A method of monitoring an additive manufacturing build process of a workpiece using an additive manufacturing system comprising a first powder bed and a recoater configured to coat the first powder bed with a layer of powder, the method comprising: a first phase comprising: depositing a first layer of powder onto the first powder bed of the additive manufacturing system; scanning, with a profilometer (24, 24'), a topography of a portion of the first layer of the powder bed; and creating a topographical profile of the portion of the first layer, the topographical profile comprising data points corresponding to the topography of the portion of the first layer of the powder bed; capturing an image of the first powder bed with a camera; combining the image and the topographical profile to create a first data set; transferring the first data set to a machine learning model; and generating, with the machine learning model, a set of training data based on the first data set; and a second phase comprising: depositing a second layer of power onto a second powder bed with the additive manufacturing system; capturing an image of the second powder bed with the camera; comparing the image of the second powder bed to the set of training data; determining a set of deviations from a nominal model of the first powder bed based on comparison of the image of the second powder bed to the set of training data, wherein the deviations are measured by comparing a measured height of the layer of the powder bed to a height of the nominal model; labelling a deviation from the set of deviations that is greater than a numerical threshold; and identifying the deviation that is greater than the numerical threshold as a defect. a first phase comprising: depositing a first layer of powder onto the first powder bed of the additive manufacturing system; scanning, with a profilometer (24, 24'), a topography of a portion of the first layer of the powder bed; and creating a topographical profile of the portion of the first layer, the topographical profile comprising data points corresponding to the topography of the portion of the first layer of the powder bed; capturing an image of the first powder bed with a camera; combining the image and the topographical profile to create a first data set; transferring the first data set to a machine learning model; and generating, with the machine learning model, a set of training data based on the first data set; and depositing a first layer of powder onto the first powder bed of the additive manufacturing system; scanning, with a profilometer (24, 24'), a topography of a portion of the first layer of the powder bed; and creating a topographical profile of the portion of the first layer, the topographical profile comprising data points corresponding to the topography of the portion of the first layer of the powder bed; capturing an image of the first powder bed with a camera; combining the image and the topographical profile to create a first data set; transferring the first data set to a machine learning model; and generating, with the machine learning model, a set of training data based on the first data set; and a second phase comprising: depositing a second layer of power onto a second powder bed with the additive manufacturing system; capturing an image of the second powder bed with the camera; comparing the image of the second powder bed to the set of training data; determining a set of deviations from a nominal model of the first powder bed based on comparison of the image of the second powder bed to the set of training data, wherein the deviations are measured by comparing a measured height of the layer of the powder bed to a height of the nominal model; labelling a deviation from the set of deviations that is greater than a numerical threshold; and identifying the deviation that is greater than the numerical threshold as a defect. depositing a second layer of power onto a second powder bed with the additive manufacturing system; capturing an image of the second powder bed with the camera; comparing the image of the second powder bed to the set of training data; determining a set of deviations from a nominal model of the first powder bed based on comparison of the image of the second powder bed to the set of training data, wherein the deviations are measured by comparing a measured height of the layer of the powder bed to a height of the nominal model; labelling a deviation from the set of deviations that is greater than a numerical threshold; and identifying the deviation that is greater than the numerical threshold as a defect.