We help you to find the right machine configurations for your laser processing, additive manufacturing or CNC machines
You have developed a new machine or material and need machine parameter values to go to market? You have existing machines and materials and you search for new applications?
IMOLab reduces the time needed to find new machine parameter values from weeks or months to hours or days. The inbuilt free search function allows to find previously unknown features for existing machines and materials.
IMOLab reduces your go-to-market time of new machines or materials drastically and increases the quality of found parameter values, to make your customers happy. Additionally, it increases the value of your existing portfolio by finding new features and applications.
We are a young team of scientists, engineers and entrepreneurs that sets out to revolutionize the way machines are optimized and machine parameter values are found. The first idea of this algorithm was developed by Pavels Cacivkins in the Laser Processing Laboratory at Rezekne Academy of Technolgies (RTA). We are still part of RTA and cooperate with the Laser Processing laboratory and the Additive Manufacturing Laboratory at RTA.
Industrial automation is an important driver of our economy. Many innovations were introduced into this markets in the last years. However, the machine optimization and the search of parameter values is still a mostly manual and labor intensive task. IMOLab is offering the possibility of a full end-to-end automation of the manufacturing process and will allow in the future the automatic adjustment to new materials, machines and manufacturing processes. This will allow R&D departments to work on innovation instead of searching machine parameter values.
IMOLab is using a combination of advanced AI and Machine Learning Algorithms in combination with a user-friendly and intuitive user-interface, designed from engineers for engineers. As a by-product our software creates a digital twin of any machine it works with, this digital twin can in return be used for predictive maintenance and predictions of machine parameter values.