Machinery : Industrial Robot : General
FANUC has consistently pursued the automation of factories since 1956, when it succeeded in the development of the SERVO mechanism for the first time in the Japanese private sector. FANUC’s business is comprised of three pillars of FA, ROBOT and ROBOMACHINE. The FA business encompasses basic technologies, consisting of NCs (numerical controls), servos and lasers, which are also applied to the ROBOT and ROBOMACHINE businesses. In addition, FANUC’s flagship IoT product, “FIELD system,” which is an open platform, has been introduced as a new business. FANUC also offers services, with a policy for not terminating support of FANUC products as long as they are used by customers. Through such activities, FANUC contributes to the development of manufacturing industry in Japan and overseas, by promoting automation and efficiency in customers’ factories.
Labor shortages have forced manufacturers to adopt collaborative technology
Robotic screwdriving differs from more traditional applications, such as fixed or handheld screwdriving. Among other things, robots make it easy to do quick changeovers and run small, varying size batches of related assemblies. In addition, robots can drive screws from all directions without ergonomic concerns and with varying degrees of torque. They also have the ability to drive different sizes of screws using various feeders for each type of fastener. Manufacturers can achieve higher cycles per screwdriver spindle and faster cycle time per screw, while improving quality.
“[Automated] screwdriving used to be a task that was complex, costly and took up a large footprint on the assembly line,” explains Leclerc. “As such, it was reserved for use in vast plants with big automation budgets producing in high volumes. “There are screwdriving systems that can be bought off the shelf, shipped within a few business days, easily installed and adapted to production changes,” claims Leclerc. “It’s a completely new era.”
Precision Parts Manufacturer Dramatically Increases Uptime with FANUC’s IoT Solution
Fast, Easy Six-Axis Robot Integration Created by a Molder for Molders
For Scott and his staff, few tools are more critical to profitability and efficiency than automation, which is why Noble Plastics has Fanuc six-axis robots on all its injection machines. The integration was performed inhouse with the philosophy that, as Rogers puts it, “The robot should be a partner for the operator, not a hindrance.” After 20-plus years of robot integration experience and eight years as an authorized integrator for Fanuc robots, Noble Plastics is now launching a turnkey package of a robot, basic and intuitive user interface, end-of-arm tooling (EOAT)—if desired, integration with the injection machine controls, job-specific programming and operator training. “We can do all this faster and at lower cost than your average integrator,” Rogers says, “and the end result is easier for the operator to use.”
Systems can be delivered in as little as 2 to 4 weeks and commissioned in 1 to 2 days, vs. up to 4 to 6 months. All this adds up to what Rogers thinks is a unique set of capabilities to serve injection molding customers in need of highly flexible automation. Is six-axis an expensive solution? Not if you make good use of its capabilities, says Rogers. “Depending on how many shifts you run, it could be $2 to $5/hr. And there are some things you can do with a six-axis that you can’t do with human operators or any other kind of robot.”
New Machine Learning Tool for Predictive Maintenance
AI Servo Monitor, in conjunction with MT-LINKi through machine learning, analyzes the daily performance of machines equipped with FANUC CNCs. Daily data is displayed in intuitive graphs which allows users to easily monitor abnormalities on these machines. Artificial intelligence automatically creates a baseline model of the machine while running in a normal state. An “anomaly score” developed expresses a difference in the baseline model and the daily recorded values. On a web interface, users can easily see the anomaly scores in a graph. Plus, email notifications can be issued if this value exceeds the predefined thresholds.
The Power of Predictive Maintenance
“Getting to the level of predictive maintenance is an evolutionary process for manufacturers, regardless of their specialty,” notes Will Healy III, global business strategy manager at Balluff Inc. “Right now, there is great interest in retrofitting equipment with sensors to perform condition monitoring as a means to implement predictive maintenance. The next step is using equipment with integrated smart sensors and artificial intelligence. These technologies also enable prescriptive maintenance, which uses machine learning to help companies specifically adjust their operating conditions for desired production outcomes.”
One of the first robotic predictive maintenance applications of the IIoT occurred several years ago in the auto industry when General Motors teamed up with Cisco and FANUC America Corp. to launch a zero downtime program. Called ZDT, the predictive analytics service identifies potential failures so engineers and plant managers can schedule maintenance and repairs. This prevents unexpected breakdowns during production, thereby saving manufacturers time and money. According to Tuohy, the ZDT program has proven to be quite successful over the last several years. He says that about 30,000 robots worldwide are connected to the system.
Automate the Impossible: MIRAI-Powered FANUC Robots Master Cable Plugging
Inside or Outside?
According to ASSEMBLY magazine’s 26th annual capital equipment spending survey (December 2021), manufacturers, on average, meet 40 percent of their assembly system needs with equipment built in-house. Manufacturers that are able to build quality automation equipment in-house gain many benefits. Some of the main ones, according to Treter, include being able to fully protect intellectual property; maintaining the confidentiality of a new product or a proprietary assembly process; and using the team’s extensive product knowledge to modify or redesign equipment whenever necessary.
Automated Assembly for Waterproof Electrical Connectors, Courtesy of Noble Plastics
Machine Shop Creates Robot Machining Cell Before There was Work for It
This machine shop’s self-integrated robot was purchased without a project in mind. However, when a particular part order came in, the robot paired with the proper machine tool was an optimal fit for the job, offering consistency and an increase in throughput.
The M-10 is a six-axis robot that is designed specifically for small work cells and can lift up to 12 kg. Young purchased the robot with a force sensor, which he highly recommends. Force sensors enable robots to detect force and torque applied to the end effector. This provides it with an almost human sense of touch. Surprising to Young and his team, the force sensor was not difficult to set up and use.
After the robot purchase and the order came in, it was time to search for the right machine tool for the job. The Hardinge Bridgeport V480 APC VMC was attractive to Young because of its pallet changing system that maximizes spindle uptime.
Custom Tool’s automated data collection and reporting system developed by company president, Gillen Young, uses a web-based, Industrial Internet of Things (IIoT) platform to pull data from machines that have agents for the open-source MTConnect communication protocol as well as the company’s JobBoss enterprise resource planning (ERP) software. The platform is Devicewise for Factory from Telit, a company that offers IIoT modules, software and connectivity services and software.
Plug-and-Play Robot Ecosystems on the Rise
Robot ecosystems are bringing plug-and-play ease to compatible hardware and software peripherals, while adding greater value and functionality to robots. Some might argue that the first robot ecosystem was the network of robot integrators that has expanded over the last couple decades to support robot manufacturers and their customers. Robot integrators continue to be vital to robotics adoption and proliferation. Yet an interesting phenomenon began to take shape a few years ago with the growing popularity of collaborative robots and the industry’s focus on ease of use.
Campbell describes the typical process for engineering a new gripping solution for a robot: “You have to first engineer a mechanical interface, which may mean an adapter plate, and maybe some other additional hardware. If you’re an integrator, it must be documented, because everything you do as an integrator you have to document. You have to engineer the electrical interface, how you’re going to control it, what kind of I/O signals, what kind of sensors. And then you have to design some kind of software.
“When I talk to integrators, they say it’s typically 1 to 3 days’ worth of work just to put a simple gripper on a robot. What we’ve been able to do in the UR+ program is chip away at time and cost throughout the project.”
How Amazon's Middle Mile team helps packages make the journey to your doorstep
“To give you an idea of the scale and complexity we’re managing, our trucking network alone presents us with over 1088 — or ten octovigintillion — possible routing solutions,” says Tim Jacobs, director of Middle Mile Research Science and Optimization. “This is an especially large number, when you consider that there are 1082 atoms in the visible universe.”
And that’s just for the trucking network.
When a product is ordered on the Amazon Store, there are several ways it can make its way from a fulfillment center to the customer’s residence.
How Robotic Automation Impacts E-Commerce
AI and machine learning technologies are enabling new applications. In fact, most of the applications in ecommerce/fulfillment require some type of machine vision. However, with the huge proliferation of SKUs, the old way of programming for a particular part or object discretely is much more difficult to figure out what item to pick next. AI and machine learning will provide more opportunities for companies to expand their capabilities and help ease the burden of dealing with high levels of product variability.