Browsing by Author "Luo, Yang"

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    A framework for recovering waste heat energy from food processing effluent
    (MDPI, 2022-12-21) Luo, Yang; Jagtap, Sandeep; Trollman, Hana; Garcia-Garcia, Guillermo
    Effluent water from food processing retains considerable heat energy after emission from treatment systems. Heat recovery technologies that may be appropriate for implementation in the food processing industry have been widely explored, and selection of the most suitable methodologies has been pursued. A four-stage framework is introduced in this paper to evaluate the potential recoverability of waste heat along with acceptor streams. The systematic approach utilizes thermal and temporal compatibility tools and cost–benefit analyses to determine the ideal heat-recovery equipment for food processing effluent. The applicability of this framework is demonstrated through an industrial case study undertaken in a vegetable canning processing facility. Based on the findings, the framework yields an efficient and optimized heat recovery approach to reducing the total energy demand of the facility.
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    ItemOpen Access
    Optimizing industrial etching processes for PCB manufacturing: real-time temperature control using VGG-based transfer learning
    (Springer, 2025-04-01) Luo, Yang; Jagtap, Sandeep; Trollman, Hana; Garcia-Garcia, Guillermo; Liu, Xiaoyan; Abdul Majeed, Anwar P. P.
    Accurate temperature control in Printed Circuit Board (PCB) manufacturing is essential for maintaining high-quality etching results. Automated monitoring using machine vision and deep learning offers an effective approach for this task. This study investigated a feature-based transfer learning technique for classifying temperature readiness in infrared images of the etching process. The captured dataset containing 470 ‘Production-Ready’ and 480 ‘Not-Ready’ infrared images of the etchant tank was utilized. Pre-trained Visual Geometry Group (VGG) Convolutional Neural Network (CNN) models, specifically VGG16 and VGG19, were employed to extract discriminative features from these images. Logistic Regression (LR) classifiers were then trained on these features to classify the infrared images. The performance of the VGG16-LR and VGG19-LR pipelines was evaluated on training, validation, and test sets using a 60:20:20 split. While both pipelines achieved 100% accuracy on the training sets, the VGG19 pipeline showed exceptional performance, achieving a validation accuracy of 95%, and a test accuracy of 99%. The VGG16 pipeline also demonstrated robust performance, achieving 96% accuracy on both the validation and test sets. Considering the dimensions and the overall efficiency of the pipeline, it was determined that the VGG19-LR model was appropriate for the captured dataset. The high accuracy indicates that transfer learning is suitable for categorizing temperature fluctuation in infrared thermography, as opposed to training a deep neural network from scratch. Computer vision and deep learning provide automated and precise temperature management during the etching process, leading to enhanced efficiency in PCB manufacturing.