Building Bridges to Construct a Better World
Visual Inspection
Predictive Monitoring: Gas Turbines Demo
Also watch:
- Altizon animates a beverage manufacturing and bottling process and overlays their Datonis Digital Factory software.
- AzkoNobel showcases how they remanufacture recycled paint in the United Kingdom.
Acoustic Monitoring
Assembly Line
In Amazon’s Flagship Fulfillment Center, the Machines Run the Show
More than the physical robots, the stars of Amazon’s facilities are the algorithms—sets of computer instructions designed to solve specific problems. Software determines how many items a facility can handle, where each product is supposed to go, how many people are required for the night shift during the holiday rush, and which truck is best positioned to get a stick of deodorant to a customer on time. “We rely on the software to help us make the right decisions,” says Shobe, BFI4’s general manager.
When managers wanted to figure out how many people they needed at each station to keep up with customer orders, they once used Excel and their gut. Then, starting in about 2014, the company flew spreadsheet jockeys from warehouses around the country to Seattle and put them in a conference room with software engineers, who distilled their work and automated it. The resulting AutoFlow program was clunky at first, spitting out recommendations to put half an employee at one station and half an employee at another, recalls David Glick, a former Amazon logistics executive who supervised initial development of the software. Eventually the system learned that humans can’t be split in half.
Artificial intelligence optimally controls your plant
Until now, heating systems have mainly been controlled individually or via a building management system. Building management systems follow a preset temperature profile, meaning they always try to adhere to predefined target temperatures. The temperature in a conference room changes in response to environmental influences like sunlight or the number of people present. Simple (PI or PID) controllers are used to make constant adjustments so that the measured room temperature is as close to the target temperature values as possible.
We believe that the best alternative is learning a control strategy by means of reinforcement learning (RL). Reinforcement learning is a machine learning method that has no explicit (learning) objective. Instead, an “agent” with as complete a knowledge of the system state as possible learns the manipulated variable changes that maximize a “reward” function defined by humans. Using algorithms from reinforcement learning, the agent, meaning the control strategy, can be trained from both current and recorded system data. This requires measurements for the manipulated variable changes that have been carried out, for the (resulting) changes to the system state over time, and for the variables necessary for calculating the reward.
How to Reduce Cycle Times by 70% and more on Your Existing CNCs and Dramatically Improve Tool Life Too
Much has been made of high efficiency milling in recent years, and for good reason. Roughing cycle times can often be reduced by as much as 80% by using solid end mills, small stepovers, faster feed rates and deeper axial depths of cut. The shortcoming has been that, due to part feature obstructions or CAM system limitations, the cutting technique can often only be used in certain areas of a part so that total part cycle time reduction ends up being much more modest.
Dr. Somekh says iMachining applies a much more flexible approach with the patented ability to dynamically vary the tool cutting angle (which refers to the degree of radial engagement of the tool with the material) and the feed rate in order to maintain a constant chip thickness and load on the cutting tool. The dynamic feed rate adjustment algorithm supports material cutting angles from 10 to 80 degrees of tool engagement. Constant load and chip thickness is key to the success of iMachining, also with very small cutters and machining in hard or highly abrasive materials.
A pressing case for predictive analytics at MacLean-Fogg
Metform chose to focus specifically on the AMP50XL’s drive train because “that was the area where we saw the biggest opportunity for improvement.” While they’d previously been gathering data from the machine for predictive-maintenance use, the old process was neither efficient nor of adequate detail, they realized. “From a data collection standpoint, there was a lot of spreadsheets, a lot of handwritten notes, a lot of tribal knowledge,” Delk said. “We wanted to make sure we could gather that information and put it into context as we were analyzing the equipment.”
“We’re able to monitor the machine health, see in real time how the machine is doing and see a signal of a problem before it becomes a major problem. We have a long way to go in terms of learning how to better use the system and gain further confidence in the system, but at this point, I’m really pleased with the progress we made. I’m anxious to expand this to the other nine Hatebur presses.”
2021 Assembly Plant of the Year: GKN Drives Transformation With New Culture, Processes and Tools
All-wheel drive (AWD) technology has taken the automotive world by storm in recent years, because of its ability to effectively transfer power to the ground. Today, many sport utility vehicles use AWD for better acceleration, performance, safety and traction in all kinds of driving conditions. GKN’s state-of-the-art ePowertrain assembly plant in Newton, NC, supplies AWD systems to BMW, Ford, General Motors and Stellantis facilities in North America and internationally. The 505,000-square-foot facility operates multiple assembly lines that mass-produce more than 1.5 million units annually.
“Areas of improvement include a first-time-through tracking dashboard tailored to each individual line and shift that tracks each individual failure mode,” says Tim Nash, director of manufacturing engineering. “We use this tool to monitor improvements and progress on a daily basis.
“Overhaul of process control limits has been one of our biggest achievements,” claims Nash. “By setting tighter limits for assembly operations such as pressing and screwdriving, we are able to detect and reject defective units in station vs. a downstream test operation. This saves both time and scrap related to further assembly of the defective unit.”
“When we started on our turnaround journey, our not-right-first-time rate was about 26 percent,” adds Smith. “Today, it averages around 6 percent. A few years ago, cost of non-quality was roughly $23 million annually vs. less than $3 million today.”
U.S. Army’s New Expeditionary 3D Concrete Printer Can Go Anywhere, Build Anything
The U.S. Army Corps of Engineers’ Automated Construction of Expeditionary Structures (ACES) program is a game changer for construction in remote areas. The project will supply rugged 3D concrete printers that can go anywhere and print (almost) anything. The project started several years ago when concrete printers were very much in their infancy, but even then it was obvious that commercial products would not fit the Army’s needs.
ACES has produced multiple printers working with different industry partners. For example, ACES Lite was made in partnership with Caterpillar under a Cooperative Research and Development Agreement. It packs into a standard 20-foot shipping container and can be set-up or taken down in 45 minutes, has built-in jacks for quick leveling and can be calibrated in a matter of seconds, making it more straightforward than other devices. Overall the printer resembles a gantry crane, with a concrete pump, hose and a robotic nozzle which lays down precise layers.
The new technology is not magic, as 3D-printed construction is still construction. It does not do everything. A printed building still requires a roof and finishing touches like any other construction work. In areas with good logistics where equipment, labor and materials are all plentiful, there may be little advantage to the ACES approach. But in expeditionary environments, where all these things are likely to be in short supply, ACES could make a real difference.
Additive for Aerospace: Welcome to the New Frontier
Gao, a tech fellow and AM technical lead at Aerojet Rocketdyne, is particularly interested in the 3D printing of heat-resistant superalloys (HRSAs) and a special group of elements known as refractory metals. The first of these enjoy broad use in gas turbines and rocket engines, but it’s the latter that offers the greatest potential for changing the speed and manner in which humans propel aircraft, spacecraft, and weaponry from Point A to Point B.
“When you print these materials, they typically become both stronger and harder than their wrought or forged equivalents,” he said. “The laser promotes the creation of a supersaturated solid solution with fantastic properties, ones that cannot be achieved otherwise. When you combine this with AM’s ability to generate shapes that were previously impossible to manufacture, it presents some very exciting possibilities for the aerospace industry.”
Eric Barnes, a fellow of advanced and additive manufacturing at Northrop Grumman, says “Northrop Grumman and its customers are now in a position to more readily adopt additive manufacturing and prepare to enter that plateau of productivity because we have spent the past few years collecting the required data and generating the statistical information needed to ensure long term use of additive manufacturing in an aeronautical environment… In the future, you may be able to eliminate NDT completely. Comprehensive build data will also serve to reduce qualification timelines, and if you’re able to understand all that’s going on inside the build chamber in real-time, machine learning and AI systems might be able to adjust process parameters such that you never have a bad part.”
Surge Demand
Samsung finds a way to get to 2nm chips before TSMC. DeepMind introduces a new benchmark for vision-based robotic manipulation. Energy prices stymie ArcelorMittal production in Europe, while Tesla waits out opposition for their gigafactory in Germany. Over 10,000 John Deere UAW workers go on strike. General Motors’ is lighting up a battery development lab at their technical center in Detroit.