Despite Fused Deposition Modeling (FDM) being an economical 3D printing method known for its material versatility and ease of use, the mechanical performance of FDM-produced components is significantly influenced by process parameter settings. This study investigated the effects of the layer thickness, raster angle, build orientation, and extrusion temperature on the ultimate tensile strength (UTS) and elastic modulus of Polylactic Acid (PLA) specimens using Taguchi methods, with significance analyzed through analysis of variance (ANOVA). The results indicated that the build orientation is the primary factor affecting both the UTS and elastic modulus, with a flat orientation yielding the best performance. ANOVA showed that the build orientation, raster angle, and extrusion temperature significantly influence the UTS, with the build orientation contributing 98.16%. For the elastic modulus, the build orientation and raster angle were significant, contributing 94.83% and 1.76%, respectively. The optimal parameters were a 0.16 mm layer thickness, flat build orientation, 30°/−60° raster angle, and 200 °C extrusion temperature, resulting in predicted UTS and elastic modulus values with error percentages of 4.33% and 2.74%, respectively, compared to experimental values. The regression model demonstrated high predictive accuracy, with R-squared values of 99.71% for the UTS and 99.52% for the elastic modulus.

This paper introduces an innovative approach that enables the automated and precise prediction of steel’s chemical composition based on the desired Jominy curve. The microstructure, and in fact the presence of martensite, is decisive for the hardness of the steel, so the study considered the occurrence of this phase at particular distances from the quenched end of the Jominy sample. Steels for quenching and tempering and case hardening were investigated. With the representative collected dataset of hardness values from the quenched end of the Jominy specimen, microstructure and chemical composition of steels, the complex regression model was made using supervised artificial neural networks. The balance between cost and required hardenability can be achieved through optimizing the chemical composition of steel. This model of designing steel with required hardenability can be of great benefit in the mechanical engineering and manufacturing industry. The model is verified experimentally.

This paper introduces an innovative approach that enables automated and precise prediction of the steel's chemical composition based on the desired Jominy curve. The microstructure, and in fact the presence of martensite, is decisive for the hardness of the steel, so the study considered the occurrence of this phase at particular distances from the quenched-end of the Jominy sample. Steels for quenching and tempering and case hardening were investigated. With representative collected dataset of hardness values from quenched-end of Jominy specimen, microstructure and chemical composition of steels, the complex regression model was made using supervised artificial neural networks. The balance between cost and required hardenability can be achieved through optimizing the chemical composition of steel. This model of designing steel with required hardenability can be of great benefit in the mechanical engineering and manufacturing industry. The model is verified experimentally.