Abstract: A method is disclosed for selecting an optimal value for an adjustable parameter of a structured light metrology (SLM) system, for scanning an object. The SLM system performs test scans of the object to acquire a plurality of sets of measurements of the object, wherein a different value is used for the parameter for each test scan. For each test scan, a value of a quality metric is calculated, based on the set of measurements of the object associated with the test scan and simulation data representing a simulated scan of the object by the SLM system. A test scan is then identified that has a quality metric value that satisfies a specified optimization criterion; and a value of the adjustable parameter that was used for the identified test scan is selected as the optimal value of the adjustable parameter, for scanning the object.

Abstract: A method is disclosed for selecting an optimal value for an adjustable parameter of a structured light metrology (SLM) system, for scanning an object. The SLM system performs test scans of the object to acquire a plurality of sets of measurements of the object, wherein a different value is used for the parameter for each test scan. For each test scan, a value of a quality metric is calculated, based on the set of measurements of the object associated with the test scan and simulation data representing a simulated scan of the object by the SLM system. A test scan is then identified that has a quality metric value that satisfies a specified optimization criterion; and a value of the adjustable parameter that was used for the identified test scan is selected as the optimal value of the adjustable parameter, for scanning the object.

Abstract: The present disclosure relates to systems and methods for screening a formulation of a material being printed in an additive manufacturing process, in situ, to enable rapid analysis, modeling and modification of at least one characteristic associated with the material formulation. In one embodiment the system includes a computer and an experimental planning software module that includes a historical database of sample material test results, a machine learning software module, and a new batch formulation generation software module. The experimental planning software module enables new material formulations to be determined in situ and in real time, using one or more machine learning models, and new material samples to be printed in accordance with newly determined material formulations, for closer inspection and evaluation of at least one desired characteristic of the sample materials.