Design of database for automatic example-driven design and assembly of man-made objects

Thesis (S.B.)--Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (page 18).In this project, we have built a database of models that have been designed such that they can be directly fabricated by a casual user. Each of the models in this database has design specifications up to the screw level, and each component has a direct reference to a commercial part from online retailers such as McMaster-Carr and Home Depot. This database was built with the purpose of assisting a data-driven approach to customizable fabrication. This system allows a casual user to create a 3D model input with rough specifications and receive a list of parts that, when assembled, will create the model specified. Using this system and database, we were able to successfully design and fabricate three pieces of furniture and therefore proved the data-driven method approach to be valid.by J. Keneth Piñera.S.B

Chopper: Partitioning models into 3D-printable parts

3D printing technology is rapidly maturing and becoming ubiquitous. One of the remaining obstacles to wide-scale adoption is that the object to be printed must fit into the working volume of the 3D printer. We propose a framework, called Chopper, to decompose a large 3D object into smaller parts so that each part fits into the printing volume. These parts can then be assembled to form the original object. We formulate a number of desirable criteria for the partition, including assemblability, having few components, unobtrusiveness of the seams, and structural soundness. Chopper optimizes these criteria and generates a partition either automatically or with user guidance. Our prototype outputs the final decomposed parts with customized connectors on the interfaces. We demonstrate the effectiveness of Chopper on a variety of non-trivial real-world objects.National Science Foundation (U.S.) (Grant CCF-1012147)National Science Foundation (U.S.) (Grant IIS-1116296)Intel Corporation (Science and Technology Center for Visual Computing

Learning a human-perceived softness measure of virtual 3D objects

We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. We collect crowdsourced data where humans rank their perception of the softness of vertex pairs on virtual 3D models. We then compute shape descriptors and use a learning to-rank approach to learn a softness measure mapping any vertex to a softness value. Finally, we demonstrate our framework with a variety of 3D shapes

PENELITIAN DESAIN MEBEL BERBASIS PANGKALAN DATA DENGAN METODE DIVERGEN KONVERGEN ITERATIF SEBAGAI STRATEGI R&D & MANUFAKTUR PERUSAHAAN (STUDI KASUS: CORPORATE SPECIALISTS, MALAYSIA & HOMELEGANCE, USA)

Furniture design for mass production & export orientation is complex innature, tend to have a lot of revisions, thus require a lot investment in fund,time, and effort. There are 2 research issue: 1) Mistakes in design proposalsoften occurred (size, construction, style, finishing, price), can not match withfactory production capacity, or buyer’s market target. In this case, factoriesare suppliers of research partner 1 (CS), and buyer is research partner 2(Homelegance). Second issue: 2) Designer’s idea often surrounds in designaesthetic alone, disregard the A-Z aspect in supply & demand chain of amass-produced furniture (production, marketing, packing, shipping, etc).This research uses qualitative research model with many case studies, andapplies 3 methods: 1) divergent convergent iterative design method; 2) 2D &3D database from CS; 3) considering US market response fromHomelegance. This research aims to: 1) providing ways and recommendationfor stakeholder to reduce revisions (time & cost saving), 2) producingdesigns with export oriented quality, 3) providing design insights foracademics about furniture design from early to final phase. Research output(furniture samples) are stored in multiple supplier’s warehouse

Dynamic Furniture Modeling Through Assembly Instructions

We present a technique for parsing widely used furniture assembly instructions, and reconstructing the 3D models of furniture components and their dynamic assembly process. Our technique takes as input a multi-step assembly instruction in a vector graphic format and starts to group the vector graphic primitives into semantic elements representing individual furniture parts, mechanical connectors (e.g., screws, bolts and hinges), arrows, visual highlights, and numbers. To reconstruct the dynamic assembly process depicted over multiple steps, our system identifies previously built 3D furniture components when parsing a new step, and uses them to address the challenge of occlusions while generating new 3D components incrementally. With a wide range of examples covering a variety of furniture types, we demonstrate the use of our system to animate the 3D furniture assembly process and, beyond that, the semantic-aware furniture editing as well as the fabrication of personalized furnitures

Boxelization: folding 3D objects into boxes

We present a method for transforming a 3D object into a cube or a box using a continuous folding sequence. Our method produces a single, connected object that can be physically fabricated and folded from one shape to the other. We segment the object into voxels and search for a voxel-tree that can fold from the input shape to the target shape. This involves three major steps: finding a good voxelization, finding the tree structure that can form the input and target shapes' configurations, and finding a non-intersecting folding sequence. We demonstrate our results on several input 3D objects and also physically fabricate some using a 3D printer

Tactile mesh saliency

While the concept of visual saliency has been previously explored in the areas of mesh and image processing, saliency detection also applies to other sensory stimuli. In this paper, we explore the problem of tactile mesh saliency, where we define salient points on a virtual mesh as those that a human is more likely to grasp, press, or touch if the mesh were a real-world object. We solve the problem of taking as input a 3D mesh and computing the relative tactile saliency of every mesh vertex. Since it is difficult to manually define a tactile saliency measure, we introduce a crowdsourcing and learning framework. It is typically easy for humans to provide relative rankings of saliency between vertices rather than absolute values. We thereby collect crowdsourced data of such relative rankings and take a learning-to-rank approach. We develop a new formulation to combine deep learning and learning-to-rank methods to compute a tactile saliency measure. We demonstrate our framework with a variety of 3D meshes and various applications including material suggestion for rendering and fabricatio