The platform is equipped for large-scale experimentation. In addition to the conventional mechanical testing devices, and the synergies with the other means of analysis available to the Ecole des Ponts ( microstructural analysis, imaging, micro-tomograph, computing cluster), Build'in also has several industrial robots. This offer is accompanied by the expertise provided by researchers, engineers and technicians dedicated to the operation of Build'in.
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Facilities
Collaborative robotics
The Freyssinet hall houses a cell dedicated to assembly, cutting and welding operations.
It is equipped with two ABB IRB 6620 type robots (150kg payload, 2.2m range), including one on a 9m track. The hall is also equipped with an overhead crane leading to a truck platform.
Large-scale 3D printing
A robotic cell is intended for large-scale 3D printing of cementitious materials or clay. This cell consists of a robot with a range of 2.2m and a payload of 150kg (ABB IRB 6620) and a print head for cementitious materials developed by XTreeE , an SME specializing in large-scale additive manufacturing technologies and partner of Build'in. An in-house raw earth and geopolymer extrusion tool is also available.
The cell is accessible by lifting tools (16 tonnes) and leads to a truck platform which allows the handling of the manufactured parts. Associated with this means of production are experimental tools (rheometer, presses, test benches) from the Navier laboratory. Synergies with the micro-tomograph platform of the Navier laboratory exist and make it possible to characterize the properties of materials on various scales.
Industrial prototyping
The platform is equipped with rapid prototyping means in its makerspace such as laser cutting, plastic 3D printers but also a foundry workshop or a composite development workshop. Two ABB IRB120 robots (payload of 2.5kg) are also available for precision applications, but also to prototype larger experiments with the large-scale robots.
It also has an infrastructure of computing clusters allowing to train deep neural networks and to develop machine learning solutions adapted to the needs of the construction sector.
