Workpiece storage device, method for assessing the loading state of a pick-up device and method for creating a machine learning model

A method for assessing a loading state of a pick-up device with receptacles for workpieces, in which the receptacles are individually adapted to shapes of the workpieces, the method comprising:

• acquiring sensor data with at least one sensor to determine a presence and/or location of the workpieces in the receptacles;
• transmitting the sensor data to a control device;
• feeding the sensor data as input values to at least one machine learning model;
• calculating from the input values in a machine learning model at least one binary output value, which is indicative of whether a statement about a loading state of the receptacles is applicable;
• and
• generating a signal including the binary output value.

A method for creating a machine learning model for assessing the loading state of a pick-up device with receptacles individually adapted to shapes of workpieces to be stored in the receptacles, the method comprising:

• creating a number of different training data sets, wherein each training data set contains sensor data that has been acquired by at least one sensor for defined loading states,
• wherein the loading states for which individual training data sets have been recorded are different from each other,
• feeding the sensor data to a machine learning model as input values for a training of the machine learning model,
• wherein, for training, at least one binary output value for each of the different loading states is predetermined to the machine learning model, and
• wherein the binary output value indicates whether a statement about the loading state is applicable.