GM has been working hard to close the competitive gap between them and other car manufacturers, which is why it has turned to 3D scanning of competitor vehicles so they can learn as much about the automobiles as possible. They want to know what makes a Mercedes what it is or what makes a Chrysler what it is. To do this, they run the rival designs through computers and they analyze them.
What is surprising is that this is not a practice that GM just started doing. In fact, they have performed 3D scanning of competitor models for over 10 years. What the practice does is turn 3D objects (cars, trucks, and SUVs) into data on a computer screen. They don’t always do this for reverse engineering or benchmarking, but to turn a clay model into mathematical data. With the more widespread usage of 3D printing, this mapping of competitor designs could result in the engineers at GM being able to easily and quickly upgrade their vehicles.
How Scanning Works
When the engineers at GM scan their competitor’s vehicles, they have to tear the competitor vehicle down. Every detail is scanned by first using a blue-light scanner for the larger bits and pieces and a red-light scanner for the more intricate details. The position of each part has to be recorded so that the vehicle can be put back together digitally. The product is usually a reverse-engineered computer model. GM engineers and designers then study the result so they can learn from the successes and failures of their competitors.
Piece by piece, 3D scanning allows GM to carefully evaluate everything from seat seams to the details of the muffler. Regardless of how ethical this may or may not be, it shows how this practice is playing an important role in benchmarking, which is something that is practiced throughout the entire auto industry.
While the three-dimensional scanning of competitor’s vehicles by GM is well-known, there are other industries that turn to 3D scanning for one reason or another. Perhaps it is for the same reason that GM does it or because companies like to evaluate their own products to improve them. Mathematical data, although it may seem useless to some, can be laid over the data of another product and the two easily compared to evaluate integrity. This helps ensure that consumers receive the best possible products and that those products continue improving over time.