Modern Machine Shop and MoldMaking Technology present ADDITIVE MANUFACTURING, a quarterly supplement reporting on the use of additive processes to manufacture functional parts. More at additivemanufacturinginsight.com.
Issue link: https://am.epubxp.com/i/497467
AdditiveManufacturingInsight.com May 2015 — 19 Additive manufacturing (AM) has seen vast improvements in its potential ability to serve more and more industrial customers. However, there remain challenges in the technology's ecosystem to identify, map and use processing attributes to ensure consis- tent, predictable part features are produced across many material/process combinations. Being able to better predict actual AM performance is a necessity for increased afordability and subsequently for increased industrial acceptance. Dr. Brent Stucker, CEO of 3DSIM, shared recent advances and discussed the diferentia- tion of algorithmic-based approaches to solving some of the metals-based processing challenges. 1 Background While fnite element models (FEM) have met reasonable expectations by accurately defning energy interactions with discrete materials, FEMs have been taxed by the additive manufacturing community to do so in a more dynamic environment. Tere are two key inhibitors for a successful FEM result of a complex process such as additive manufacturing: 1) cost (of high-performance computing) and 2) time (of processing algorithms). During the preprocessing stage (where FEM occurs), three distinct steps are necessary for metal additive manufacturing. Te frst is to computationally repre- sent the scan patterns. Te second is to capture material data focusing on important thermal variables such as the heat energy absorption coefcients (i.e., laser absorptivity), density of diferent states of matter at diferent temperatures and thermal conductivity. Tese variables, particularly for a powder bed, are typically unknown and require experimental means to collect data. Other variables of interest 2 are typically obtained from literature values. Te third step is to generate a multi-scale mesh which accurately fts the physics of the problem 3 (mesh transitions from a fne to coarse mesh domain in X, Y and Z directions to ft the thermal gradients of the problem) such that the macroscopic domains and the dynamic energy sources are fully captured. Area of Interest For dynamically changing multi-scale meshes in FEM, calculating mesh changes and thermal evolution represent the lion's share of the computer prowess that is needed. Te major objective here is to correlate the energy source, raw material characteristics and geometry with the thermal history and cooling rates which in turn leads to predictions of melt pool dimensions, defects, residual stresses and distortions. Together, process and performance modeling of AM structures could help reduce the number of fabrication, metallurgical characterization and "standardized shape" mechanical testing experiments typically used for quali- fying AM for a particular application. Figure 1 – Plastic strain distribution at 10 percent total average strain for the stress/strain curves from: (a) 3DSIM simulations and (b) ANSYS anisotropic multilinear continuum plasticity model. Predicting Performance with Multi-scale Simulation Architecture By Tim Shinbara, VP of Manufacturing Technology, AMT–Te Association For Manufacturing Technology (a) (b)