Thesis Defence: An AI-Augmented Cross-Platform Application for Enhanced Operator Training and Process Optimization inThermoforming
November 17 at 9:00 am - 1:00 pm
Nikta Attari, supervised by Drs. Abbas Milani and Pourang Irani, will defend their thesis titled “An AI-Augmented Cross-Platform Application for Enhanced Operator Training and Process Optimization in Thermoforming” in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering.
An abstract for Nikta Attari’s thesis is included below.
Defences are open to all members of the campus community as well as the general public. Please email abbas.milani@ubc.ca to receive the Zoom link for this defence.
Abstract
Thermoforming is a manufacturing process in which thermoplastic sheets are heated and shaped over a mould to produce desired geometries. Traditionally, determining the optimal heater configurations to achieve uniform and accurate temperature distributions has relied heavily on trial-and-error methods, resulting in inefficiencies in both time and resource usage. This thesis presents an interactive application that integrates predictive neural networks with a series of feedback-providing tools in order to assist in determining an optimal heater setting in thermoforming based on the user-specified temperature target. Users can interactively sketch their desired thermal distribution on a virtual canvas, and the application computes and visualizes both the corresponding heater configuration and the resulting temperature profile in real-time. This system combines two neural network architectures: a Backward Convolutional Neural Network (CNN) that determines proper heater power settings based on user-defined thermal targets (R2 up to 0.9650) and a Forward Fully Connected Neural Network (FCNN) that predicts temperature distributions from power input con-figurations with high predictive accuracy (R2 > 0.97). Both models were trained on synthetic datasets generated from a calibrated lab-scale simulation environment. These models are embedded within a user-friendly, Unity-based application that runs across multiple platforms, enabling continuous, interactive exploration of heat transfer behaviour in an augmented reality environment.
To evaluate the educational effectiveness of the developed application, we conducted a user study with participants from engineering and computer science backgrounds to evaluate the educational effectiveness of the developed application. The accuracy of task performance increased significantly when interacting with the predictive module (from 80.06% to 92.10%, p < 0.001, Cohen’s d = 1.27), indicating the application’s potential for conceptual learning and skill transfer. Furthermore, no differences in performance were found according to academic background, prior thermoforming skill, or immersive tool experience, indicating that the application ensures equitable learning for a variety of user groups.
By equipping operators with intelligent tools that reduce reliance on costly physical prototyping and accelerate decision-making, this work contributes to the goals of Industry 5.0, promoting sustainable, human-centric manufacturing through emerging digital innovations.
