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Optical metamaterials have revolutionized imaging capabilities by manipulating light‐matter interactions at the nanoscale beyond the diffraction limit. Bilayer nanowire grating configurations exhibit significant potential as exceptional elements for high‐performance polarimetric imaging systems. However, conventional computational approaches for predicting electromagnetic responses are time‐consuming and labor‐intensive, and thereby, the practical implementation remains challenging through an iterative design, analysis, and fabrication process. Here, a deep learning‐based design process is presented utilizing an artificial neural network (ANN) trained on finite element method (FEM) simulations that enables the prediction of bilayer nanowire gratings‐based electromagnetic responses. The study validates predictions through nanoimprinted bilayer nanowire gratings, demonstrating the reliability of the ANN's predictions. Furthermore, the research identifies critical geometric parameters significantly influencing transverse magnetic (TM) and transverse electric (TE) transmission. The ANN model effectively tailors design for specific mid‐wavelength infrared (MWIR) wavelengths, which may provide a practical tool for rapidly designing and optimizing metamaterial for high‐performance polarizers. [ABSTRACT FROM AUTHOR]

Reconfigurable assembly systems enable a family of products to be assembled in a single system by adjusting and reconfiguring fixtures according to each product. The sharing of fixtures among different products impacts their robustness to fixture variation due to trade offs in fixture design (to allow the accommodation of the family in the single system) and to frequent reconfigurations. This paper proposes a methodology to achieve robustness of the fixture layout design through an optimal distribution of the locators in a multistation assembly system for a product family. This objective is accomplished by (1) the use of a multistation assembly process model for the product family, and (2) minimizing the combined sensitivity of the products to fixture variation. The optimization considers the feasibility of the locator layout by taking into account the constraints imposed by the different products and the processes (assembly sequence, data scheme, and reconfigurable tools' workspace). A case study where three products are assembled in four stations is presented. The sensitivity of the optimal layout was benchmarked against the ones obtained using dedicated assembly lines for each product. This comparison demonstrates that the proposed approach does not significantly sacrifice robustness while allowing the assembly of all products in a single reconfigurable line. [DOI: 10.1115/1.3123320] Keywords: fixture layout, robust design, product family, multistation assembly

A general and efficient methodology has been developed to analyze dimensional variations of a welded assembly, taking into account of weld distortion. Weld distortion is generally probabilistic because of the random nature of welding parameters such as the welding speed, maximum welding temperature, ambient temperature, etc. The methodology is illustrated by a very simple example of two perpendicular plates fillet-welded to each other. Two steps comprise the methodology: establishment of a weld distortion database and tolerance analysis using the database. To establish the database, thermo-elastoplastic finite element analyses are conducted to compute the weld distortion for all combinations of discrete values of major welding parameters. In the second step of tolerance analysis, the weld distortion retrieved from the database is taken into account in addition to the dimensional tolerances of the parts. As a result of such an analysis, sensitivities of the assembly's dimensional variations to the part tolerances and weld distortion are obtained, which could provide a guideline for improving dimensional quality of the assembly. [DOI: 10.1115/1.3160319] Keywords: weld distortion, tolerance analysis, finite element analysis