Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

AI for manufacturing: Today and tomorrow

David Rogers (Sight Machine)
11:55am–12:35pm Wednesday, September 20, 2017
Verticals and applications
Location: Yosemite A Level: Non-technical
Secondary topics:  Data science and AI, IoT (including smart cities, manufacturing, smart homes/buildings)
Average rating: ***..
(3.33, 3 ratings)

Prerequisite Knowledge

  • Basic knowledge of artificial intelligence and the IoT
  • Familiarity with manufacturing practices (useful but not required)

What you'll learn

  • Understand how manufacturers in the future will use AI to be more efficient and profitable

Description

Artificial intelligence in manufacturing has been around for a long time, but are you aware of how it can make your operations more efficient and profitable? David Rogers explains how existing technologies like the digital twin approach, advanced decision making, and downtime cause detection have primed manufacturing for a profitable and efficient future.

A digital twin mirrors the entire production process, including machines, lines, and plants, and serves as the foundational layer for enabling advanced analytics and decision making on the factory floor. By adopting a digital twin approach, manufacturers can gather plant floor data and improve operations. With the digital twin in place, the digital thread will become a reality. Manufacturers today are using data to make decisions to improve the manufacturing process, but this can be complicated. AI will make it less so, as systems become more integrated and automated, providing manufacturers with an integrated view (instead of a siloed view) of the asset’s data throughout its lifecycle.

In addition, dynamic tolerance will be simplified. AI will solve one of the fundamental challenges that assembly line manufacturers face, which is determining where the different tolerances in the assembly of the product are and how best to optimize them. At the same time, predictive maintenance will become mainstream. Manufacturers will be able to count on machines to tell them when the best time is to replace a part or when a machine will need maintenance—and the overall costs associated with it; essentially manufacturers will be able to optimize variables to maximize profitability and operational effectiveness beyond just uptime. All of this will require less human interaction. In the future, machines will automatically react to countless factors, such as variation in material supply, that could introduce variability into a process.

Photo of David Rogers

David Rogers

Sight Machine

David Rogers is a data scientist at Sight Machine, where he solves complex manufacturing problems for Global 500 companies with digital twin and AI technologies. His background includes full stack software development and applying system thinking for Boeing and nonprofit organizations. David holds a BS in computer engineering from Michigan State University and an MS in systems engineering from the University of Virginia.