iTSSe TSS ADVANCED MATERIALS & PROCESSES | JULY/AUGUST 2025 49 iTSSe TSS JTST HIGHLIGHTS Fig. 4 — Diagram illustrating the model structure of the TBC. ENHANCING MECHANICAL BEHAVIOR ASSESSMENT IN POROUS THERMAL BARRIER COATINGS USING A MACHINE LEARNING FINE-TUNED WITH GENETIC ALGORITHM Ahmed A.H. Alkurdi, Hani K. Al-Mohair, Paul Rodrigues, Marwa Alazzawi, M.K. Sharma, and Atheer Y. Oudah In this study, a Genetic Algorithm-Enhanced Machine Learning (GAML) model has been established to predict stress variations (σave) and equivalent strain (εcr) in porous thermal barrier coatings (TBCs) subjected to diverse thermal loading conditions. Remarkable predictive performance was observed, with determination coefficient values of 0.971 for εcr and 0.939 for σave, emphasizing a robust correlation between predicted and actual values. (Fig. 4) Fig. 3 — CAD replica of the robot-profilometer-spray table system in Robot Operating System. IN SITU MEASUREMENT OF TRACK SHAPE IN COLD SPRAY DEPOSITS Scott E. Julien, Nathaniel Hanson, Joseph Lynch, Samuel Boese, Kirstyn Roberts, Taşkin Padir, Ozan C. Ozdemir, and Sinan Müftü Successfully cold spraying free-form parts that are close to their intended shape requires knowing the fundamental shape of the sprayed track, so that a spray path can be planned that builds up a part from a progressively overlaid sequence of tracks. Several studies have measured track shape using ex situ or quasi-in situ approaches, but an in situ measurement approach has, to the authors’ knowledge, not yet been reported. (Fig. 3) Fig. 5 — Detected anchor boxes and predicted process parameters with confidence levels. REAL-TIME THERMAL SPRAY PROCESS MONITORING USING CONVOLUTION NEURAL NETWORK DEEP LEARNING ARCHITECTURES K. Malamousi, K. Delibasis, and S. Kamnis Thermal spray is essential for surface modification and coating of materials but is challenging to monitor in real-time due to high velocities, temperatures, and continuous torch or part movement. This study demonstrates that a specific convolutional neural network architecture, which divides the image into a grid and predicts bounding boxes and class probabilities for each cell, can accurately monitor high-velocity-oxy-fuel thermal spray processes in real-time. (Fig. 5) Fig. 2 — Growth of a CS spot from Gaussian to triangular. COLD SPRAY ADDITIVE MANUFACTURING: A REVIEW OF SHAPE CONTROL CHALLENGES AND SOLUTIONS Roberta Falco and Sara Bagherifard Despite the latest steep technological advancements, a significant hindrance to the wide application of cold spray (CS) in this field is shape accuracy. Deposit shape modeling can play a major role in addressing this challenge and counterbalancing the restrictive resolution issues by predicting the deposit shape, as a function of kinetic process parameters. Macroscale deposition modeling can furthermore boost automated process planning for high geometrical control. (Fig. 2) 15
RkJQdWJsaXNoZXIy MTYyMzk3NQ==