Rob Spiegel, from Design News, highlights some of the varied technologies that are expandind under the umbrella of advanced manufacturing.
Smart Manufacturing by Any Name
What’s in a name? The nomenclature for manufacturing technology comes in many colors: advanced manufacturing, smart factories, the digital plant, Industry 4.0. The term Industry 4.0 comes from a German government program announced in 2011 that is designed to encourage manufacturers to digitize manufacturing in order to improve Germany’s global competitiveness. In 2015, China launched “Made in China 2025,” an effort to advance its manufacturing technology, again for competitiveness.
In the US, smart manufacturing is viewed as a collection of emerging technologies based on digital communication and high-powered computer processing. Software is the key.
The Big Promise of Big Data
Big Data is the term given to data processing that takes advantage of high processing speeds and inexpensive memory. With increased processing speeds, simulation and analysis that previously took days has been reduced to hours, even minutes. Analytic challenges that were not possible just a few years ago – or not affordable – have become inexpensive and quick.
As applied to manufacturing, Big Data makes it possible for control engineers to move from standard preventive maintenance – much like automobile maintenance that’s done on a mileage schedule – to predictive maintenance, where the maintenance schedule is based on real-time data collection and analysis. The savings comes from doing maintenance when the equipment actually needs work rather than adhering to a per-determined schedule. The Big Data monitoring also detects and alerts to potential machine malfunction before the machine breaks down.
Maintenance is the low-hanging fruit of Big Data. Other functions include determining the manufacturability of products during the design process, as well as altering the product to accommodate the specific manufacturing equipment. Big Data is also used to analyze and adjust production in order to speed the process, reduce inefficiencies, improve throughput, reduce scrap and defects, and drive down energy consumption.
The Futuristic World of Advanced Robotics
Developments in robotics have been stunning in recent years, including applications in surgery, health care support, agriculture, military robots that swarm, and robots that detect and deactivate bombs. Recently ABB programmed a robot to conduct a symphony orchestra. Yet manufacturing was the birthplace of modern robotics applications, particularly in automotive. Robots on the car line took over welding and painting decades ago.
Robot applications in manufacturing have matured substantially since the turn of the century. Aided by processing intelligence and sometimes internet connectivity, manufacturing robots have moved into specialized roles such as food processing – where the robots are built to withstand wash-down – and inspection where the robotic eye can detect cracks that can’t be seen by human vision. In packaging, robots have taken over most functions.
Grippers have evolved, as well, becoming their own specialized mini-industry. You can find grippers that lift heavy weights or use suction to move a windshield. You can also swap those for robotic hands that can lift a delicate orchard or move an egg.
The Friendly World of Collaborative Robots
The collaborative robot – sometimes called a “cobot” – is the steel within the velvet glove. The robot can help you lift, but it won’t hurt you if you bump into it. These robots are designed to work side-by-side with humans on the assembly line. These friendly robots were invented in 1996 by J. Edward Colgate and Michael Peshkin, professors at Northwestern University. A 1997 US patent filing describes them as “an apparatus and method for direct physical interaction between a person and a general-purpose manipulator controlled by a computer.”
While assembly is the collaborative robot’s forte, they’ve been deployed for lifting, moving heavy objects, and recently, to conduct a symphony orchestra, which ABB’s YuMi did over the summer. These robots are produced by traditional robot companies such as ABB and companies such as Rethink Robotics that were launched specifically to build and market collaborative robots.
The Virtual Factory: The Plant on a Computer Screen
In the past, factory lines were set up by moving equipment around until it was arranged in an optimal manner to support manufacturing. Some plant managers used forethought before moving heavy equipment, but basically, it was a bit of trial and error to get things right. The virtual factory occurs in a computer model that simulates the optimal configuration – including everything from wiring to stampers, robots and, electrical boxes. The layout is completed before any physical equipment is touched. The idea is to use simulation to gain the just-right configuration.
As well as creating a simulated configuration of an individual production line, the virtual plant is also portable. A manufacturer can take an optimized plant configuration and use it for plants across the globe. A well-configured plant simulation can also be used as a teaching tool. Ford and Siemens created a virtual model of a highly optimized plant that showed detail down to individual work stations. Users across the globe are able to use to model to teach workers how to efficiently run the work station.
IoT Connectivity: The World of Connected Devices
IoT connectivity on the factory floor went from zero to sixty at lightning speed. One very simple advantage of internet-connected devices – such as sensors on plant equipment – is the ability to move beyond wiring. Hard-to-get-to sensors were suddenly easy to connect. Another major benefit of plant IoT is the ability to gather plant data into one dashboard that can be read from anywhere via a browser.
While the overall benefits of IoT are still nascent in wearables, healthcare, and buildings, the IoT delivered nearly instant return on investment to manufacturing. IDC estimated that the 2016 global spend on IoT by manufacturers was $102 billion.
Use of IoT by manufacturers includes monitoring of plant assets for maintenance (catching ill machines before they fail), monitoring all plant operations for efficiency and optimization, and collecting field-service data. Some automakers are monitoring products after they reach the consumer, gathering data to determine consumer use and to track product problems.
Machine Learning: Machines Teaching Machines
Machine learning in manufacturing a form of artificial intelligence (AI). Computers are using algorithms to monitor and optimize plant operations. Forms of AI can be deployed to measure plant efficiency and make adjustments to optimize operations. This can include everything from energy use to energy usage to production speeds and maintenance schedules.
Machine learning came into play as tremendous amounts of data became available from plant operations. Plant managers have started to use that information to analyze all aspects of plant operations, including how machines can best work together.
Data from the plant can also be deployed to the product design team so product engineers can alter designs to optimize the manufacturing process. Computers can figure out the best match between design and plant equipment to get the optimal result. This includes letting the computer help design the product and allowing the software to plot the manufacturing process.
M2M Connectivity: The Fully Connected Plant
M2M (machine-to-machine) connectivity can be deployed either by wire, through a wireless local network, or via the internet. The link from machine to machine enables a sensor or meter to communicate the data it records – such as temperature or inventory level – to application software that can use it to make appropriate adjustments.
In manufacturing, the M2M vision is a fully connected plant, from the product design team, to suppliers, to the plant operations, to maintenance, to finance, to the customer. Any of these teams can look into the plant’s operations to see production status, inventory, as well as finished products and shipping status.
M2M began life in the hard-wired world, where the connections from plant to enterprise traveled along cables. The IoT made the process less onerous and less expensive while also allowing a greater number of stakeholders to view plant data via browser. The downside of the IoT connectivity is greater security vulnerabilities.
Additive Manufacturing: 3D Printing on the Production Floor
Additive manufacturing – also known as 3D printing – is having an effect on plant operations. For decades, the technology was primarily used to create prototypes. The 3D-printing process was simply too slow for production. Plus, the products were typically weaker than those created by traditional manufacturing processes such as machining, casting and plastic molding.
That has changed somewhat as 3D-printing technology and materials have advanced. The 3D-printing process is getting faster, and materials are now available that render products as strong as traditional production methods. These advances have allowed for some low-volume manufacturing applications that use additive manufacturing as part of production. One advantage of additive manufacturing is the ability to create customized products.
The Digital Twin: Virtual Products in Virtual Production
Large manufacturing technology companies such as GE and Siemens have been talking about the Digital Twin for a few years. The concept is that each physical product has a collection of data to accompany it. Given the low cost of memory and processing, the kind of data that can be collected into the digital twin is vast. The digital twin can include design data that stretches from early sketches to fully formed iterations. Collected with the design data is simulation results on materials as well as finished-product test simulations.
The digital twin includes the bill of materials (from supplier to costs), the manufacturing configuration, and production data. Rounding out the content is sales records and field information on how the product is used by the consumer as well as maintenance and defect records on the products through its end-of-life.
Cyber/Physical Systems: The Blend of Real and Digital
Cyber-Physical Systems (CPS) include the integration of data computation, networking, and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa.
In manufacturing, the CPS brings together asset management, configurability, and productivity analysis. CPS in manufacturing provides data to keep equipment users aware of networked asset status as well as alerts for possible risks that can include asset stress indications or security breaches. These systems combine engineering and computation to provide analysis of the manufacturing process, paths toward optimization and efficiency, and data on equipment health.
>> Learn more by Rob Spiegel, Design News, September 18, 2017