Project Background
Manufacturing efficiency in factories is subject to a wide range of constantly changing factors. Machine operators and other qualified production workers need to keep monitoring the particular stages of production and, if need be, respond appropriately and immediately. These manual technical activities may take a few hours, generating bottlenecks in the manufacturing process. Companies are then to a large extent dependent on the experience and skills of their staff. Stabilis, a company specializing in intelligent applications for Industry 4.0, decided to resolve these issues with our help.
Main Challenge
Our task was to create an algorithm to take values from the manufacturing machine and advise when and how settings should be altered. One of the challenges in this project was the fact that gathering and processing additional data (particularly data coming from the surroundings) was not possible. An additional algorithm was necessary to identify if any available sets of data contained data essential for the main algorithm, and then to select that data.
Our Approach
The first step was to check and analyze the data the customer possessed.
Our team developed an algorithm that signaled the need to alter parameters and settings of a manufacturing machine, and then indicate which of those will facilitate production.
Python and Scikit-learn libraries were used for developing the software, and the entire software solution was packaged into a Docker image and given to the customer with complete documentation.
Final Outcome
The Stepwise team created algorithms that are able to suggest parameters required to change the settings of manufacturing devices. Using this technology, manufacturing companies are now able to considerably and rapidly adjust appropriate settings for their machines, which enhances production effectiveness and saves on raw materials and other consumables.
If you wish to increase the effectiveness of your technological product, contact our Data Science department now!
