In this study, environmentally convenient highly metal powder filled feedstocks intended for powder injection molding is presented. The composition of 60 vol % 316L stainless steel gas atomized powder feedstocks containing semicrystalline waxes: acrawax or carnauba wax and paraffin wax, combined with polyethylene glycol and modifier, was optimized to provide defect-free parts. Rheological as well as thermogravimetric analyses supported with scanning electron microscopy and metallography were employed to set up optimum conditions for molding, debinding and sintering. The performance of the novel feedstock was compared with currently available polyolefines-based materials, and results showed an efficiency enhancement due to the substantially lower (about 100 °C) mixing and molding temperatures as well as a reduction of debinding and sintering times at the simultaneous achievement of better mechanical properties in terms of elongation and tensile strength, in comparison to the mass production feedstock.

Owing to the quality issues of highly filled compounds used in metal injection molding, the current research is focused on intercepting flow-induced inhomogeneities in multiphase compounds resulting from the segregation of metal powder particles from (typically) three/four-component polymer binders, resulting in an unacceptable porosity of the final sintered metal parts. A recently developed nondestructive approach for quantifying the extent of these flow-induced defects was employed to study the effect of the size and shape of water- and gas-atomized 17-4PH stainless steel powders on segregation. This method combines scanning electron microscopy/energy dispersive x-ray spectroscopy with an in-house analytical tool. The results show a higher tendency of coarser particles (D50 of 20 μm) for flow-induced defects, while an irregular shape (water-atomized particles) reduces this unwanted phenomenon.

This paper presents the measurement and evaluation of the surfaces of molds produced using additive technologies. This is an emerging trend in mold production. The surfaces of such molds must be treated, usually using laser-based alternative machining methods. Regular evaluation is necessary because of the gradually deteriorating quality of the mold surface. However, owing to the difficulty in scanning the original surface of the injection mold, it is necessary to perform surface replication. Therefore, this study aims to describe the production of surface replicas for in-house developed polymer molds together with the determination of suitable descriptive parameters, the method of comparing variances, and the mean values for the surface evaluation. Overall, this study presents a new summary of the evaluation process of replicas of the surfaces of polymer molds. The nonlinear regression methodology provides the corresponding functional dependencies between the relevant parameters. The statistical significance of a neural network with two hidden layers based on the principle of Rosenblatt’s perceptron has been proposed and verified. Additionally, machine learning was utilized to better compare the original surface and its replica.