3D modelling software developer Riven has announced the launch of a technology that enables the production of to “ten times more accurate” 3D printed parts.
Compatible with FFF, SLA, MJF and binder jetting machines, the new Warp Adapted Model or ‘WAM’ capabilities of the firm’s platform, allow users to capture and use full-part 3D data from an initial design to identify and correct errors in minutes.
The company has also agreed to integrate WAM into Authentise’s AMES software, in a way that will see scan data from Riven’s sensors automatically loaded into the program’s traceability report. In doing so, Authentise says that users will be able to achieve the “full contextual data capture of all parts,” pick out any divergence from Riven’s WAM models and make orders with greater confidence moving forwards.
“Riven’s WAM technology is levelling-up part accuracy across all AM production techniques,” said Authentise CEO Andre Wegner. “I am very excited to see existing AM systems now able to produce production parts and meet tolerance specs that were previously impossible. Authentise customers will see immediate benefits – especially for setting up and managing series-production orders.”
Riven’s ‘3D reality platform’
Riven specializes in developing software and scanning products that optimize the New Production Introduction or ‘NPI’ process. Describing the way manufacturers launch new products as “broken,” the firm says that “the majority of companies miss critical ship dates and suffer cost overruns,” due to antiquated analog data communication, engineering capacity constraints and fragile supply chains.
To get around these issues, Riven has developed a combined data capturing and computation platform, in which parts can more easily be scanned, checked against CAD files and shared across users. Using this approach, the firm claims that adopters are able to automatically check for dimensional defects with extreme precision, down to an accuracy level of 0.002″ and at a repeatability of 0.0004″.
In practise, Riven is still rolling the technology out and working with select groups to help optimize their new product designs, while marketing the platform as a subscription for $4800 per year. However, having worked with several mid-market and Fortune 500 OEMs, as well as service bureaus during the course of the year, the company is now taking its approach a step further, with the introduction of WAM.
Putting ‘WAM’ into practise
According to Riven, WAM has demonstrated the ability to “improve the quality and accuracy of every build” produced across various 3D printers, including those powered by legacy technologies. As opposed to simulation-based approaches to part optimization, the company also says that WAM is more accessible, as it doesn’t require a prior knowledge of machine or material parameters to operate.
When tested with entry-level FFF machines, for instance, the firm found that its software reduced average print errors by nearly a third, and improved part accuracy (where errors were deviations of over 0.25mm) from 80% to 93%.
Whether it’s being deployed by short-run clients with projects that only need a few units, or those that require thousands of components, Riven also sees its technology as being hugely scalable. In fact, the company says it’s currently developing joint solutions with “leading AM equipment and AMES partners,” that it anticipates will open new 3D printing markets in the industrial, aerospace and consumer sectors.
“Riven’s WAM is a unique and powerful capability that enables us to deliver production parts with tighter tolerances and saves weeks by eliminating process iterations,” said Nate Higgins, President of WAM adopter FreeFORM Technologies. “This further demonstrates Riven’s value in speeding up product acceptance and improving the experience of our mid-size and Fortune 500 customers.”
Alongside the launch of WAM, Riven has revealed that it’s developing a new predictive ‘PWAM’ version of the software as well. Now in pre-release testing, the technology is said to be capable of automatically creating pre-adjusted models, enabling it to deliver parts on an even greater scale than its predecessor, and further minimize scrap-induced waste.
Riven’s Authentise partnership
As well as unveiling WAM, Riven has announced the integration of its 3D reality platform into the Authentise Manufacturing Execution System (AMES). In essence, AMES is a data-driven workflow optimization software that’s designed to enable users to create a digital thread of their entire manufacturing process, and monitor the compliance and scheduling of parts produced in real-time.
Over the last year or so, Authentise has added several upgrades to build on its platform’s capabilities, first releasing an AMES powder tracking update in October 2020, then adding nebumind’s digital twin visualization tool a month later.
With the integration of WAM, AMES now feeds extra information around a part’s CAD file, processing and materials directly into Riven’s software, which in turn, can be used to rapidly identify any divergence from desired specifications. As a result, users can expect to deliver more consistent components moving forwards, while in future, WAM compensation is set to be able to automatically trigger new orders.
Using this newly-upgraded version of AMES, Wegner has also said that he expects adopters to be able to “greatly accelerate new product introduction,” while Riven Founder James Page claims that the revised software could be vital in providing OEMs and service bureaus with the confidence, that 3D printing can “deliver production parts quickly that meet specs.”
“Authentise’s open platform and workflow automation are an excellent complement to Riven’s push-button easy capture, analysis and streamlined data aggregation,” concluded Page. “Data lakes based on capture of complete as-manufactured parts, fuel Riven’s next generation predictive ML technology, enabling even higher part quality and reduced time to production deliveries for Authentise customers.”
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Featured image shows a WAM-optimized 3D printable part model on Authentise’s AMES software. Image via Authentise.