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How to Automate Robotics Processes in Record Time

Watch the recording

Resource Center Home / Webinars / How to Automate Robotics Processes in Record Time

If you've ever programmed a robot, CNC, or pick-and-place machine, you've experienced them doing exactly what you told them to, but not what you wanted them to do. The current state of automating these machines is similar to programming in assembly language. While it's better than bare metal machine code, the improvement is minimal.

Join us to walk away with

How to avoid common missteps when automating at small scale
How to reduce the fixed costs of automating processes
How to use tools of mass-production at any scale
Factor hardware into the overall automation process.

Hosted by

David Mayer

Senior Technical Project Manager, Fictiv

David Mayer has been on the Operations Team here at Fictiv for the past 2 years. As a Senior Technical Project Manager, he focuses on managing our production of CNC machined parts in the US. He holds a Bachelor's in Mechanical Engineering from Case Western Reserve University. As an engineer, he loves to take things apart to see what makes them tick, and then use the pieces to make something that ticks faster, longer, and louder.

Scott Menor

Co-founder, CTO, SilSync

SilSync is an award-winning hardware engineering team that specializes in getting your product to market fast. Scott's mastery of engineering, physics, mathematics, and software helps SilSync achieve the impossible for customers. Prior to SilSync, Scott worked as a research professor in ASU's High Performance Computing Lab. Scott has a masters in Microbiology from the University of Hawaii at Manoa. His masters' research involved applying nonlinear optimization techniques to cell culture conditions. At the same time, he developed a distributed, semi-synthetic environment for simulating and testing underwater robots at the University of Hawaii's Autonomous Systems Lab. Scott has a Ph.D. in Physics from Arizona State University. His graduate research involved developing a scalable system for inferring a hierarchy of mechanical structure from statistical fluctuations in observed motion.