An Interactive Product Customization Framework for Freeform Shapes

Additive Manufacturing (AM) enables the fabrication of three-dimensional (3D) objects with complex shapes without additional tools and refixturing. However, it is difficult for user to use traditional computer-aided design tools to design custom products. In this paper, we presented a design system to help user design custom 3D printable products on top of some freeform shapes. Users can define and edit styling curves on the reference model using our interactive geometric operations for styling curves. Incorporating with the reference models, these curves can be converted into 3D printable models through our fabrication interface. We tested our system with four design applications including a hollow-patterned bicycle helmet, a T-rex with skin frame structures, a face mask with Voronoi patterns, and an AM-specific night dress with hollow patterns. The executable prototype of the presented design framework used in the customization process is publicly available

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing

Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies

DESIGNING END USE COMPONENTS FOR ADDITIVE MANUFACTURING Exploring experiences, knowledge and information requirements

The primary aim of this study was to develop an understanding of the experience, the current knowledge regarding the design of end-user components for series production in additive manufacturing (AM) and the information needs of designers involved in designing end-user components for AM. To achieve this, the survey intended to uncover the following aspects of the topic: 1. If and how frequently AM technologies are used by designers for series production of end-user components 2. To what extend designers have had experience of designing end-user components for AM 3. Which aspects are important when designing end-user components for additive 4. Why designers have or not have designed end-user components for series production in AM 5. How designers have learned how to design for AM and which resources they have used 6. What designers would like to know about Design for AM 7. How they prefer this information to be conveye

Tools and Methods to Analyze Multimodal Data in Collaborative Design Ideation

Collaborative design ideation is typically characterized by informal acts of sketching, annotation, and discussion. Designers have always used the pencil-and-paper medium for this activity, partly because of the flexibility of the medium, and partly because the ambiguous and ill-defined nature of conceptual design cannot easily be supported by computers. However, recent computational tools for conceptual design have leveraged the availability of hand-held computing devices for creating and sharing ideas. In order to provide computer support for collaborative ideation in a way that augments traditional media rather than imitates it, it is necessary to study the affordances made available by digital media for this process, and to study designers\u27 cognitive and collaborative processes when using such media. In this thesis, we present tools and methods to help make sense of unstructured verbal and sketch data generated during collaborative design, with a view to better understand these collaborative and cognitive processes. This thesis has three main contributions

Development Of Generative Computer-Aided Process Planning System For Lathe Machining

Computer Aided Process Planning (CAPP) is the bridge between computer-aided design (CAD) and computer-aided manufacturing (CAM). CAPP functions as the recognizer of the geometric input from CAD and analyse it into specific function for manufacturing purpose in CAM. These functions always create irregular data descriptions in current CAD and CAM system supply and demand. This study attempts to solve this problem by recognizing the part model’s features via its geometrical based and produce sub-delta volumes that can later be used to generate manufacturing feature-based data for CAM in a single system via generations of algorithm through open source 3D CAD modeller. To map the generated sub-delta volume and respective machining process, part model complexity (PMC) is introduced. Errors of the overall delta volume (ΔODV) were calculated and verification of the proposed PMC is done. Furthermore, to minimize unit production cost, machining parameters including cutting speed (CS), feed rate (f) and depth of cut (d) were optimized for regular form surfaces by using firefly algorithm (FA). These parameters were then useful for tooling selections and tool-path planning. The results from the automatic feature recognitions show less than 0.02% of error in comparison of algorithm overall delta volume, (ODValg) and the manual calculation ODV, (ODVmanual). To validate the generated tool-path, G-codes generated in media package file (MPF) file format and verified through CNC lathe machine. Indeed, the developed algorithm was able to determine the minimum unit production cost of lathe machining part model. Therefore, a single automatic system that able to transfer CAD data into machining readable data through CAM data had been developed