In a quest for efficiency on production lines, Trigo does not want to miss out on automation. The group specializing in the inspection and securing of production lines announced on Thursday the acquisition of the start-up Scortex, which specializes in quality control automation solutions for manufacturers.
This start-up, launched in 2016, has made industrial innovation its battlefield. His team has spent nearly six years building deep learning algorithms to automate inspection and generate real-time data and statistics on production quality. In this long process, large volumes of data are necessary for the machine to be able to sort out the defective products and the compliant products, as a human operator would do.
Trigo, which mainly works with transport manufacturers in the automotive and aeronautical sectors, began experimenting with use cases with Scortex a year and a half ago. Matthieu Rambaud, CEO of the Trigo group, notes that the automation solution lends itself particularly well to the inspection of “large and recurring” parts, such as bearing sub-assemblies.
Reliability and repeatability
“Quality is not a black or white domain, it is traversed by large gray areas” emphasizes Aymeric de Pontbriand, founder and CEO of Scortex.
If the human operator is not infallible, the driven machine on the other hand is a little more so: “The Scortex solution makes it possible to objectify subjective faults, with very high reliability and repeatability of the detection of these faults. explains Aymeric de Pontbriand.
For Trigo, the automated tool has another advantage: that of collecting information on a large scale that can improve product quality. Trigo offers industrial quality engineering and consulting services to its customers. Such a tool in its purse could thus help the company to “analyze the faults detected and understand the causes, and thus offer problem-solving solutions to our customers” explains Matthieu Rambaud.
An acceleration of the roadmap
Scortex, for its part, intends to double its workforce this year to accelerate the “go to market” of its product, and to scale up via the Trigo network. The startup also wants to continue its product roadmap and digitize the management of operational quality, “to address as many use cases as possible and provide modern tools that will allow experts to have something centered on real-time data. says Aymeric de Pontbriand.
At a more advanced stage, AI also leads to predictive. According to Aymeric de Pontbriand, “we can start to detect weak signals if the quality is drifting”. Thus, “before judging that a part is non-compliant, we will be able to alert earlier upstream. »