Customization has traditionally been seen as a human driven process and personalization a largely algorithmic one, they look set to collide in the notion of data driven manufacturing that is core to the vision of Industry 4.0. I explore the idea in which entire ecologies of products to better meet an individual’s needs would be customized by the combination of digital services that profile consumers’ interests, tastes, abilities and contexts and AMT. Specifically, a combination of humans and computer vision systems capture data about dancers so as to drive AMT, specifically additive manufacturing, to customize lower artificial limbs for dancers.

There are some basic requirement for prosthesis: First of all, customization by personal identity is basic requirement for prosthesis. Secondly, it is agreed by most prosthetists that weight of prosthetic devices should be as light as possible, once the requirements of the safest, efficient and most functional componentry possible are matched.[1] Thirdly, normal prosthetic devices bring back amputee part of functionality has been lost. However, most conventional designs of prosthesis come from functional consideration. So a very mechanical and robotic look is quite normal for them. But this phenomenon exacerbates some amputees’ sense of lost. At last, improvement of daily activities, such as ascending and descending stairs and inclines was the main field of prosthetic design and controlling system research, for decades. [2,3].

FDM by plastic is potential solution for those requirements, especially for lightweight structure. However, most of conventional 3D printers today can be described as three-axis machines that print parts one horizontal layer at a time. The notorious problem with this approach is the resulting weaknesses that occur at the lines of adhesion between layers. To solve this problem, I am planning to explore six-axis 3D printing technology which does require knowing how the part will be used. In other words, printing logic is reorganized by loading features such as loading force strength, direction, frequency and so on. The printed part contains parts constructed by horizontal layers and pats printed following curved surface which crossing vertical direction. So even the target is to get totally same appearance and inner fills, printing logic could be generated by the specific utilization. That accomplishes deeper customization – printing logic bespoke by specific utilization.

Data will be collected from non-amputated dancer during various species of dance. Then the collected data will be analysed to get loading features e.g. strength, direction, frequency, on target limb. In addition, how to achieve interaction between muscle movement mapping and surroundings, with the assist of computer vision technology will be explored. At last, profile dancers’ personal information through interview e.g. taste of design, personal physical data, species of dance, interaction idea with surroundings.

Contribution on academic and real world: 1, Achieve deeper customization on additive manufacturing by profile users’ personal data. 2, Get a potential solution to a pain point on mechanical properties of FDM. Thus it is also potential to extending FDM utilization in many fields, such as skeleton printed by PEEK with more load applicability than conventional 3D printing logic, fiber strengthened printed parts, building printed by concrete and so on. 3, 6-axis 3D printing brings more accuracy, flexibility for additive manufacturing. 4, Supply an affordable solution for prosthetics printing with enough firmness and light weight structure. 5, Narrow the gap between human limitation and human potential by extending dancer’s prosthesis to surroundings. 6, Accomplish three to five time stronger structure with totally same appearance and infills, just by printing logic optimization. 7, Enrich database of prosthetics’ bionic controlling system for dancer’s behavior.

References

1. Martin, P. E. and D. W. Morgan. 1992. Biomechanical considerations for economical walking and running. Medicine & Science in Sports & Exercise 24 (4):467-74.

2. Hargrove LJ, Simon AM, Young AJ, Lipschutz RD, Finucane SB, et al. (2013) Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers. New England Journal of Medicine 369: 1237–1242. pmid:24066744

3. Herr HM, Grabowski AM (2012) Bionic ankle–foot prosthesis normalizes walking gait for persons with leg amputation. Proceedings of the Royal Society B: Biological Sciences 279: 457–464. pmid:21752817