Abstract: An electronic device may have a hinge that allows the device to be flexed about a bend axis. A display may span the bend axis. The device may have a stainless steel and/or carbon fiber reinforced polymer layer with slots. The slots may overlap the bend axis. The slots may have at least one property that varies in a non-linear manner as a function of position on the layer. The device may include an adhesive layer with a cutout that overlaps the slots. An interface that defines the cutout may have a plurality of recesses with varying depths. A flexible printed circuit may be attached to an edge of the display panel. The display cover layer may overlap a bonding region between the flexible printed circuit and the edge of the display panel. A UV-curable gap filler may be interposed between the flexible printed circuit and the display cover layer.

Abstract: An electronic device may have a hinge that allows the device to be flexed about a bend axis. A display may span the bend axis. The device may have a stainless steel and/or carbon fiber reinforced polymer layer with slots. The slots may overlap the bend axis. The slots may have at least one property that varies in a non-linear manner as a function of position on the layer. The device may include an adhesive layer with a cutout that overlaps the slots. An interface that defines the cutout may have a plurality of recesses with varying depths. A flexible printed circuit may be attached to an edge of the display panel. The display cover layer may overlap a bonding region between the flexible printed circuit and the edge of the display panel. A UV-curable gap filler may be interposed between the flexible printed circuit and the display cover layer.

Abstract: Systems and methods for training a machine learning model for non-destructive evaluation. A plurality of training samples are generated, with a subset of the plurality of training samples representing an article with a defect or a combination of defects. Training samples are generated by generating a virtual model of the article with defect at a computer system, generating a simulated signal representing an output of a non-destructive evaluation system scanning the physical article using the virtual model at the computer system, and associating a representation of the simulated signal with parameters representing a characteristic of the virtual model of the article with defects. The machine learning model is trained on the plurality of training samples. The trained machine learning model is applied on to measurements collected from physical articles to characterize or quantify defects.

Abstract: In devices with flexible displays, multilayer adhesive stacks may be included. A multilayer adhesive may attach a flexible display panel to the display cover layer in an electronic device. Including multiple layers of adhesive in the adhesive stack (as opposed to a single layer) provides more degrees of freedom for the tuning and optimization of the properties of the adhesive stack. The multilayer adhesive stack therefore has better performance than if only a single layer of adhesive is used. The multilayer adhesive stack may include one or more layers of soft adhesive, hard adhesive, hard elastomer, hard polymer, and/or glass to optimize the mechanical and optical performance of the multilayer adhesive stack. Soft adhesive layers may be included to optimize lateral decoupling (e.g., during folding and unfolding) of the adhesive stack. Harder layers may be included to provide rigidity and prevent denting during impact events.