We have developed a solution based on AWS services which creates a closed loop between designing parts and validating its efficient producibility in the cloud. This is demonstrated with a simplified polishing process for a car door.
The workflow begins with a designer making changes to the part they want to produce in a CAD software. Once they are happy with this version of their part, they commit these changes to a change tracking system along with a set of parameters required for the production of the part. In the demonstration these parameters include polishing force range, pattern options and the robot cells on which the part could be processed. In a real-world scenario, the design data and production parameters could come from two different people or the design data could even come directly from a customer with little to no knowledge of the production process.
Once the design and parameters have been pushed, to an Amazon S3 bucket and DynamoDB respectively, the client calls an API to start the validation of the process. This triggers an AWS Step Function which creates a batch of parameter configurations which need to be tested. In turn, this launches a Fargate task for each configuration which pulls a Container Image loaded with the HAL Robotics Framework and some custom code to generate a toolpath from the incoming parameters and geometric part data.
This Image generates a toolpath for a specific robot cell and solves the toolpath to ensure feasibility, identify any potential issues and calculate a number of metrics, e.g. duration or energy usage, which can be used to score different valid options. These metrics are then pushed to the DynamoDB alongside the parameters used, any errors, and a simulation of the process in the digital twin.
The user can log in to a portal to view their validation runs, drill into the metrics and download the simulation which can be viewed in an interactive 3D viewer on their computer.
Through this workflow the designer has gone from making design changes in their software of choice, all the way to ensuring the producibility of their new design and knowing how it can be optimally processed without ever needing to touch a line of robot code, manually tweak parameters or wait for any simulations to run.