3D Software

Simufact launches metal binder jetting simulation tool for Simufact Additive

Simulation software developer Simufact has announced the launch of a simulation tool for the metal binder jetting 3D printing process for its Simufact Additive program.

Users of the software will be able to predict and prevent – at the design phase – the distortion effects that sintering, often used in post-processing, may have on binder jetted parts. The new functionality is ultimately intended to allow manufacturers to achieve industrial-grade part qualities on their existing machines.

Dr. Gabriel McBain, Senior Director of Product Management at Simufact, states: “We are pleased to introduce the first solution for simulating the metal binder jetting sintering process to the market so that manufacturers can take advantage of this important new method. We know customers see metal binder jetting as a pivotal technology for manufacturing, particularly where there’s a need to produce intricate parts at high volumes like the automotive industry.”

The three phases of geometric optimization in the Simufact Additive program. Image via Simufact
The three phases of geometric optimization in the Simufact Additive program. Image via Simufact.

How does metal binder jetting work?

Metal binder jetting is one of the fastest and most sustainable additive processes available. It works by spraying precise volumes of a liquid binding agent onto a bed of metal powder, which in turn binds the powder into solid layers. These layers are printed on top of each other, one at a time, to produce the desired 3D model.

Binder jetting systems tend to have large build volumes and don’t require support structures, so they often have higher production volume capabilities than their powder bed fusion counterparts. It’s for this reason that Simufact believes the technology can completely replace low-volume, high-cost metal injection molding for a wide variety of applications in automotive, medical, and even aerospace.

However, parts fresh out of the build chamber usually require extensive post-processing as they are in their ‘green state’, meaning they are especially brittle and fragile. Sintering can be used as a post-processing step to increase the density of these parts, and improve their mechanical properties. The issue: sintering can result in part shrinkage by as much as 35% – this distortion needs to be accounted for right at the design phase with scaled up dimensions.

Simufact Additive

Intended as an alternative to the conventional but costly trial-and-error approach, Simufact Additive’s new binder jetting tool aims to predict the resultant shrinkage computationally. Manufacturers will then be able to compensate and scale up their dimensions as required without any expertise in thermal simulations.

Additionally, the tool also predicts sintering-induced mechanical stresses in the part, giving an indication as to where defects may occur. With this knowledge at hand, engineers should be able to make design changes earlier in the product life cycle, leading to fewer failed attempts.

As the cherry on top, the model setup and simulation stages are completely automated and can be further customized with Python scripts. As a final validation stage for the compensation, the optimized geometries can be immediately compared to both the initial STLs and a metrology scan of a manufactured part within the UI of the program.

The translucent grey outline shows the degree of shrinkage expected in the sintering process. Image via Simufact.
The translucent grey outline shows the degree of shrinkage expected in the sintering process. Image via Simufact.

Predictive and simulation tools can be extremely useful in reducing costs and shortening the time to market for novel parts. Engineering firm Etteplan recently developed a new, free online tool to calculate the production costs of 3D printing – AMOTool. The software program enables companies to computationally calculate the costs associated with manufacturing an object using a well-established metal 3D printing method, before any production has even taken place.

Taking a deep learning approach, geometric ML specialist Physna recently launched what it is calling the world’s most powerful geometric search engine – Thangs. Instead of scanning for text or images, Thangs uses deep learning algorithms to index 3D models based on the polygons, or triangles, that make up their volumes. The engine also provides version control functionality and ‘compatible part predictions’ for the 3D community.

The 4th annual 3D Printing Industry Awards are coming up in November 2020 and we need a trophy. To be in with a chance of winning a brand new Craftbot Flow IDEX XL 3D printer, enter the MyMiniFactory trophy design competition here. We’re happy to accept submissions until the 30th of September 2020.

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Featured image shows the three phases of geometric optimization in the Simufact Additive program. Image via Simufact.