An Israeli company has developed new artificial intelligence technology that promises to dramatically change how original equipment manufacturers and their suppliers conduct quality control inspections.
After three years of work, military researchers are near the end of a project to find a faster, cheaper way to make tools for large aerospace parts like skins for wings and fuselages.
The institutes that make up Manufacturing USA need to move at the speed of business, considering that the endeavor represents the U.S. government’s biggest investment in the digitization of manufacturing to date.
Manufacturers who have deployed the digital or smart factory have put down their pencils, found new uses for their clipboards and closed their spreadsheet programs in favor of using real-time data gleaned from condition monitoring of their machinery.
Metrology-grade laser scanners are expanding their range of applications. New users are finding the main attractions of laser scanners—speed and ease of use. What prevented more widespread use in the past were laser scanners’ perceived tradeoffs. Using one usually meant sacrificing accuracy or working with noisy data.
SHANGHAI—The $150 million “factory of the future” that the Swiss innovator ABB announced nearly a year ago is becoming reality in this enormous city’s Pudong New Area.
Information technology and operations technology are unlikely candidates for a successful marriage. But to ensure that manufacturers thrive in the digital age, OT and IT must find ways to work together—or to at least, as on Tinder, swipe right to indicate interest.
I experienced the end of the Third Industrial Revolution as I began my career in manufacturing. Closed government and private networks gave way to an open network called the Internet.
Vollmer of America completed its move into a new 30,000-square-foot facility near Pittsburgh International Airport in June of 2020 and participated in the Las Vegas AWFS Fair in July this year.
In the AI world, fear is driven by unfamiliarity with the process, the professional impact of failure and the daunting tasks of pulling together all of the people and perspectives required just to get started. Here are three key lessons learned from our work with Rolls-Royce and Gulfstream that will clear your AI project for takeoff.
