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The Selective Laser Sintering process is a widely established Rapid Manufacturing technology that offers various possibilities not available from other production techniques. The application of Selective Laser Sintering in the creation of products such as shoe outsoles has led to a requirement for compression property data and an understanding of the factors that influence it. This research investigated the effect of sintering temperature on compression modulus and strength of Selective Laser Sintered nylon-12 parts. Three parameters were used to vary temperature. These were laser power, part heater set point and powder bed location. The modulus and strength increased as temperature increased. A statistical analysis showed that laser power and part heater set point had a statistically significant effect on compression properties. They had over a 95% probability of influencing compression properties. With less than a 95% chance, the effect of bed location on compression properties was considered statistically insignificant. k The variations in temperature also led to changes in crystallinity and density. An increase in temperature caused a decrease in crystallinity. In contrast density was increased as temperature increased. Except for parts built at various bed locations, there was over a 95% certainty that temperature caused differences in crystallinity and density. However, there was a less than 95% probability that crystallinity and density influenced compression properties. Hence, they had no statistically significant effect on compression modulus and strength. This work has added to the knowledge base of Selective Laser Sintered nylon-12 within the specified boundaries of this research. Users of this technology who consider compression modulus and strength to be of importance can focus their efforts on optimising temperature. This has been shown to have a greater statistical impact on compression properties compared to crystallinity and density.

Additive manufacturing (AM) technology describes a set of processes capable of producing 3D physical products from CAD data directly. The rapid development of AM technologies and their wide applications makes the selection of the suitable process chains and materials a difficult task. Some researchers have tackled this problem by developing selectors that should assist users in their selections. The existing selector systems have some drawbacks: (і) often being outdated even before they were completely developed because new processes and materials are evolving continuously, (іі) representing only the point of view of their developers because users were not involved in the development process and (iii) not being holistic and able to help in all AM aspects for example process chains, materials, finishing methods and machines. This work has developed an updatable decision support system that assists users in their selections regarding AM process chains, materials, finishing methods, and machines. First, the study started by analyzing the available additive manufacturing selector systems and identifying their shortcomings. Secondly, the researcher identified target specifications for the new system, investigated different possible architectures for the system, selected knowledge based system (KBS) and database (DB) architecture to work together as a versatile tool that achieves the required target specifications. Next, the first version of the system was developed. Furthermore, verification and validation processes were made to test the developed system. Three case studies were used for the validation purpose: a typical consumer razor blade and two automotive components. These case studies were manufactured using AM technologies and then a comparison between real life decisions and the developed decision support system decisions were made. In addition, a number of interviews were performed in order to obtain users’ feedback about the first developed version. As a result of the feedback and evaluation a second version of the system was developed and evaluated. The results obtained from the second evaluation suggest that the second version is more effective than the first version during the selection process. To conclude, this study has shown that using KBS and DB together is effective to develop an updatable additive manufacturing decision support system. In addition, the user involvement in the development stage of the system enhances the system performance.

Reverse engineering requires the acquisition of large amounts of data describing the surface of an object, sufficient to replicate that object accurately using appropriate fabrication techniques. This is important within a wide range of commercial and scientific fields where CAD models may be unavailable for parts that must be duplicated or modified, or where a physical model is used as a prototype. The three-dimensional digitisation of objects is an essential first step in reverse engineering. Optical triangulation laser sensors are one of the most popular and common non-contact methods used in the data acquisition process today. They provide the means for high resolution scanning of complex objects. Multiple scans of the object are usually required to capture the full 3D profile of the object. A number of factors, including scan resolution, system optics and the precision of the mechanical parts comprising the system may affect the accuracy of the process. A single perspective optical triangulation sensor provides an inexpensive method for the acquisition of 3D range image data. However, there are often locations within each scan where data is seriously flawed because the data acquisition process is subject to distortions. Such distortions are often associated with edges in the object, where regions of high curvature (relative to the incident angle of the sensor) cause occlusions and secondary reflections of the laser beam, resulting in false height readings. Abrupt changes in surface reflectance or texture can also have similar effects. Previous work has determined that the orientation of the scan head with respect to the edges of the object is a major factor in the degree of such distortions. Combining multiple range images using compensation algorithms has reduced the level of distortion in the integrated data set; however capturing the number of necessary repetitions of the entire scan is very time-consuming. A development platform has been established to investigate how data distortions may be reduced by the application of image analysis techniques in planning the scan process. By using information on edge location and orientation recovered from a digital camera image, partial scans may then be performed for each determined orientation of the scanner, thereby avoiding much redundant coverage of the entire scan area. Vectorisation algorithms, based on known edge detection techniques, have been developed to determine the position of vectors corresponding to the discovered edges. Further algorithms have been developed to process these vectors into 'scan regions' corresponding to each particular scanner orientation. When the object is scanned at the orientation corresponding to the scan region the distortions are likely to be much reduced. Some features of the object geometry, such as small holes or internal corners present a particular problem where a number of scan regions representing different scan orientations overlap. Because of the nature of the scanner such regions are liable to show some level of distortion for all laser orientations. However, these locations can be identified from the camera image and the user alerted to the presence of unreliable data. Calibration methods relating the image and scan space have been shown to be susceptible to errors caused by optical effects from the camera, such as lens barrel distortion and errors due to parallax. Algorithms have been developed to compensate for these effects and combine the data from a number of partial scans in order to provide a single integrated point cloud.