Big Data Management in Additive Manufacturing

Big Data Management in Additive Manufacturing

Additive Manufacturing of polymers is transitioning from rapid prototyping to a true industrial production technique. While it brings valuable opportunities to the industry it also comes with a series of challenges for the engineers: the reliability of the mechanical properties of the final part still has some uncertainty and is not fully supported by standard engineering tools. To support this transition, the engineering workflow, which is daily applied for traditional manufacturing processes, needs to be reviewed and adapted to facilitate the introduction of new technologies. A holistic simulation approach for additive manufacturing of plastics and composites is proposed: it allows to use multiscale material modeling techniques, essential to handle the several scales involved in Additive Manufacturing, to predict important effects of the printing process such as warpage, shrinkage and residual stresses. These output can be easily used to simulate the effective mechanical performance of the as-printed part as a function of the material and other important printing process parameters such as toolpath.

Globally, both in physical and virtual cases, to optimize the whole process, it is necessary to track material behavior and the process parameters to fully characterize the as-manufactured parts. For example, in SLS additive manufacturing these parameters include but are not limited to: powder size, powder recycle percentage, machine calibration and specific software version, print speed, and build path. As result, the challenge is to quantify the variability in the process to achieve consistent, and repeatable parts. A system which supports comprehensive data collection, management, and traceability across multiple batch is required to address these challenges. This enables the correlation between the manufacturing process parameters and part performance, which can reveal the key influencers in the variability of the process; in addition, collecting the process data can provide predictive part performance using statistical models. Finally, the data traceability can be used to calibrate and account for variation between two printing machines, to insure quality control.

In recent years, the introduction and the increasing emphasis to process management has enabled the industry to shift towards interactive applications, batch automation, new web-based technologies, and the ability to monitor the full data lifecycle of high-quality corporate data. A system as such sets forth several key benefits in the engineering community. In order to support, comprehensive data collection, management, and traceability, it is necessary that a system be able to:

  • Capture data, including: process information, raw material, machine specifications, inspection data, mechanical testing and characterization
  • Provide controlled access for modification, review, and approval workflow
  • Maintain traceability and pedigree information from the manufacturing process to the tested part
  • Execute/automate processes such as statistical tools and visualization of data
  • Maintain, data across the lifecycle of the part in service
  • Provide critical information to establish inspection cycle planning

All these requirements define the next generation materials process and management system which must be provided as a COTS (Commercial Off-the-Shelf) solution. First, it allows the dissemination of large quantities of physically-tested and virtually-simulated material data. Second, the automation of data capture and analysis of material test data becomes possible throughout the material lifecycle. Third, the definition of workflows and approvals can apply best practices to efficiently manage the flow of business information. Lastly, integrations with commercial or proprietary CAE, CAD, and PLM tools with scalable web-protocols enable intellectual property protection through process control and traceability.

Manage Material Models, Data and Processes with Full Traceability

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