Hexicurity (Boerne, Texas, USA), a manufacturer of security access integration solutions for commercial doors, turnstiles, and elevator systems, is developing an AI-enabled vision system to improve the quality of wire crimps in one of its products, called TransVerify.
TransVerify is a networking solution that integrates the badging security systems of individual tenants in a multi-tenant building or campus. This allows employees of the various tenants to access both the building and the office suite with a single badge. “We offer a measure of privacy to the corporations. They don’t have to release their entire employee list to the base building,” explains George Mallard, vice president of engineering for Hexicurity.
The Hexicurity system contains hardware and software in one self-contained unit. Describing it as an IoT-type “appliance,” Mallard says, “It is set up, and it runs for years, so reliability of the equipment is paramount.”
Hexicurity manufactures wire crimps for the verifier and distributor components that are then integrated into the TransVerify system.
Manual Wire-Crimping Process
Each TransVerify system contains about 100 wire crimps, which Hexicurity produces using a precision crimping machine from Molex (Lisle, IL, USA). Operators manually position a wire on the machine for each crimp—a process they repeat several hundred times per day—and then trigger the crimp process with a treadle.
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However, the design of the machine partially blocks the operators’ view of the crimp point, making it difficult for them to position the wire precisely.
While the company has had only two instances of faulty wire crimps in its history, Hexicurity’s managers are developing a semi-automated process using a machine learning-based vision system to trigger the go/no-go decision to crimp a wire. Operators will still position the wire manually and press a treadle to initiate the crimping action.
Adopting Industrial Automation Using Machine Vision
Mallard says the company built a machine learning (ML) model to evaluate the position of the wire on the Molex crimping machine. After tweaking the model, Hexicurity will test it.
In a later phase of crimping automation, Hexicurity plans to develop a second algorithm to evaluate the quality of the crimp after it is completed, leading to an accept/reject decision.
Even without the machine learning capabilities, the Hexicurity team installed the components as an interim imaging solution, allowing employees to have an unobstructed view of how the wire is positioned. “Right now, it’s just an imaging system. It throws an image up on the screen so that the operator can see it,” Mallard says, adding that the use of an imager has already improved crimping process flow and production cycle time.
Machine Vision Imaging System
The Hexicurity team designed the system using numerous components including:
- Raspberry Pi 4K camera from Raspberry Pi Foundation (Cambridge, United Kingdom)
- STM32N6 ARM microcontroller from ST Microelectronics (Geneva, Switzerland), which integrates an ARM processor with a proprietary Neural-ART accelerator
- SL410A 4-10 mm varifocal lens from Theia Technologies (Wilsonville, OR, USA)
- 52 mm circular polarizer lens filter from Amazon (Seattle, WA, USA)
- PAR16 LED bulb, which replaces the incandescent light that came with the Molex machine and provides enough illumination to produce good images
