An autonomous laser kirigami method with low-cost real-time vision-based surface deformation feedback system

Wang, Zhujiang; Wang, Zimo; Ko, Woo Hyun; Iquebal, Ashif Sikandar; Nguyen, Vu Huy; Kazerooni, N. Afsar; Ma, Qiyang; Srinivasa, Arun R.; Bukkapatnam, Satish

doi:10.1007/s00170-021-07661-8

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…While Fibercuit is primarily a rapid prototyping method for small-scale flexible circuits, we also present additional techniques to fold copper substrates to form circuits beyond 2D. Unlike previous work that uses 𝐶𝑂 2 laser cutters to bend acrylic sheets based on gravity [8,22,28,47], a fiber laser can control the bending angle of a metallic sheet computationally [13,14,22]. We showcase how the bending of metal sheets can be combined with circuit prototyping to create 3D artifacts with integrated mechanical and electrical features.…”

Section: Fabricating Circuits Beyond 2dmentioning

confidence: 99%

Fibercuit: Prototyping High-Resolution Flexible and Kirigami Circuits with a Fiber Laser Engraver

Yan,

Sathya,

Yusuf

et al. 2022

Preprint

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Section: Fabricating Circuits Beyond 2dmentioning

confidence: 99%

Fibercuit: Prototyping High-Resolution Flexible and Kirigami Circuits with a Fiber Laser Engraver

Yan,

Sathya,

Yusuf

et al. 2022

Preprint

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“…The focus on quality assurance is becoming ever-more important due to the increasing global competition, democratization of manufacturers, and the use of novel materials and processes [2][3][4]. Due to the advancements and increasing availability of imaging systems [5,6], many of these imaging systems are increasingly considered not just of product inspection, typically at the end-of-the-lines, but also for on-line, intermittent and in situ quality monitoring [7,8].…”

Section: Introductionmentioning

confidence: 99%

Smart defect identification for manufacturing applications

Nakkina¹,

Vinayaka²,

masad³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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Quality control procedures are fundamental to any manufacturing process that intend to ensure that the product adheres to a defined set of requirements. However, manual quality control procedures tend to be visually laborious, tedious, and vulnerable to human mistakes. To meet the ever-growing demand for high-quality products, the use of intelligent visual inspection systems is gaining importance for deployment in production lines. Many works imbibing image processing techniques, machine learning, and neural network models have been proposed to perform defect detection and segmentation focused on specific domains of defects. Defects in manufacturing processes manifest in varied forms and attributes which add to the woes of developing a one-shot methodology for defect detection, while it is also very expensive to generate a dataset of images capturing the variety to train a one-shot machine learning model. This paper presents a framework that captures the essence of defect detection by proposing a mind-map to classify various defects based on visual attributes, and another to classify the various processing methodologies presented in the literature. In addition, the paper proposes a mapping between the class of defects and methodologies to act as a basis for anyone to come up with a solution for defect detection in their use cases. It also proposes an empirical recommendation formula, based on three image metrics to judge the performance of a method over a given class of images. This paper showcases the implementation of a Smart Defect Segmentation Toolbox assimilating methodologies like Wavelet Analysis, Morphological Component Analysis (MCA), Basic Line Detector (BLD), and presents case studies to support the working of the recommendation formula.

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