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This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on variousin-situandex-situimaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM).In-situimaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered fromin-situimaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand,ex-situimaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.
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Engineering nonlinear activation functions for all-optical neural networks via quantum interference
All-optical neural networks (AONNs) promise transformative gains in speed and energy efficiency for artificial intelligence (AI) by leveraging light's intrinsic parallelism and wave nature. However, their scalability has been fundamentally limited by the high power requirements of conventional nonlinear optical elements. Here, we present a low-power nonlinear activation scheme based on a three-level quantum system driven by dual laser fields. This platform introduces a two-channel nonlinear activation matrix with self- and cross-nonlinearities, enabling true multi-input, multi-output optical processing. The system supports tunable activation behaviors, including sigmoid and ReLU functions, at ultralow power levels (17μW per neuron). We validate our approach through theoretical modeling and experimental demonstration in rubidium vapor cells, showing the feasibility of scaling to deep AONNs with millions of neurons operating under 20 W of total optical power. Crucially, we demonstrate the all-optical generation of gradient-like signals with backpropagation, paving the way for all-optical training. These results mark a significant advance toward scalable, high-speed, and energy-efficient optical AI hardware.
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- Award ID(s):
- 2211989
- PAR ID:
- 10651604
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 33
- Issue:
- 25
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 52458
- Size(s):
- Article No. 52458
- Sponsoring Org:
- National Science Foundation
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