It is necessary to reduce the weight of components while maintaining or improving their mechanical properties to withstand dynamic loads in lightweight structures. In this study, heat treatment and a trigger mechanism were implemented for a thin-walled tube of aluminium to increase energy absorption while reducing the peak crushing force. Different geometries and locations were proposed to trigger deformation in a controlled manner, in combination with heat treatments. Experimental designs for each energy absorption mechanism were performed, and designs were tested by quasi-static crushing. Data obtained from experiments were used to calculate energy absorption indicators that were used to compared designs with components without mechanism to analyse performance. By comparing proposed designs with tubes without modification, the best combination of design variables for each trigger mechanism were identified. It was determined that 160 mm from the upper side, 250 mm${}^2$ area and a rectangular trigger shape reduced peak crushing force by 22.03% and increased energy absorption by 37.76%. For heat treatment, the optimal combination was heating in a furnace at 175 ${}^{\circ }$C for 1 h and cooling in water at 70 ${}^{\circ }$C during 10 min while only soaking half of its length. This combination reduced peak crushing force by 19.02% and increased energy absorption 15.08%. When these mechanisms were combined on a single tube, peak crushing force was reduced by 21.63%, and energy absorption increased by 42.53%.

Injection molding is a high-volume manufacturing process widely used for producing polymer components; however, its process parameters strongly influence residual stress, warpage, and the resulting mechanical performance. This work presents a comprehensive factorial design and ANOVA to evaluate the simultaneous effects of the injection temperature, packing pressure, packing time, and specimen orientation on the warpage, hardness, tensile, and flexural properties of polypropylene plates. The results demonstrate that the injection temperature and packing pressure are the dominant factors affecting the hardness and ultimate tensile strength, whereas warpage is mainly governed by the injection temperature and orientation. Under the tested conditions, certain combinations of injection temperature and packing pressure led to an improved mechanical performance; however, these adjustments also produced reductions in other properties, indicating that the balance among parameters depends on the targeted application rather than a single optimal set. Conversely, the parameter combination that produced the lowest warpage still yielded a significant increase in $E_{sec}$, indicating that reducing the warpage does not necessarily compromise the tensile stiffness. Interestingly, variations in the stress distribution between the tensile and bending tests suggest that the solidification-induced structure of the material influences its mechanical response, with specimens that showed a lower tensile strength exhibiting a comparatively higher resistance under bending. These findings provide new insights into the trade-offs between dimensional accuracy and mechanical performance and offer practical guidelines for optimizing polypropylene injection molding processes.

Predicting the fatigue behavior of injection-moulded short glass-fiber-reinforced thermoplastics is hindered by the large, irreducible scatter of fatigue data and by the strong dependence of the response on the moulding conditions, which conventional point-estimate models neither reproduce nor quantify. This study presents a physics-informed heteroscedastic Gaussian process regression model (Pi-HGPR) that reconstructs the S–N curve of a given processing condition by predicting the fatigue stress amplitude as a function of the number of cycles, the injection temperature, the packing pressure, the specimen orientation, and the stress ratio, and that returns a $95\%$ prediction band rather than a single value. The model combines a damage process-type physical mean, an anisotropic Matérn 5/2 covariance with Automatic Relevance Determination, and a local dispersion filter coupled to the process likelihood, which makes the noise level input-dependent. Trained on S–N curves compiled from the literature and assessed under two out-of-fold validation protocols (point-wise interpolation and leave-condition-out extrapolation), the model reaches a coefficient of determination of $R^2=0.839$ in interpolation and $R^2=0.649$ in leave-condition-out extrapolation, outperforming Random Forest and gradient-boosting baselines in both regimes, with a global empirical coverage of the prediction band of $92.9\%$ and $86.2\%$, respectively, slightly below its nominal value and lower (76–$81\%$) in sparsely sampled regions of the processing space. Automatic Relevance Determination identifies the packing pressure and the injection temperature as the most influential inputs, ahead of the stress ratio and the specimen orientation. Framing the task as predicting the stress amplitude along the low-scatter direction of the S–N field, and incorporating an input-dependent uncertainty, yields an accurate model within the tested processing window and provides reliable extrapolation limits governed by experimental data coverage rather than the learner.

The mechanical properties of fiber-reinforced polymer matrix composites are largely influenced by the characteristics of their constituents, including the matrix and fibers. During injection molding, various process parameters such as thermal and pressure variations play a critical role in shaping the final properties of the molded parts. This study investigates the impact of key process parameters controlling the solidification of glass fiber-reinforced polypropylene (PPGF) plates, including injection temperature and packing pressure, on warpage, hardness, tensile strength, and flexural properties. To the best of the authors’ knowledge, studies on the characterization of changes induced in flexural properties by modifications to injection molding process parameters in fiber-reinforced polymers are limited. Through a statistical analysis, the relationship between process parameters and the contribution of fibers to the composite’s mechanical performance is assessed by comparing the properties of reinforced and unreinforced materials. While the addition of fibers tends to increase warpage, optimal combinations of process parameters significantly reduce warpage while enhancing mechanical properties such as tensile and flexural moduli. These findings offer valuable insights into optimizing injection molding conditions to minimize warpage and improve mechanical properties.