Automation Component and Method for Analyzing the Material Composition of a Test Object

A method for analyzing material composition of a test object, the method comprising:

• acquiring, by an impedance measuring unit via an electric test signal, an impedance spectrum dependent on at least one of a frequency and an amplitude of the test signal from the test object via a sensor;
• storing different material compositions in an allocation table;
• providing a test sample for each of the material compositions;
• employing a respective test sample for the test object in a detection phase, acquiring a specifiable number of test spectra from the respective test sample via the impedance measuring unit, and storing said number of test spectra in a sample detection unit as a training data set specific to the material composition;
• training a specific neural network for the number of test spectra of the respective material compositions during a learning phase;
• and
• analyzing a currently acquired impedance spectrum of the test object utilizing the neural networks specific to the material composition in an analysis phase, and outputting the material composition which has proved to best match the respective neural network specific for the material composition for the impedance spectrum to be analyzed as a result;
• wherein the impedance spectrum is specified as a complex-valued frequency curve, and separate neural networks specific for the material composition are trained for an absolute value, a phase angle, a real part and an imaginary part in each case, which operate in parallel in the analysis phase for the comparison with the currently acquired impedance spectrum to increase an accuracy of a statement about the material composition to be determined.

An automation component comprising:

• an impedance measuring unit configured to utilize an electric test signal to acquire an impedance spectrum dependent on at least one of a frequency and an amplitude of the test signal from a test object via a sensor;
• a processor module configured to process neural networks;
• a data memory in which a neural network is stored;
• an allocation table in which different material compositions are stored;
• a sample detection unit configured to acquire a specifiable number of test spectra for the material compositions specified in the allocation table in a detection phase via the impedance measuring unit;
• a data manager configured to store and manage the specifiable number of test spectra as training data sets specific to the material composition in the sample detection unit;
• an interface to a learning tool, which is configured in each case to provide a neural network specific to the material composition in a learning phase for respective training data sets;
• wherein the data memory stores specific neural networks are stored;
• and
• wherein the processor module enables a currently acquired impedance spectrum to be analyzed with the specific neural networks in an analysis phase such that the material composition to be output which, for the impedance spectrum to be analyzed has proved to best match the respective neural network specific to the material composition.