Method for determining process deviations of a melting process when generating a workpiece or when joining workpieces, comprising the steps: - providing a target value (SET_E10) for a process light of a melt pool, - detecting a radiation intensity, which is emitted by the melt pool, and/or a wavelength of the process light as an actual value (ACT_E10) and - comparing the target value (SET_E10) with the actual value (ACT_I10), in order to detect process deviations, wherein the target value (SET_E10) is determined by a radiation intensity and/or a wavelength of the process light, which radiation intensity and/or wavelength of the process light is determined by means of a determination method, wherein the process light comprises emission in visible and near infrared light and wherein the melt pool underlying the process light can be generated by irradiating a metal material with an energy beam along at least one path (x), wherein the energy beam is a laser beam, wherein the energy beam can be moved in accordance with a power profile along the path (x), comprising the determination method steps: - providing the power profile for a section (dtPOI) of the path (x) as an input variable for a machine learning model, in particular for a neural network, wherein the model is trained using historical power profiles and associated historical radiation intensities (E10H) and/or wavelengths of the process light from previous measurement series and wherein the model has a topology having coefficients of regression, and - determining the radiation intensity and/or the wavelength of the process light as an output variable of the model. - providing a target value (SET_E10) for a process light of a melt pool, - detecting a radiation intensity, which is emitted by the melt pool, and/or a wavelength of the process light as an actual value (ACT_E10) and - comparing the target value (SET_E10) with the actual value (ACT_I10), in order to detect process deviations, wherein the target value (SET_E10) is determined by a radiation intensity and/or a wavelength of the process light, which radiation intensity and/or wavelength of the process light is determined by means of a determination method, wherein the process light comprises emission in visible and near infrared light and wherein the melt pool underlying the process light can be generated by irradiating a metal material with an energy beam along at least one path (x), wherein the energy beam is a laser beam, wherein the energy beam can be moved in accordance with a power profile along the path (x), comprising the determination method steps: - providing the power profile for a section (dtPOI) of the path (x) as an input variable for a machine learning model, in particular for a neural network, wherein the model is trained using historical power profiles and associated historical radiation intensities (E10H) and/or wavelengths of the process light from previous measurement series and wherein the model has a topology having coefficients of regression, and - determining the radiation intensity and/or the wavelength of the process light as an output variable of the model. - providing the power profile for a section (dtPOI) of the path (x) as an input variable for a machine learning model, in particular for a neural network, wherein the model is trained using historical power profiles and associated historical radiation intensities (E10H) and/or wavelengths of the process light from previous measurement series and wherein the model has a topology having coefficients of regression, and - determining the radiation intensity and/or the wavelength of the process light as an output variable of the model.