Parametric analysis of part suitability in Additive Manufacturing

Abstract

Additive Manufacturing (AM), with its bottom-up approach enables the fabrication of highly complex geometries, the combination of multiple materials to achieve specific physical properties, and the consolidation of assemblies into single parts. Although many of the prominent processes were invented 20-30 years ago, we are now seeing rapid performance improvement and the synthesis of innovative processes that greatly expand the applications of AM. That being said, there are significant barriers to entry, one being the low throughput of additive processes when compared with more conventional methods, another being the high cost of introducing AM processes into current supply chains. Identifying key use cases where AM can be of maximum benefit is pivotal to exploit its potential. This research aims to provide methodology and tools to enable rapid estimation for part suitability in AM with physics-based and predictive models. A method is presented to estimate build time for parts manufactured via the Selective Laser Sintering process, utilizing specific metrics for part geometry and machine specific parameters. Key elements of the AM cost function are identified using the estimation model, paying close attention to assessing the impact of machine cost on the final part. In summary, these tools can be expanded to allow for not only part selection in AM, but also to act as decision tools when designing equipment..