Powder Injection Molding (PIM) for Low Cost Manufacturing of Intricate Parts to Net-Shape
Abstract : Powder Injection Molding (PIM) is a low cost manufacturing process that produces very complex parts to net-shape in a wide variety of materials and unique alloys, including superalloys, stainless steels and carbides, resulting in minimal secondary and assembly operations. PIM offers significant cost savings, increased design and materials flexibility, increased possibility of miniaturization, high mechanical properties, good surface finish and high speed production. The activities and expertise in powder metallurgy as well as in process numerical modeling related to powder injection molding at the Industrial Materials Institute of the National Research Council of Canada (NRC-IMI) and at Maetta Sciences, a company that has research and development facilities at the NRC-IMI, will be presented. Selected solutions and examples realized by NRC-IMI and by Maetta Sciences using respectively their high pressure and their scalable PIM platforms will be presented and described. Potential applications for the military, transportation and aerospace sectors will be highlighted.
Evolution of properties of parts during MIM and sintering of recycled oxide particles
ABSTRACT Metal injection moulding (MIM) is an established process for high volume production of complex shaped metallic parts using commercially available feedstocks. The characteristics of parts after moulding, debinding, and sintering cannot be simply predictable from raw materials because the properties get altered with the process parameters and the corresponding levels of porosity during processing steps. In this study, physical properties, microstructure, and mechanical properties of the MIM parts have been characterised to understand the evolution of strength during various steps in MIM processing. Feedstocks with different binder loading show a considerable difference in physical as well as mechanical characteristics. During sintering of parts which have solid loading of grinding sludge, simultaneous in situ reduction and densification takes place, whereas only densification occurs in carbonyl iron parts. It is, therefore, possible to make complex shaped parts of different levels of porosity from downgraded shop floor metallic waste.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.