3D PRINTING IN LUXURY MASS CUSTOMIZATION: INSIGHTS FROM AREZZO DISTRICT COMPANIES

Additive manufacturing, also known as three-dimensional (3D) printing, enables production of complex customized shapes without requiring specialized tooling and fixture, and mass customization can then be realized with larger adoption. The slicing procedure is one of the fundamental tasks for 3D printing, and the slicing resolution has to be very high for fine fabrication, especially in the recent developed continuous liquid interface production (CLIP) process. The slicing procedure is then becoming the bottleneck in the prefabrication process, which could take hours for one model. This becomes even more significant in mass customization, where hundreds or thousands of models have to be fabricated. We observe that the customized products are generally in a same homogeneous class of shape with small variation. Our study finds that the slicing information of one model can be reused for other models in the same homogeneous group under a properly defined parameterization. Experimental results show that the reuse of slicing information has a maximum of 50 times speedup, and its utilization is dropped from more than 90% to less than 50% in the prefabrication process.

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RAPIDS 2.0

10.47982/bookrxiv.21 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ulrich Knaack ◽

◽

Dennis de Witte ◽

Alamir Mohsen ◽

Marcel Bilow ◽

...

Keyword(s):

Additive Manufacturing ◽

3D Printing ◽

Built Environment ◽

Mass Customization ◽

Book Series ◽

The World

The imagine series, developed at our faculty at TU Delft, is a book series championing ideas, concepts and physically built results. It is for designers and architects: to inspire them and to create a culture of imagination. At the start, the editors needed to promise the publisher a series of ten books and started with imagine 01, “Façades”, in 2008. The series continued with volumes about interesting (“Concretable”, 08), relevant (“Energy”, 05) and unusual aspects of architecture (“Deflateables”, 02, which dealt with vacuum constructions, and “Rapids”, 04, which took a first look into the world of additive manufacturing for buildings, something we now call 3D-printing). Now, with number 10 we have completed the cycle. It is again about the development and the potentials of additive manufacturing for the built environment. This technology is developing very rapidly and promises to be revolutionary for the construction of buildings. It has the potential to truly bring mass-customization on a detail level. And it is interesting to see how imagine 04, “Rapids”, helped to accelerate this development – some of the ideas mentioned in that issue felt really naive and impossible at the time. Today, a few years later, our colleagues at MIT refer to these books and are now printing with glass! This is what the book series was meant to do: to showcase potentials and to imagine possibilities.

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Mass Customization: Reuse of Digital Slicing for Additive Manufacturing

Volume 1A: 36th Computers and Information in Engineering Conference ◽

10.1115/detc2016-60140 ◽

2016 ◽

Cited By ~ 2

Author(s):

Tsz-Ho Kwok ◽

Hang Ye ◽

Yong Chen ◽

Chi Zhou ◽

Wenyao Xu

Keyword(s):

Additive Manufacturing ◽

3D Printing ◽

Mass Customization ◽

Small Variation ◽

Liquid Interface ◽

Fabrication Process ◽

Homogeneous Group ◽

Experimental Results ◽

Continuous Liquid Interface Production ◽

Very High

Additive manufacturing, also known as 3D printing, enables production of complex customized shapes without requiring specialized tooling and fixture, and mass customization can then be realized with larger adoption. The slicing procedure is one of the fundamental tasks for 3D printing, and the slicing resolution has to be very high for fine fabrication, especially in the recent developed Continuous Liquid Interface Production (CLIP) process. The slicing procedure is then becoming the bottleneck in the pre-fabrication process, which could take hours for one model. This becomes even more significant in mass customization, where hundreds or thousands of models have to be fabricated. We observe that the customized products are generally in a same homogeneous class of shape with small variation. Our study finds that the slicing information of one model can be reused for other models in the same homogeneous group under a properly defined parameterization. Experimental results show that the reuse of slicing information have a maximum of 50 times speedup, and its utilization is dropped from more than 90% to less than 50% in the pre-fabrication process.

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Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4046746 ◽

2020 ◽

Vol 142 (6) ◽

Author(s):

Jida Huang ◽

Hongyue Sun ◽

Tsz-Ho Kwok ◽

Chi Zhou ◽

Wenyao Xu

Keyword(s):

Deep Learning ◽

3D Printing ◽

Mass Customization ◽

Shape Matching ◽

Three Dimensional ◽

Solution Space ◽

Optimization Methods ◽

Correspondence Problem ◽

Shape Correspondence ◽

Nonlinear Complexity

Abstract Many industries, such as human-centric product manufacturing, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes flexible design and manufacturing possible. However, the personalized designs bring challenges for the shape matching and analysis, owing to the high complexity and shape variations. Traditional shape matching methods are limited to spatial alignment and finding a transformation matrix for two shapes, which cannot determine a vertex-to-vertex or feature-to-feature correlation between the two shapes. Hence, such a method cannot measure the deformation of the shape and interested features directly. To measure the deformations widely seen in the mass customization paradigm and address the issues of alignment methods in shape matching, we identify the geometry matching of deformed shapes as a correspondence problem. The problem is challenging due to the huge solution space and nonlinear complexity, which is difficult for conventional optimization methods to solve. According to the observation that the well-established massive databases provide the correspondence results of the treated teeth models, a learning-based method is proposed for the shape correspondence problem. Specifically, a state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected deformed shapes. Through learning the deformations of the models, the underlying variations of the shapes are extracted and used for finding the vertex-to-vertex mapping among these shapes. We demonstrate the application of the proposed approach in the orthodontics industry, and the experimental results show that the proposed method can predict correspondence fast and accurate, also robust to extreme cases. Furthermore, the proposed method is favorably suitable for deformed shape analysis in mass customization enabled by 3D printing.

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