Surface structure analysis of injection molded highly filled polymer melts

Abstract

Material components' separation limits to a great extent the efficiency of injection molding of highly filled polymer melts. An uneven redistribution of particle filler within a polymer melt is evaluated with the specially developed testing mold allowing observing the progress of separation during injection molding. Then, scanning electron microscopy of the particular areas derived from the testing mold combined with energy dispersive X‐ray analysis of the distribution of elements typical for filler and analytical approach are employed to derive a quantitative parameter describing the tendency of highly filled feedstocks to the separation. POLYM. COMPOS., 34:1553–1558, 2013. © 2013 Society of Plastics Engineers

Surface adhesion between ceramic injection molding feedstocks and processing tools

“…Recently, we have proposed a method to quantify the separation via SEM/EDX combined with the analytical tool [2] as well as the rheological model suitable for ceramic feedstocks [3]. Further, we have pointed out that wall slip could be considered as a rheological parameter indicating powder-binder separation absence/occurrence [4,5].…”

Online Rheometry Investigation of Flow/Slip Behavior of Powder Injection Molding Feedstocks

“…For PIM feedstocks, Thornagel [13] assumes that particles present in the regions of high shear rate gradients try to leave these areas and concentrate in the middle of the flow channel, which results in so-called powder/binder separation. [14,15].…”

Development of 17-4PH stainless steel bimodal powder injection molding feedstock with the help of interparticle spacing/lubricating liquid concept

“…Currently, the investigation of the separation phenomenon is focused mainly on prediction of its onset and development during mold filling, which requires consideration of relevant processing factors and conditions and finding the method of quantitative determination of separation severity [6][7][8]. Further, Dihoru et al [9] used a combination of capillary and torque rheometers, and developed neural network model to predict the maximum solid loading without powder-binder separation.…”