Artificial intelligence data models and predictions used by our customers in real life.
In an enterprise that produces paper, there were rupture and winding problems in the paper line. Before these problems occurred, anomaly detection with artificial intelligence was provided to make accurate predictions and to take precautions in time.
In order to predict the welding quality in the robot welding line; the quality was defined as OK or NOT OK with the artificial intelligence project.
We have developed an artificial intelligence system that predicts the amount of energy needed by generators that produce energy with fossil fuels and enables them to be activated by considering the optimum cost.
In the factory producing tires, anomalies such as malfunctions and problems in the machines negatively affected the production. Data that could be effective in detecting anomalies were collected directly from the production equipment and source. The data models developed using this data were trained with AI and it was ensured that the equipment sends notifications before anomalies occur.
Quality problems of the products had to be prevented in the biscuit producing facility. Quality problems were directly leading to productivity decline and damage. With the predictive quality application developed using artificial intelligence, it was possible to predict product quality and prevent possible problems.
Increasing trends in the digitalization of gas turbine plants allow significant opportunities for utilizing predictive maintenance methods to optimize engine performance. Historical engine performance, air quality metrics and ambient weather conditions were taken as a basis for predictive maintenance. Thus, machine maintenance need was predicted early.
Quality problems in wheel production were causing very critical problems in the future. In order to increase the quality of the produced wheels and to prevent possible quality problems, a solution was developed with the artificial intelligence supported predictive quality method.
The use of aluminum as a raw material for many products required quality to be very important. The quality of aluminum production directly affected other products. With the application of predictive quality using artificial intelligence, it was ensured that the quality values were predictable.
Predictive quality is widely seen as a critical service for the future of the wind energy industry. It can help wind operators to cut costs and optimize energy production. With the application of predictive quality using artificial intelligence, it was ensured that the quality values were predictable.
The unexpected maintenance needs of the machines in the automotive conveyor line caused production to stop and orders could not be delivered at the specified time. Artificial intelligence algorithms developed using the data collected from the equipment provided early detection of possible maintenance needs.
In the facility producing glassware, where quality problems are frequently experienced, artificial intelligence models were developed using data that affect quality. Thus, the quality values can be predicted in advance.