Electricity and Control September 2022

CONTROL SYSTEMS + AUTOMATION : PRODUCTS + SERVICES

Continued from page 8

mate the system’s inspection process, achieving greater productivity and efficiency, without the need for techni cians or engineers to supervise or manage the process. “dStudio and Deep Learning deliver an inspection solution with high reliability, plus faster and more accu rate processing. The fail rate for Jendamark Automation’s inspection system is now almost zero,” says Bresler. This has proved a cost-effective solution, which delivers measurable ROI, fast. dStudio and Deep Learning have been designed for use with SICK sensors. Both products are intuitive and users do not need technical skills or AI knowledge to complete their implementation. However, the SICK team provides user training and technical support, to facilitate seamless adoption of the technology. “This combination of web service and AI software is usable across a range of industries. The solution is especially well-suited to the automotive and FMCG in dustries, where products are often highly reflective and accurate inspection is critical to ensuring product qual ity,” Bresler says. For more information contact SICK Automation. Tel: +27 (0)10 060 0550 Email: anton.bresler@sickautomation.co.za Visit: www.sick.com/za

that took several weeks). The highly reflective nature of the products and the low lighting of the plant environment meant that the InspectorP261 sensors failed to function effectively, as they could not ‘see’ the products. SICK Automation Market Product Manager and Market Applications Engineer, Anton Bresler, consulted with the customer over several days, to provide technical support during the programming process. Realising the short comings of the existing system, he proposed the use of SICK’s dStudio together with the Deep Learning software. dStudio is a web service which enables the user to up load pre-sorted product images to the cloud. The Deep Learning software has the capability to analyse the images and ‘train’ the user’s neural network (comprising SICK sen sor systems) to make decisions, quickly and accurately. Bresler worked alongside the customer, providing training on the functionality of the dStudio web service. He also guided the customer’s selection of product imag es for the system’s neural network – a process that took less than two hours. Following the implementation of the AI-powered neu ral network, programmed by the Deep Learning software, the customer’s inspection system can successfully iden tify individual product assemblies, even in conditions with low lighting. This has allowed the customer to auto

9 SEPTEMBER 2022 Electricity + Control