7 research outputs found
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Deflection and the Prevention of Ingress within Laminated Tooling for Pressure Die-Casting
Wrthin the context ofrapid tooling, we are currently assessing the fundamental limitations
oflaminated tooling for pressure die-casting (PDC) applications. The use ofindividual laminates
to form a die-cast tool presents it own problems, namely the prevention of excessive deflection
that may lead to the ingress of pressurised molten aluminium between laminates. Ultimate
solutions lie with bonding and clamping techniques of which work is already underway. This
paper describes an initial study to establish the fundamental laminated die behaviour in extreme
die-casting environments.Mechanical Engineerin
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An Investigation of the Control Parameters for Aluminum 3003 under Ultrasonic Consolidation
In this article, we investigate an innovative solid-state welding technique called Ultrasonic
Consolidation (UC) that is being developed as a freeform process for the layered fabrication
of aluminium tapes. UC involves the use of high frequency, low amplitude mechanical
vibrations that induce combined static and oscillating shear forces to produce elastic-plastic
deformation at the work-piece interface. This tends to break up and disperse aluminium
oxide and permits atomic diffusion to occur. The work centres on material characterisation
of aluminium tapes for aerospace and tooling applications. This paper will look at the
mechanical properties of aluminum 3003 specimens prepared by UC using different control
parameters that will lead to the determination of a general process window.Mechanical Engineerin
Ensemble neural network approach detecting pain intensity from facial expressions
This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, threestream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients’ pain level accurately.Ghazal Bargshady, Xujuan Zhou, Ravinesh C. Deo, Jeffrey Soar, Frank Whittaker, Hua Wan