OMNI3D and SMARTTECH Team Up Again for 3D Printing and Scanning of Supercar Part

3D technology is changing automotive design on the race track, and the Divergent Blade supercar and the Arrinera Hussarya supercar have both been the product of 3D printing technology in one way or another. Earlier this summer, Polish industrial 3D printer manufacturer OMNI3D 3D printed several parts, like mirror housings and intakes, for the Hussarya 33 on its large-format FFF Factory 2.0 Production System, after SMARTTECH, which manufactures optical 3D scanners, put its scanning systems to work during the car’s design phase.

Now, OMNI3D, Arrinera, and SMARTTECH are working together again to use 3D scanning technology to verify the high precision of 3D printed elements for the Hussarya supercar. When it comes to supercars like the Hussarya, it’s extremely important to have highly accurate prints.

Factory 2.0 3D printing process

OMNI3D’s Factory 2.0 Production System can be used to 3D print large components – up to 500 mm in each axis – using thermoplastic materials. In this instance, the industrial printer was used to manufacture a fastening for a set of three-way lights in the car’s front head lamp. Black ABS-42 material was used to print the part. The printer itself offers high dimensional accuracy, which is good when dealing with the automotive industry’s strict criteria.

Then, Arrinera brought SMARTTECH on to conduct quality control for the 3D printed final head lamp part that would be used in the supercar, with its industrial MICRON3D green 3D scanner.

MICRON3D Green Scanner

The MICRON3D green, which features a 10-megapixel detector, was brought in for the first part of the supercar’s rigorous quality control process, and SMARTECH used it to capture data about the part geometry so Arrinera could determine if anything needed to be fixed before putting the Hussarya through its paces on the track.

Head lamp

The MICRON3D green is able to achieve 30% better scanning results than typical white light scanners, thanks to its structured green LED light technology. This is very helpful for quality inspections, and small details can be reflected with crystal clear accuracy due to the scanner’s high resolution.

The scanner projects patterns onto the surface of whatever it’s scanning, which then deform where the surface curves. The measuring head uses a detector to record the visible patterns, which are later converted to a point cloud. MICRON3D green has a 600 x 800 mm field of view, which ensures results that are accurate to 0.084 mm.

Once the accuracy of the 3D printed head lamp was verified by SMARTTECH, a dimensional quality inspection was completed using Geomagic Control metrology software by 3D Systems. The program’s functionalities include thickness verification, dimensioning, and cross section creation, among others. The software used a reference CAD model and the measurement result to perform the final quality control check for the 3D printed head lamp fastening.

Arrinera Hussarya 33

According to OMNI3D, “Engineers accepted a tolerance of 0.3 mm. The entire operation allowed the creation of a deviation map with point deviation information. The most extreme point representing the material shortage used 78,6% of the assumed tolerance. For the opposite point (material excess) the value was 61.8%. This means that the precision of the 3D printed part has been 30% better than expected. What is more, the average of all deviations was -0.033 mm. The average material shortage was equal to -0.133 mm, while the average material excess was 0.112 mm.”

According to tests, the model had 30% better results than originally expected, and the part was ready to be installed in the Arrinera 33 supercar. Next month at formnext 2017 in Frankfurt, these solutions will be presented at OMNI3D’s Stand C29 in Hall 3.1. 3DPrint.com will also be attending formnext, to bring you all of the latest news from the show floor.

To get a closer look at the 3D printing and quality control process for the 3D printed final Arrinera part, check out the video below:

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