wälivaara, Peter Abrahamsson, B-G Rosen, and Jonas Anderud, “Quality assurance of Stereolithography based biocompatible materials for dental applications”. Submitted to the Journal.
Additive manufacturing (AM), often known as "3D printing", has come a long way in recent years and is now being utilized as an alternative to traditional methods of making plastic and metal components for a wide variety of applications. A simple way of explaining this process is that the material, such as plastic, is passed through a heated nozzle, where the plastic melts and is deposited one layer upon another to build a product. Among the many sustainability benefits of AM are its capacity to produce parts on demand, reduce wastage during manufacturing, manufacture complex and lightweight designs, and minimize energy consumption during manufacturing. Despite its popularity, AM is still not being used as an industrial standard for manufacturing due to certain downsides of this technology. One of its most notable shortcomings is its poor surface finish. Most product failures can be traced back to the initial surface conditions, making the surface texture a crucial factor in determining how well a product will perform. Hence, it is crucial to carefully measure and analyze the AM surfaces to understand their behavior in relation to the factors affecting them.
Before proceeding to discuss the measures taken for improving the surface quality of AM, it is important to understand why the surface quality is bad in the first place. In AM, the stacking of layers of material to build an object causes the formation of "stair-step" like features on the surfaces, leading to an increase in surface roughness or texture. Then there are other contributing factors due to variations in the AM process settings. For example, if the printer nozzle is not heated to the right temperature, the material will have inconsistent flow, leading to irregularities on surfaces. Also, there are external factors, such as vibrations or environmental temperature fluctuations, which can contribute to poor surface quality. Today, there are several different types of AM processes, each producing surfaces differently, and this mandates the need for surface quality control.
There are two ways to tackle this problem. The first is to study the surfaces produced by varying the AM process settings in a controlled environment to understand the relationship between them, thereby allowing process optimization to get a product with the best surface quality. The second is to use surface finishing methods to improve surface quality. For instance, shot blasting uses jets of abrasive particles that hit the sample surface at great velocity to remove "stair steps" and other irregularities from the surface. With this said, this thesis presents diverse research involving several AM technologies, surface finishing methods, surface measuring instruments, and characterization/analysis techniques to provide ways for improving the surface quality of AM products. The findings from this research can be utilized as a guideline for AM users who are looking to optimize the process through surface analysis.
Before proceeding to discuss the measures taken for improving the surface quality of AM, it is important to understand why the surface quality is bad in the first place. In AM, the stacking of layers of material to build an object causes the formation of "stair-step" like features on the surfaces, leading to an increase in surface roughness or texture. Then there are other contributing factors due to variations in the AM process settings. For example, if the printer nozzle is not heated to the right temperature, the material will have inconsistent flow, leading to irregularities on surfaces. Also, there are external factors, such as vibrations or environmental temperature fluctuations, which can contribute to poor surface quality. Today, there are several different types of AM processes, each producing surfaces differently, and this mandates the need for surface quality control.
There are two ways to tackle this problem. The first is to study the surfaces produced by varying the AM process settings in a controlled environment to understand the relationship between them, thereby allowing process optimization to get a product with the best surface quality. The second is to use surface finishing methods to improve surface quality. For instance, shot blasting uses jets of abrasive particles that hit the sample surface at great velocity to remove "stair steps" and other irregularities from the surface. With this said, this thesis presents diverse research involving several AM technologies, surface finishing methods, surface measuring instruments, and characterization/analysis techniques to provide ways for improving the surface quality of AM products. The findings from this research can be utilized as a guideline for AM users who are looking to optimize the process through surface analysis.
Subject Categories
Manufacturing, Surface and Joining Technology
Areas of Advance
Materials Science
ISBN
978-91-7905-759-6
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5225
Publisher
Chalmers
Virtual Development Lab (VDL) and Online on Zoom
Opponent: Professor Mohamed El Mansori, ENSAM Paris Tech, France