Improve the accuracy of additive manufacturing part production

Riven, a leader in 3D reality intelligence for digital manufacturing, has developed Warp-Adapted-Model (WAM) capability that enables higher accuracy Additive Manufacturing (AM) part production. WAM uses full-part 3D data from an initial part to identify errors and produces a corrected model in minutes, one that eliminates warp and is up to 10 times more accurate when printed.

WAM capability has been tested extensively and has shown improvement across a variety of additive manufacturing technologies including FFF, SLA, metal binder jetting and MJF. WAM improves parts made by nearly any AM technology or machine and is available to select customers and partners now.

WAM enables users to deliver production parts with tighter tolerances and saves weeks by eliminating process iterations. WAM works for any additive technology without the need for detailed knowledge of the specific machine or material parameters. It is complementary to simulation-based approaches and can be used alone or in combination to correct remaining errors related to environmental conditions or imperfect simulation input.

A typical part printed on a FFF system shows significant deviations from the design. Red areas are oversize, blue areas are undersize and grey areas match the design. A histogram shows error distribution.

In a comprehensive FFF trial, average print errors were reduced by over 2.8 times where the accuracy score improved from 80% to 93% (where errors are defined as areas with deviation over 0.25 mm). Trials were conducted with three different part types and in three different materials.

A warp-adapted-model (WAM) generated with Riven and printed on the same printer shows 10X lower total deviations.

WAM is scalable, making additive manufacturing a viable option for customers with projects that only need a few units to those that require thousands or more. Riven is developing joint solutions with leading AM equipment and AMES partners to open new markets for AM production across industrial, automotive, aerospace and consumer applications.

Riven is also pre-release testing PWAM, a predictive, machine-learning driven version of the technology which creates pre-adjusted models automatically and will deliver even greater economies of scale and minimize production of scrap parts.