Plastics processing machines already feature an extremely high degree of automation. If they’re going to further optimize their production processes, manufacturers in the plastics industry have to look for solutions that improve overall plant efficiency across the board. This means including auxiliary buildings, media supply and the entire plant infrastructure in the equation. Better than any other system on the market, integrated automation technology from B&R does precisely that. With a system that reaches from the I/O level up to the process control system for the entire plant, B&R offers a complete solution cast from a single mold. A solution for every automation requirement. A solution for more productivity.
Production of plastic parts is highly automated with extremely fast cycle times. The various production methods – such as injection molding, deep-drawing, compression molding and blow molding – place intense demands on speed, precision and repeat precision.
Access to information about the capacity utilization and availability of individual machines is essential to optimizing productivity. This data provides the foundation that allows the various machines in a production line to be coordinated. Machines with a high level of availability are crucial for ensuring maximum productivity. Considerable improvements in maintenance can be made with a condition monitoring solution.
Condition monitoring raises quality and lowers costs
Continuous condition monitoring increases the quality of products and the availability of machines and plants, while at the same time reducing maintenance costs. B&R’s APROL ConMon solution provides vibration monitoring and analysis based on key condition parameters calculated from acquired measurement data.
With individual machines already fully automated, the next frontier is increasing the level of automation in the plant as a whole – including not only the individual machines but also the inhouse logistics systems and building services technology. Effective automation at this level demands a system whose abilities go beyond those of a conventional SCADA system.
Individual machine controllers must be grouped together into a network without sacrificing their autonomy in the process. In addition, this networked system must be capable of directly controlling and querying sensors and actuators so that no gaps in the process occur between individually controlled devices.
Energy monitoring sheds light on real energy costs
Production costs will never be completely optimized until energy consumption has been optimized as well. Before this can be
done, one must first know the exact energy costs involved – both primary (forming processes) and secondary (machine and plant). B&R’s APROL EnMon solution makes it easy for plant operators to acquire all the relevant energy data and generate comprehensive reports for evaluation and interpretation.
Comparing the efficiency of each machine is the first step. What is more difficult however, is collecting data for entire production lines and the plant itself. This demands a sophisticated process data acquisition system that spans the entire production chain and plant infrastructure: B&R’s APROL PDA.
UNIWELL Rohrsysteme GmbH & Co KG, German manufacturer of air and fluid lines for automobile production, uses APROL PDA for quality assurance. “Fluctuations in production parameters can have a negative impact on quality that may otherwise go unnoticed until it is too late,” explains UNIWELL’s technical manager Lutz Goldhammer.
“Since we’ve had APROL collecting process data across the entire production line and making it available to automation processes, our machines are now able to quickly compensate for any deviations, or stop production entirely if necessary,” Goldhammer continues.
POWERLINK integrates proprietary systems
I/O modules connected via POWERLINK permit sensor data to be queried directly. This allows UNIWELL to monitor every step in production
seamlessly and to take action when necessary to avoid unnecessary costs.
A significantly higher level of process optimization can be attained by monitoring not just a single production line, but rather an entire plant along with all of its auxiliary equipment. Manufacturers can make the necessary adjustments to the production processes and substantially improve the overall energy balance of their products.
APROL process control system
APROL is a full-fledged process control system whose comprehensive features go far beyond process data acquisition to include supervisory level control and process visualization tasks. Customers enjoy process automation with a uniform experience and performance, from the field level up to the management level. APROL covers all applications, whether they are oriented toward processing or production. Integrated functions provide data exchange with production planning, simulation and control systems via a database interface, web interface or OPC.
Uniform platform – From the management level to the field
Regardless of their make and model, the controllers on the individual production machines can be connected directly to the network using standard fieldbus technologies. With B&R controllers, the integration goes even deeper. For configuring the hardware and fieldbus network, the APROL process control system contains Automation Studio, the same development environment used for machine automation.
Together with the APROL system’s process data acquisition functions, integration of this familiar engineering tool inside of APROL provides a consistent platform for optimum efficiency in the coordination of machines and their infrastructure.
Integrated safety technology
Like other sectors of machine manufacturing, the plastics industry is being shaped by a trend towards modularization. Bus-based safety technology is an important topic when it comes to modular machines. It facilitates the development of optional machine components and makes it possible to exchange machine modules during operation. The result is a considerable boost in efficiency compared to a hard-wired safety solution.
B&R uses the fieldbus-independent openSAFETY protocol for the communication of secure signals. Using the “black channel” principle, openSAFETY can tunnel through the transport layer of any Ethernet system. Even machine components equipped with different control systems and different fieldbus systems can be easily integrated.
openSAFETY – First choice for plastics processing machines
There are other reasons why openSAFETY is first choice for plastics processing machines, however. With extremely short response times it guarantees minimum stopping distances, even for very fast movements.
openSAFETY also allows drives to perform intelligent reactions, preventing damage in the event of an abrupt stop and providing controlled emergency operation and rapid restarts – all while ensuring complete safety for plant employees. An array of safe reactions, such as safely limited speed, can be implemented not only for individual axes, but also for the tool center point of complex kinematic chains.
Safe communication throughout the plant
Integrated safety technology has firmly established itself as a solution for individual machines – particularly for machines with handling units and optional components. Recently, however, plastics manufacturers have also been using it to provide safe communication throughout an entire series of cascaded production machines.
With openSAFETY, the machines can be grouped in a safety network regardless of what fieldbus technology they use internally. This enables them to coordinate their reactions to safety-related events. It also eliminates the risks involved in having different machines along the same production line each respond differently. Having the entire line share a single safety perimeter shrinks its footprint and saves the cost of peripheral safety equipment.
Complete system – Complete consistency
Numerous users are already using B&R’s APROL process control system to unite their plant systems centrally into a hierarchical complete system. With a broad spectrum of functions – including integrated system simulation using MATLAB/Simulink – APROL is able to combine every level of automation into a homogeneous complete system.
Direct integration of external systems and signal sources allows for an all-encompassing approach that ensures reliable and efficient operation of the system over its entire service life.
FOR MORE INFORMATION AT http://www.br-automation.com
New Technologies Pair the Physical with the Digital
Digital twinning is one part of the technology road map for Industry 4.0 and the Industrial Internet of Things. A gamut of new technologies must be integrated to work seamlessly together to pair the physical domain with the digital information domain.
Digital twinning seeks to improve the design and maintenance of physical systems by offering datadriven ways to discretely map these physical systems into digital and computerized replicas of themselves. With the arrival of automation and data exchange, digital twinning could be useful in a myriad of industrial applications.
This new industrial context, where the physical and the digital worlds meet, is known as the fourth industrial revolution—or Industry 4.0. Brought on by the intersection of a host of high-technology electronic and computer systems, the “new way” of Industry 4.0 promises increasing gains, efficiencies, and flexibility. A gamut of new technologies must be integrated to work seamlessly together to pair the physical domain with the digital information domain. Digital twinning is only one part of the technology roadmap for Industry 4.0, as these additional technologies are helping to enable digital twinning for Industry 4.0 to manifest its potential:
• Pairing technologies
• Cyber-physical systems
• Augmented, virtual, and mixed reality
• Artificial intelligence
• Additive manufacturing
• 3D printing
• Digital thread
Pairing technologies are critical to digital twinning and the world of Industry 4.0, as these technologies empower a device or system to find, connect, and communicate with other devices and systems. For example, sensors and the Industrial Internet of Things (IIoT) products require the ability to find and connect with other devices successfully. Technologies such as Bluetooth®, among others, are employed to make these connections. To accomplish this, connected devices must be able to interrogate other potentially connectable devices successfully. When inquiring other devices, units must be able to ascertain whether they are communicating with a unit that they should be corresponding and exchanging data with. When properly enabled and successful, this accomplishment is called pairing.
Security issues are paramount. Every device should pair only after proper identification has been confirmed to avoid crosstalk or misinformation. Shortcuts may be achieved through programming algorithms that allow the devices to quickly and easily identify other units that they should pair with. Pairing gets accomplished through authentication keys employing cryptography. Pairing works to ensure that the connections stay bonded in a data exchanging relationship between devices and works to prevent an external source from prying into their data exchanges.
Being that flexibility is paramount, units must be able to make and break their pairing quickly and without external, human involvement. Successful pairing may require ongoing communication to keep the pairing active. If one of the units determines that the pairing bond is no longer relevant to its successful operational objectives, it will remove its pairing relationship and present itself available for a different pairing opportunity.
The National Science Foundation (NSF) defines cyber-physical systems (CPS) as, “The tight conjoining of and coordination between computational and physical resources.” The critical element in this definition is that it focuses on a system approach— where a set of connected things or parts form a complex whole.
A current example of a CPS is the automated airline flight-control systems. Industry 4.0 requires traffic control, not for airplanes, but for the machines, computers, robots, sensors, and processes that comprehensively work together for its realization. It represents a system of higher order than IIoT, because it sits one level higher in the complexity chain. Where IIoT is concerned with collecting, handling, and sharing of large amounts of data, CPS is focused on ensuring that this large amount of data, collected from multiple systems, gets properly utilized across multiple disciplines that are relevant to the industry involved. The unique dilemmas of any given industry will require engineering expertise to address these specific challenges.
Augmented, Virtual, and Mixed Reality
New technologies are augmenting our reality. They are providing us with the ability to overlay digital content in front of us physically, merging the real with the virtual, creating a mixed reality that should be considered augmented. This gain is allowing engineers to view things in new ways. For example, rather than viewing a DT on a computer monitor, we could view a DT using an augmented reality (AR) headset that enables the users to engage with digital content or interact with holograms.
The use of such AR empowers viewers to have an immersive experience whereby they engage their bodily senses.
Reality-enhancing headsets can create real-time experiences of actual conditions happening in the physical world, by way of experiencing them through a digitized environment. AR could lead to new insights and understandings. Additionally, a DT display could appear in the user’s field of view, making real-time feedback that much more accessible and easy to use.
Artificial Intelligence Technologies
IIoT offers the promise to provide connected data; therefore, useful data must be stored and analyzed. Artificial intelligence (AI) is a solution to how to analyze and successfully handle large amounts of digital data. It helps in allowing digital twinning to become more realized because it promotes value by enabling rapid integration, hybrid integration, investment leverage, and system management and compliance.
Through machine learning, it offers the opportunity to use digital data to model, analyze, train, apply, and infer how best to make decisions. AI is helping to change the traditional perspective of computing, moving it beyond what primarily has been an automating- and scaling-process perspective towards a knowledgebased perspective, via actionable insights. Soon, it will help engineers gather new insights and ways to create value. By using a data-science approach, rapidly powered decisions will enable the generation of further opportunities.
Additive manufacturing (AM) is a method of production in which 3D objects are built by adding layer-upon-layer of material. AM holds promise because it leads to industries that can address variable demand and produce products that are distributable and flexible. Two areas of AM – 3D printing and digital thread – are advancing to make digital twinning possible.
3D printing is perhaps the most well-known example of AM. In 3D printing, a printer is programmed to print an object using plastics, metals, or other custom materials with virtually zero lead-time. 3D printing is extremely flexible and eliminates the issues involved in producing objects with large economies of scale. What this means for the future is that you will be able to get what you want quickly—as if walking up to the fast food counter.
With complex systems, however, AM has been confined primarily to the laboratory because all the systems involved do not operate under a unified system and, thus, are hard to scale. Digital thread promises to change that.
A digital thread is a single, seamless strand of data that acts as the constant behind a data-driven digital system. It activates the potential of AM by allowing a unification of disparate applications by way of their adherence to the thread, which is their source of shared information. A digital thread creates an easier process for collecting, managing, and analyzing information from every location involved in the redesigned Industry 4.0. It enables better and more efficient design, production, and utilization throughout the entire process.
Digital twinning will be a hallmark of Industry 4.0, helping to increase gains, efficiencies, and flexibility for existing products and processes. But digital twinning is just one part of the Industry 4.0 road map. Pairing technologies, CPS, AI, and AM are key to seamlessly bringing together the physical realm and the realm of its DT information and insights. While these technologies are bringing their complexities into the digital twinning equation, ultimately, they promise to enable Industry 4.0 to manifest its potential.
by Paul Golata for Mouser Electronics
Industrial manufacturers turn AI to “turbocharge” products and services, says Accenture
The vast majority of manufacturers are turning to artificial intelligence (AI) to “turbocharge” their products and services, finds a new research report from Accenture.
Based on a survey of 500 manufacturing companies in six industries across Europe, North America and Asia, the report notes the ability of AI – particularly when combined with mobile computing and big data analytics – to transform not only core operations, but also worker and customer experiences, and ultimately even business models, and to enable “Industry X.0” strategies.
Yet the research found that only a small group is already leveraging AI in a way that Accenture refers to as Applied Intelligence – intelligent technology and human ingenuity, combined with analytics and industry expertise, applied at the core of business – at scale. For instance, while 98 percent of the surveyed organizations have begun to enhance their offerings with AI, only 16 percent of them have established a holistic AI vision for their business, only 5 percent are committing resources to AI-driven product initiatives, and only 2 percent report that they have begun to leverage AI solutions at scale.
The research also highlights the challenges companies face when trying to use the technology: The concerns cited most often were data quality (identified by 51 percent of respondents); data- and cyber security (45 percent); deciding between ‘buying vs. making’ AI-embedded solutions (45 percent); and data sharing and protecting intellectual property (40 percent).
“The re-invention of industrial products with AI is still in its early stages, and getting it right is anything but easy,” said Eric Schaeffer, a senior managing director at Accenture and global lead of its Industrial practice. “However, the successes of the AI leaders in our sample clearly show that it can be done and that the business case for AI in industrial is very strong.”
The report mentions how companies that re-invent their products by combining AI with other digital technologies can reap huge rewards. For instance, it cites other Accenture research showing that mastering AI can enable industrial-equipment manufacturers to boost their market capitalization by as much as 25 percent.
How AI leaders succeed
To get to these kinds of results, companies must go through a journey which, according to the Accenture report, comprises four stages: (1) exuding belief in AI and its ability to digitally reinvent products; (2) building a vision for leveraging existing offerings with AI; (3) committing resources to AI-driven product reinvention; and (4) executing on their vision and planned initiatives to digitally reinvent the product at scale.
Through clustering the surveyed companies by both industry and “AI-journey stage,” the report shows that AI maturity seems to vary by industry: Automotive companies seem to be more likely to commit to and execute AI initiatives, with 9 percent reaching the third stage and 5 percent reaching the fourth stage. However, only 7 percent and 3 percent of consumer durables companies and industrial and heavy equipment makers, respectively, reach the third stage, with only 1 percent of companies in each of those two sectors reaching the fourth stage.
Other results indicate what sets apart the 16 percent of companies that are at least envisioning AI-enabled ways to reinvent their products: Companies that reach the “vision” stage develop the investment and ecosystem strategies to acquire, process and secure the data needed to drive maximum value from AI. Moreover, they carefully analyze what they need to focus on: 82 percent of these “visionaries” cited enhanced “customer loyalty” and “deeper insights from product and service usage” as the key value drivers for themselves. The same proportion also said that “greater safety” and “smarter solutions and services” would be critical benefits of AI use for their customers.
Most of the 5 percent of surveyed companies that commit significant resources to AI initiatives concentrate on building both the IT capabilities and the skills necessary for large-scale AI implementation: 91 percent of those companies cited analytics and systems integration skills as imperative, and 64 percent said they would change elements of their business model as a result of embedding AI.
The 2 percent that reach the fourth stage – execution at scale – set themselves apart by closely working with ecosystem partners to identify, in granular detail, the AI components they want to combine with other digital technologies, now and in the future, as part of their customer value propositions. Among the key AI solutions these companies are planning to use are computer vision (73 percent), deep learning (64 percent) and robotics process automation (64 percent).
“Our findings suggest a strong correlation between a holistic, well-planned strategy and AI success,” said Raghav Narsalay, a managing director and Industry X.0 research lead at Accenture. “However, three-quarters of the companies we surveyed are still experimenting, using what might be called a ‘scattershot’ approach. But this is likely to change in the near future — and that’s when we’ll really see a rise in product reinvention with AI.”
Results from the research also indicate that the rate of product reinvention with AI will likely vary by industry. Changing sources of profitable revenue is a priority for 65 percent of those in the automotive sector, for example, while the sub-groups of the reports’ “industrial equipment manufacturing” cluster are split: Most heavy equipment makers (57 percent) state that product-lifecycle sales and marketing strategy is their key priority, while 42 percent of those in the industrial and electrical equipment sector expect that embedding AI will lead to changes in their innovation architecture.
hordon kim / email@example.com
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