“…Thermal conduction between the roads and layers plays a significant role in contributing to the bond strength of the printed sample [15,30]. Furthermore, the selection of processing parameters greatly influences the thermal profile of a printed sample and can have an adverse effect on part distortion [24,31,32]. Therefore in this study, the thermo-mechanical properties are expressed as a function of temperature (T) and also the physics incorporated in the model are coupled with respect to the evolving temperature of the system [33,34].…”
Section: Heat Transfer Studymentioning
confidence: 99%
“…To understand and derive a relationship between the residual stress and warpage after completion of printing and cooling down of samples to ambient temperature, the final residual stress and overall warpage values of the samples printed under various printing conditions [32,61,63] were evaluated. The data is plotted in Figure 8.…”
Section: Relationship Between Residual Stresses and Warpagementioning
“…Thermal conduction between the roads and layers plays a significant role in contributing to the bond strength of the printed sample [15,30]. Furthermore, the selection of processing parameters greatly influences the thermal profile of a printed sample and can have an adverse effect on part distortion [24,31,32]. Therefore in this study, the thermo-mechanical properties are expressed as a function of temperature (T) and also the physics incorporated in the model are coupled with respect to the evolving temperature of the system [33,34].…”
Section: Heat Transfer Studymentioning
confidence: 99%
“…To understand and derive a relationship between the residual stress and warpage after completion of printing and cooling down of samples to ambient temperature, the final residual stress and overall warpage values of the samples printed under various printing conditions [32,61,63] were evaluated. The data is plotted in Figure 8.…”
Section: Relationship Between Residual Stresses and Warpagementioning
“…27,28 However, determining the specific contribution of each phenomenon to overall shrinkage requires a thorough thermomechanical investigation on PA/30 wt% GF composite. 28,29 ANOVA was used on the data in Table 3 to understand better the influence of the researched factors on d f and Shr. The following are the findings of these analyses for d f and Shr.…”
Section: Resultsmentioning
confidence: 99%
“…27,28 However, determining the specific contribution of each phenomenon to overall shrinkage requires a thorough thermomechanical investigation on PA/30 wt% GF composite. 28,29 Figure 4.Scanning electron microscopy images of polyamide/30 wt% GF; the left image shows the whole cross-section and the right one illustrates a close view of the broken GFs. GF: glass fiber.Figure 5.The thermoplastic matrix composite samples after single point incremental forming and shrinkage.…”
The experimental study of single point incremental forming (SPIF) of a thermoplastic matrix composite (TMC) was presented in this research. The TMC’s matrix was built of polyamide (PA) 6.6, and the reinforcement was 30 wt percent short glass fiber (GF). The TMC was manufactured by hot pressing PA/30 wt percent GF granules to a thickness of 1.5 mm. The impact of SPIF process variables such as forming temperature (60°C–120°C), spindle speed (500 rpm–3000 rpm), wall angle (30°–60°), and step size (0.5 mm–1.5 mm) on forming depth and shrinkage owing to cooling were investigated. The findings revealed that the wall angle is the most effective parameter on TMC forming depth and shrinkage. Furthermore, TMC showed a significant percentage of shrinkage when it is cooled (up to 83.6% of shrinkage).
“…The effects of these phenomena manifest as deformation and residual stresses that can cause issues such as delamination. This has motivated the development of physics-based [7][8][9][10] methods to predict the outcome of the printing process. To reduce the time and costly empirical calibration of processing parameters, there has been much interest in developing simulation capabilities to predict the outcomes of a given print geometry or strategy [7,9].…”
We present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The properties are inversely determined using only a few experimental tests. Developing composite manufacturing digital twins for SFRP composite processes, including injection molding and extrusion deposition additive manufacturing (EDAM) requires extensive experimental material characterization. In particular, characterizing the composite mechanical properties is time consuming and therefore, micromechanics models are used to fully identify the elasticity tensor. Hence, the objective of this paper is to infer the fiber orientation and the effective polymer modulus and therefore, identify the elasticity tensor of the composite with minimal experimental tests. To that end, we develop a hierarchical Bayesian model coupled with a micromechanics model to infer the fiber orientation and the polymer elastic modulus simultaneously which we then use to estimate the composite elasticity tensor. We motivate and demonstrate the methodology for the EDAM process but the development is such that it is applicable to other SFRP composites processed via other methods. Our results demonstrate that the approach provides a reliable framework for the inference, with as few as three tensile tests, while accounting for epistemic and aleatory uncertainty. Posterior predictive checks show that the model is able to recreate the experimental data well. The ability of the Bayesian approach to calibrate the material properties and its associated uncertainties, make it a promising tool for enabling a probabilistic predictive framework for composites manufacturing digital twins.
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