Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts

Abstract

Additive manufacturing demonstrates tremendous progress and is expected to play an important role in the creation of construction materials and final products. Contactless (remote) mechanical testing of the materials and 3D printed parts is a critical limitation since the amount of collected data and corresponding structure/strength correlations need to be acquired. In this work, an efficient approach for coupling mechanical tests with thermographic analysis is described. Experiments were performed to find relationships between mechanical and thermographic data. Mechanical tests of 3D-printed samples were carried out on a universal testing machine, and the fixation of thermal changes during testing was performed with a thermal imaging camera. As a proof of concept for the use of machine learning as a method for data analysis, a neural network for fracture prediction was constructed. Analysis of the measured data led to the development of thermographic markers to enhance the thermal properties of the materials. A combination of artificial intelligence with contactless nondestructive thermal analysis opens new opportunities for the remote supervision of materials and constructions.

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

“…Jayasudha et al, 2022 employed ANN and Decision Tree Regression to predict the tensile strength of printed elements [98]. Similar findings are obtained in [99,100]. ML modeling can cater to various parameters related to the concrete mix design.…”

Effect of individualized narrative nursing mode on recovery of elderly patients with fracture complicated with cerebrovascular accident

“…We also create narrative materials based on the patient’s mood and needs, and feedback on treatment interventions and outcome evaluations to discover the highlights of the patient’s illness experience. [29,30] Provide treatment documentation based on the patient’s needs, thereby strengthening the patient’s positive self-perception, weakening the patient’s negative self-perception, and reinventing themselves. [31] These bright spots are good things for patients, and helping patients to recall these beautiful things can not only relax the mood of patients.…”

Machine Learning-Based Fatigue Life Prediction of Functionally Graded Materials Using Material Extrusion Technology

“…This investigation involves the assessment of uniaxial fatigue properties of plastics through tensile and fatigue tests conducted using a Testresources 810E4 [20] load frame equipped with 15 KN load cells in accordance with ASTM E606M standard test protocols. Specifically, the experimental setup employed a closed-loop servo-hydraulic machine of the aforementioned model, with control and guidance facilitated through the Newton Testware interface [21]. This interface effectively managed crucial testing variables such as frequency rate, load application, and amplitude.…”

“…These findings hold the potential to influence material design and applications, particularly those necessitating heightened durability. Subsequently, Boiko et al [21] encompassed the analysis of empirical data obtained from 3D-printed plastic components. Additionally, an ANN was employed to forecast fracture behavior in the samples.…”