Learning factory robots3/24/2023 ![]() ![]() Readily available! Prepared! Waiting to be used in your factory. We approached the rules for detecting bottlenecks in the production process in a similar way: by cataloging them in the library of processes (for example, the packing process or the number of machines connected on the production line). With that in place, it should be possible to connect such ready models to the definitions of machines in the library and, a crucial step, generalize them to a specific class of machine, not only to a single machine in the library. Next, we had to catalog it all and create failure prevention models. We reached the conclusion that a key measure would be building a library of machines, types of machines, and types of production processes. Those projects were always huge and expensive, and when they were brought over to the manufacturing industry, they simply didn’t add up financially. This meant collecting hundreds of measuring points, together with hundreds of thousands of measurements – and with a short time frame for making a go/no-go decision. For example, one of those companies was in the airline industry, and their project dealt with detecting possible engine failures when airplanes were parked in hangars. We kept on talking to large companies involved in big projects related to predictive maintenance. ![]() Our goal is for users to simply connect their machines to our platform and immediately start benefiting from the self-learning system, without the time-consuming creation of rules, algorithms or predictive models. ![]() We want the implementation process of a platform with such intelligence and a self-learning recommendation system to be very simple for the end-user. It means that every unit produced has lower energy cost while workers receive recommendations given “on a plate” which allows them to focus on their tasks rather than on looking for solutions to typical problems. We want LogiX to help factories become eco-factories that do not waste energy by idle work, but instead, we want them to work with maximum effectiveness. The recommendations are intended to minimize unplanned downtime and increase the number of manufactured goods, while also minimizing the energy consumption and making sure that the people involved in the production process are working in a hospitable environment. That is how we came up with the concept of extending the platform with intelligent recommendations about the ongoing production process and the condition of the machines being used. LogiX – which we’ve been developing since 2018 – is a solution for monitoring the production processes at factories and increasing their efficiency. We did some brainstorming in our team and dug through 10 years of our implementations at factories, contrasting it all with the capabilities of the cloud technology we are using as the base of our LogiX platform. It all started last year during the COVID pandemic.
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