Abstract: Systems and methods for machine based defect detection of 3D printed molds for orthodontic aligners are described. A method includes providing illumination of a 3D printed mold for an orthodontic aligner; generating a plurality of images of the 3D printed mold for the orthodontic aligner using one or more imaging devices, wherein each image of the plurality of images depicts a distinct region of the 3D printed mold for the orthodontic aligner; processing the plurality of images by a processing device to identify one or more types of manufacturing defects of the 3D printed mold for the orthodontic aligner, wherein for each type of manufacturing defect a probability that an image comprises a defect of that type of manufacturing defect is determined; and classifying, by the processing device, the 3D printed mold for the orthodontic aligner as defective based on identifying the one or more types of manufacturing defects.

Abstract: Implementations describe systems and methods for machine based defect detection of three-dimensional (3D) printed objects. A method of one embodiment of the disclosure includes providing a first illumination of a 3D printed object using a first light source arrangement. A plurality of images of the 3D printed object are then generated using one or more imaging devices. Each image may depict a distinct region of the 3D printed object. The plurality of images may then be processed by a processing device using a machine learning model trained to identify one or more types of manufacturing defects of a 3D printing process. The machine learning model may provide a probability that an image contains a manufacturing defect. The processing device may then determine, without user input, whether the 3D printed object contains one or more manufacturing defects based on the results provided by the machine learning model.

Abstract: Implementations describe systems and methods for machine based defect detection of molds for orthodontic aligners. A method of one embodiment of the disclosure includes receiving, by a processor, a digital representation of the fabricated mold for the orthodontic aligner, the digital representation having been generated based on imaging of the fabricated mold. The method further includes analyzing, by the processor, the digital representation of the fabricated mold to identify a quality-related property of the fabricated mold. The method further includes determining, based on the quality-related property, that the fabricated mold comprises a defect. The method further includes classifying, by the processor, the fabricated mold as defective based on determining that the fabricated mold comprises the defect.

Abstract: A method for inspecting a customized orthodontic aligner for manufacturing defects, the customized orthodontic aligner customized for a specific arch of a specific patient and a specific stage of orthodontic treatment, the method including obtaining images of the customized orthodontic aligner; identifying an identifier of the customized orthodontic aligner, determining a digital file based on the identifier, the digital file associated with the customized orthodontic aligner including a digital model of a mold used during manufacture of the customized orthodontic aligner, determining a intended property for the customized orthodontic aligner by digitally manipulating the digital model of the mold, determining an actual property of the customized orthodontic aligner from the images, determining whether there is a manufacturing defect in the customized orthodontic aligner by comparing the intended property with the actual property, outputting an output associated with the determination of whether there is a ma

Abstract: Implementations describe systems and methods for machine based defect detection of three-dimensional (3D) printed objects. A method of one embodiment of the disclosure includes providing a first illumination of a 3D printed object using a first light source arrangement. A plurality of images of the 3D printed object are then generated using one or more imaging devices. Each image may depict a distinct region of the 3D printed object. The plurality of images may then be processed by a processing device using a machine learning model trained to identify one or more types of manufacturing defects of a 3D printing process. The machine learning model may provide a probability that an image contains a manufacturing defect. The processing device may then determine, without user input, whether the 3D printed object contains one or more manufacturing defects based on the results provided by the machine learning model.

Abstract: An interface device provides an interface between testing equipment and an integrated circuit to be tested. The interface device includes a body member. A number of elongate contact members are mounted on the body member. Each contact member includes a contact end, adapted to contact a bond pad of the integrated circuit to be tested, and a body portion. The interface device also includes a guide member mounted on the body member. The guide member includes a substantially planar member having a number of apertures therein, and the contact end of each elongate member extending through a respective aperture in the guide member.