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Luca Quagliato

Assistant Professor (RTT)

University of Trento

Italy

Research Interests

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About

Dr. Luca Quagliato is a Research Professor at Ewha Womans University in South Korea. His research interests encompass finite element analysis, machine learning applications in biomedical engineering, and material properties modeling. He has published extensively on topics including bone mineral density prediction, hyperelastic properties of heel pads, and the mechanical performance of advanced composite materials.

Articles (31)

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Collaborators (2)

Seok Chang Ryu

Ewha Womans University

SOUTH KOREA

View details

Taeyong Lee

Professor

Ewha Womans University

SOUTH KOREA

View details

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Luca Quagliato

Research Interests

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About

Articles (31)

Gait parameters and plantar pressure estimation from the prosthesis evaluation questionnaire using a synthetic-data-enhanced supervised learning model: A proof-of-concept study

Synthetic process modeling for ring rolling via physics-guided GANs and analytical transfer learning

Proxy-Physics-Informed and Transfer Learning Networks for Radial-Axial Ring Rolling (RARR) under Varying Data Availability

Analytical models’ driven machine learning modeling for the setting and control of the radial-axial ring rolling process

Corrigendum to “Investigation on the static and cyclic anisotropic mechanical behavior of polychloroprene rubber (CR) reinforced with tungsten nano-particles”(Engineering Fracture Mechanics, (2020), 235, C, (107183), (S0013794420307669), 10.1016/j.engfracmech.2020.107183)

Toward Robust Gait Identification: A Frequency Domain Approach in Varied Surveillance Environments

Identification of heel pad thickness and visco-hyperelastic properties by finite element analysis and Gaussian process regression

Gradient-enhanced neural network and extreme gradient boosting modeling for the prediction of the 3D bone mineral density distribution from 2D-DXA scans

Heel pad’s hyperelastic properties and gait parameters reciprocal modelling by a Gaussian Mixture Model and Extreme Gradient Boosting framework

Modeling of tensile strength and wear resistance in friction stir processed MMCs by metaheuristic optimization and supervised learning

Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters

Investigation of compressive strength of 3D-printed PLA/continuous glass fibers composites manufactured by simultaneous impregnation extrusion-based additive manufacturing

Investigation of compressive strength of 3D-printed PLA/continuous glass fibers composites manufactured by simultaneous impregnation extrusion-based additive manufacturing

Expert-Informed Neural Network (EINN) for the Forming Depth Prediction from a Small-Scale Sheet Metal Forming Database

Gradient Enhanced-Expert Informed Neural Network (GE-EINN) for Forming Depth Prediction from a Small-Scale Metal Stamping Dataset

Optimizing the Graft Size in the Evans Osteotomy to Minimize the Calcaneocuboid Joint Pressure by Highly Realistic in-Silico Analysis

Gaussian process regression-driven deep drawing blank design method

Influence of Grinding Tool Mesh Size and Rotational Speed on Post-Machining Quality of CFRP Laminates by Acceleration Signal and Surface Roughness Analyses

Influence of Grinding Tool Mesh Size and Rotational Speed on Post-Machining Quality of CFRP Laminates by Acceleration Signal and Surface Roughness Analyses

Gaussian process regression-driven deep drawing blank design method

On Improving Sheet Metal Formability in Hydrodynamic Deep Drawing Process through a Solid‐Oil‐Type Mineral Grease Pressure Medium

Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process

Extreme gradient boosting-inspired process optimization algorithm for manufacturing engineering applications

Manufacturing Process, Tensile-Compressive, and Impact Properties of Tungsten (W)-Particle-Reinforced SLA Methacrylate

Geometry evolution prediction and process settings influence in profiled ring rolling

Geometry evolution prediction and process settings influence in profiled ring rolling

Experimental and numerical investigations on the failure behavior of metal skin and carbon fiber-reinforced polymer core structures considering layup and manufacturing process conditions

FEA Modeling of Soft Tissue Interaction for Active Needles With a Rotational Tip Joint

Notch effect in 20% short carbon fibre-PA reinforced composites under quasi-static tensile loads

Quasi-ductile to brittle transitional behavior and material properties gradient for additively manufactured SLA acrylate

A Buckling Instability Prediction Model for the Reliable Design of Sheet Metal Panels Based on an Artificial Intelligent Self-Learning Algorithm

Collaborators (2)

Seok Chang Ryu

Taeyong Lee

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