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Spare Parts 3D unveils 2D-drawing to 3D-model software

Théia enhances the capabilities of the company's DigiPART software - facilitating the identification, digital twin creation, and sourcing of 3D printing suppliers for economically and technically viable spare parts

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Spare Parts 3D (SP3D), a Toulouse-based startup established in 2018, is known for its groundbreaking innovations in the industrial sector, notably its DigiPART software. This advanced AI technology is designed to identify, digitize, and 3D print the spare parts of its clientele – earning the company the prestigious label of a Young Innovative Company. The company has now unveiled ‘Théia’, another innovative technology that is the culmination of a three-year R&D project initiated in 2022 – conducted in collaboration with the university research laboratory LURPA at ENS Paris-Saclay and supported by the Defense Innovation Agency (AID).

Spare Parts 3D unveils 2D-drawing to 3D-model software. Théia enhances the capabilities of the company's DigiPART software. Théia transforms 2D technical drawings into 3D models automatically – addressing a longstanding need in the manufacturing industry. This advancement significantly enhances the capabilities of DigiPART software – facilitating the identification, digital twin creation, and sourcing of 3D printing suppliers for economically and technically viable spare parts.

The technology is particularly noteworthy for its ability to streamline the digitization process of spare parts inventories. By converting 2D drawings to 3D models swiftly and efficiently, Théia democratizes access to digital passports and additive manufacturing, thereby fostering a more rapid and cost-effective digitization process.

“Théia addresses two major challenges: on one hand, reading, understanding and interpreting the information present on the drawing, and on the other, obtaining a 3D shape from its representation on 2D plans, while respecting the geometric and dimensional specifications of the original object. The development of Théia is the result of a harmonious marriage between new developments in AI (deep learning, segmentation, automatic labeling, etc.), mastery of classic computer vision technologies (optical symbol recognition, feature matching) and semantic analysis of technical drawings. This combination makes it possible to identify the various semantic layers of the 2D drawing and thus to interpret the technical drawing in an optimal way for its 3D reconstruction,” said Professor Nabil Anwer, CIRP Fellow, Professor at Paris-Saclay University, Deputy Director of LURPA at ENS Paris-Saclay, and expert in shape engineering and 3D reconstruction.

Théia offers multiple benefits such as the reduction of storage costs and enhancement of supply chain efficiency by enabling on-demand, localized production of parts – thereby supporting the Ministry of Defence’s innovation agenda; the promotion of sustainable manufacturing practices by reducing material waste and transport-related CO2 emissions – contributing to global decarbonization efforts; and fostering industrial independence and potential reshoring of production – mitigating reliance on unstable foreign supply chains.

“Theia is a world first. It is a disruptive innovation, that represents a major technological leap forward that makes it possible to digitalize supply chains on a massive scale for sectors such as energy, defense, petrochemicals, railways, shipping and mining, where the need for spare parts is considerable. Theia will deliver considerable savings by reducing physical inventories of spare parts. According to a 2018 study by Theano Advisors, the global inventory value of printable parts was $174 billion (€160 billion). Considering an average inventory cost of 20%, Theia will save companies $34 billion (€31 billion) a year through digitalization,” said Paul Guillaumot, Founder and CEO of Spare Parts 3D. “The next step will be to generalize 3D reconstruction from drawings and parts of any complexity, and to obtain parametric files. The latter will enable us to automatically modify them according to the chosen manufacturing processes.”

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Edward Wakefield

Edward is a freelance writer and additive manufacturing enthusiast looking to make AM more accessible and understandable.

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