On Characterization and Optimization of Engineering Surfaces

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

A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed

Reducing risk in pre-production investigations through undergraduate engineering projects.

This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level. The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor. The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits. The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process

A new frequency distribution architecture for wavelength division systems

Includes bibliographical references (p. 10-16)."Presented at Octima '91, Rome, Italy, January 1991."--Cover. Cover title.Research supported by DARPA. F19628-90-C-0002 Research supported by Bellcore, Nynex and NEC.Pierre A. Humblet, Peter C. Li

On Characterization and Optimization of Surface Topography in Additive Manufacturing Processes

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.\ua0\ua0\ua0 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

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered

Selective laser melting of silica glass powders

Additive manufacturing (AM) has recently amassed great media coverage thanks to its unique capabilities and its appeal towards practicality; contrary to popular belief, these technologies have been used or developed for decades and are continuously improving. Currently, they have managed to allow a new method for fast prototyping, and new practices for producing complex structures with relative ease. The variety of AM technologies currently available is large and employs different approaches to the process. Similarly, a multitude of materials are available for production which further broadens the reach of AM products. There is however, a lack of success in the field of glass AM as there have not been many significant developments in the field which would make this process as versatile as with other materials. Furthermore, the produced components with current glass AM methods do not fulfill the requirements that traditionally-manufactured glass components require, thus preventing these developments from getting past the experimental phase. This study attempts to give a better understanding of the causes for these limitations, as well as provide a solution to these recurring issues by testing theories which presumably could solve them. This process was systematically and qualitatively documented and a conclusion, as well as an own attempt to solve recurring problems, is detailed

Master of Science

thesisAdvances in silicon photonics are enabling hybrid integration of optoelectronic circuits alongside current complementary metal-oxide-semiconductor (CMOS) technologies. To fully exploit the capability of this integration, it is important to explore the effects of thermal gradients on optoelectronic devices. The sensitivity of optical components to temperature variation gives rise to design issues in silicon on insulator (SOI) optoelectronic technology. The thermo-electric effect becomes problematic with the integration of hybrid optoelectronic systems, where heat is generated from electrical components. Through the thermo-optic effect, the optical signals are in turn affected and compensation is necessary. To improve the capability of optical SOI designs, optical-wave-simulation models and the characteristic thermal operating environment need to be integrated to ensure proper operation. In order to exploit the potential for compensation by virtue of resynthesis, temperature characterization on a system level is required. Thermal characterization within the flow of physical design automation tools for hybrid optoelectronic technology enables device resynthesis and validation at a system level. Additionally, thermally-aware routing and placement would be possible. A simplified abstraction will help in the active design process, within the contemporary computer-aided design (CAD) flow when designing optoelectronic features. This thesis investigates an abstraction model to characterize the effect of a temperature gradient on optoelectronic circuit operation. To make the approach scalable, reduced order computations are desired that effectively model the effect of temperature on an optoelectronic layout; this is achieved using an electrical analogy to heat flow. Given an optoelectronic circuit, using a thermal resistance network to abstract thermal flow, we compute the temperature distribution throughout the layout. Subsequently, we show how this thermal distribution across the optoelectronic system layout can be integrated within optoelectronic device- and system-level analysis tools

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio