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Efficient Tube Cutting Using Laser

Efficient Tube Cutting Using Laser

Laser tube cutting machines can reduce overall part costs, or even open up new design possibilities. By Dipl-Wirtsch-Ing Christopher Börner, sales laser tube cutting, and Andreas Finsterle, product management, Trulaser Tube, Trumpf

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The Role Of Machine Vision In IIot

The Role Of Machine Vision In IIot

The Industrial Internet of Things will profoundly change automated production processes. How do machine vision technologies help to cope with these challenges? By Dr Maximilian Lückenhaus, director, marketing and sales, MVTec Software 

The Industrial Internet of Things (IIoT, aka Industry 4.0) stands for the digital networking of humans, objects, and systems to create integrated production processes.

In this context, industrial companies have to cope with several new trends that will fundamentally change production and logistics processes. The IIoT means that all technologies, systems, and components that are involved in the industrial value creation process are connected to each other as well as to company networks and the Internet.

Another important trend in this connection is the smart factory. It refers to a manufacturing environment in which machines, installations, and logistics systems interact independently, without human influence. Here, all systems involved communicate through an integrated network connected to the IIoT.

Automated Automotive

Robotics plays a very important role in the smart factory and IIoT environments. The 2015 World Robot Statistics from the International Federation of Robotics illustrates this fact. The survey expects that around 1.3 million industrial robots will be in use worldwide by 2018. The current global market value of robot systems is US$32 billion across all industries.

Robotics is very strong in the automotive industry. In this sector, investments increased by 43 percent from 2013 to 2014. Robots are particularly well represented in the manufacturing industry. An average of 66 robotic units come to 10,000 workers worldwide. South Korea is the world leader in automating processes using industrial robots, followed by Japan and Germany. The USA takes seventh place, and China ranks 28th.

Cobots Work Closely

There is a current trend within the robotics segment: A new generation of smaller, lighter, and more compact industrial robots are finding their way into the automation and manufacturing industry.

These collaborative robots, called cobots, work very closely with human staff. They often possess one or two arms and sometimes even a head. Besides, they are much cheaper than large stationary five-axis robots. Owing to their light weight, they can also be used as mobile units. The robots can take over further tasks such as supporting their human colleagues or even replacing them when they get sick.

There is a technology which plays a key role in accompanying the new production trends and supporting the further development of automated processes: Machine vision, which has grown by leaps and bounds in the past few years.

The technology uses image acquisition devices, such as high-resolution cameras and sensors. These devices record the production processes from different perspectives and generate digital image data. Special machine vision software is used to process this data. The technology makes it possible to monitor all manufacturing processes and to identify weak spots and optimisation potential.

Another benefit: The technology is very fast. Algorithms process the digital image data in milliseconds, thus paving the way for real-time applications. Besides, machine vision facilitates very robust identification processes and a high detection rate.

Creating Machine Vision Applications

Machine vision technology makes the interaction between humans and the new generation of robots much more efficient. Most of the new industrial robots are already equipped with one or more cameras and integrated machine vision functions. The robots should be prepared rapidly and flexible for different application scenarios—without long training of the various tasks and without cumbersome set-up processes.

In addition, it is essential to easily create machine vision applications. Software such as MVTec’s Merlic can be used, for example. The software’s central element is an image-centric user interface, which guides the user through the application. While conventional programming tools work with complex codes, command- and parameter lists, users benefit from an easy-to-read visual display, similar to a “what you see is what you get” editor.

Moreover, the software also contains a collection of standard vision tools such as image acquisition, calibration, alignment, matching, measuring, counting, checking, reading, position determination, and defect detection.

An integrated feature called easyTouch is able to detect, highlight, and select objects with a single click by simply moving the mouse cursor over an image. This means that configuration of complex parameters is no longer needed, thus saving time and money during the development process. Therefore, employees without in-depth programming experience or image processing knowledge are able to create comprehensive machine vision applications. It is no longer necessary to write a completely new program for each new task.

Optimising The Industrial Value Chain

Machine vision streamlines numerous processes in the industrial value chain. For instance, it precisely detects objects in manufacturing processes, identifies the exact position of workpieces, and finds the optimum alignment for the same. Thus, robots can accurately grasp and process any kind of object. As a result, safety and efficiency in automated production processes will rise significantly.

Two-dimensional processes have so far been the standard method in this area. In this way solely the position of horizontally moving objects (such as on a conveyor belt) can be determined. However, 2D methods cannot identify three-dimensionally acting objects such as interacting cobots. Therefore, the 2D technology is only restrictedly suitable for highly automated production scenarios.

Three-dimensional vision technologies fit better with such scenarios. The method is integrated into the machine vision software, uses a multiple camera setup: Several cameras positioned in various locations view production processes from different perspectives.

As a result, a three-dimensional movement profile is generated. This determines not just the accurate position of objects, but also their movements in three-dimensional space and their speed. The 3D technology optimises robot-supported, highly automated manufacturing processes. The collaboration between humans and machines will become more efficient and safer. For instance, the technology can exactly determine the movement direction of mobile robots that are moving independently through factory buildings, avoiding potential collisions with humans or vehicles.

Efficient Interactions

There is another benefit: The 3D technology is able to optimise the work of stationary and large five-axis robots used for welding and other assembly processes. For example, in the automotive industry these robots operate in separate areas that are inaccessible to human staff. If a person nevertheless steps over a certain line, sensors will stop the robot to ensure that the employee is not hurt.

Valuable time goes by before the robot starts up again, which leads to expensive interruptions in production processes. Using the innovative 3D technology, these processes now become much safer and efficient. The three-dimensional movement profile precisely determines the action radius of the robot. In this way, imminent collisions with human beings are detected in time. Thus, the company can increase safety and additionally save costs, since the frequency of robot shutdowns can be reduced with the precise, three-dimensional monitoring of production processes.

Powerful machine vision technologies not only assist production processes but also optimise quality assurance within numerous industries. For instance, they can be used for the precise scanning and inspection of tool surfaces in the metal industry. The software can safely identify and automatically reject any defective parts before entering downstream process chains. Thanks to the high speed of the image processing systems, automated inspections of large batch sizes take less time.

Furthermore, the technology supports processes in the electronics industry: Machine vision can help safely identify and detect errors for many different components and electronic parts. In addition, packaging processes are optimised: The quantity of products in packages can now be reliably determined, ensuring completeness.

Integrated Logistics And Transfer Processes

Last but not least, logistics and transfer processes also benefit from machine vision software, particularly for communication between products and manufacturing equipment in modern IIoT- resp. Industry 4.0 scenarios. In this context, products contain all manufacturing information in machine-readable form, such as bar codes, QR codes, or color-coding.

Machine vision systems can accurately read this coded information—even with defective codes (such as overexposure, excessively narrow, blurry or partly occluded code bars). The product’s path through the production equipment and individual process steps can be controlled automatically using this data.


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EuroBLECH: Where Are You Going Industry 4.0?

EuroBLECH: Where Are You Going Industry 4.0?

When Industry 4.0 took off in Germany it rapidly made it to the headlines. With the digital agenda of the federal government it received highest political priority. But where stands Industry 4.0 in reality? By Dipl-Ing Nikolaus Fecht and Dr Andreas Thoss on behalf of EuroBLECH.

Theory describes Industry 4.0 or the fourth industrial revolution as full integration of production and communication technologies. It creates a so-called “smart factory”, where people, machines and processes are well connected by internet technologies for the purpose of increased cost efficiency, higher process stability and greater flexibility. After all, technological innovations should save time and money.

How does that look in real life? On one hand, there is an approach to scrutinise whole factories and to re-think and optimise all processes, from the first customer request to after-sales services. On the other hand many SMEs offer solutions for separate business processes. With special software tools, efficiency can be increased dramatically. Beside consequent digitisation of processes, there is a second trend coming up: While product lines are unified the single product is increasingly personalised. While this can lead to smaller lot sizes (even down to one), the new tools will help to retain profitability even then.

A Management Issue

Friedhelm Loh, the sole proprietor of the Friedhelm Loh Group with more than 11,000 employers, spoke on his experiences with the introduction of Industry 4.0 in the Rittal, Germany, factory for industrial control cabinets.

The product portfolio had been adjusted, until 2015 they reduced the number of products from 465 to 110. Five product lines were combined into one. In future, customers will define their purchase using an online configurator. The data from this configurator tool go directly into SAP and NC programmes. From initial material supply up to final distribution all logistic processes are fully automated. The whole process from “customer to customer” is digitally organised.

The cost savings in the process steps are between 15 percent (purchasing and sales department, after-sales service) and 50 percent (manufacturing). Mr Loh’s conclusion: “Only an integrated end-to-end solution which is consistently based on configuration and data, results in a continuous process.”

Within the Trumpf group, a new production unit Sheet Metal Processing has been set up as a fully connected factory. It is comparable to a conventional sheet metal job shop which is completely converted into a smart factory. They use and develop software tools from Trumpf ‘s proprietary solution portfolio TruConnect and their digital business platform Axoom. For a further optimisation of the production process they introduced a MES (Manufacturing Execution System)-system from the TruConnect tool box.

As the heart of the production planning, it evaluates the machine conditions and allows a paper free production with digital accompanying documents. Also, the topic intra-logistics will be optimised towards Industry 4.0 to automatise error-prone routine tasks.

Solutions For All

Not every company can or wants to implement Industry 4.0 in the form of an entire new factory. Today there are many solutions for separate processes, which serve the idea of higher efficiency by connectivity and specialised software.

It starts with indirect processes, that are all the steps in a job that take place before or after the actual manufacturing of the part, regardless of the batch size. As batch sizes shrink due to increasing individualisation, these indirect processes are no longer in proportion to the actual productive work (ie: production itself).

A study conducted by Fraunhofer-IPA (S-Tec) in collaboration with Trumpf found out that the costs for material planning may shrink by up to 75 percent in a smart factory surrounding.

Dominik Weibel and Marco Wüst, two Swiss entrepreneurs, have implemented a similar tool for a sheet metal processing job shop. Within their company eMDe Blechfabrik AG they developed an online system based on Trumpf’s online quotation calculator WebCalculate. Here customers can upload drawings and set material parameters and they receive a full quotation immediately.

After placing an order, customers can track the order throughout all processing steps including delivery. eMDe saves a lot of time with small lot sizes and retains an opportunity for price negotiations with larger orders. More such tools (or actually apps) can be expected soon when Trumpf’s spin-off Axoom becomes fully operational.

Smart software may also save money in manufacturing processes. For example Bystronic has developed a special software for planning a sheet metal cutting job. The online service ByOptimizer calculates an optimised cutting plan for the laser machine based on more than 300 parameters. Parts are grouped so closely on the metal sheet that the gaps (ie: raw material offcuts) are reduced to a minimum. The online service connects seamlessly with existing software, it needs just a few clicks to upload data and online service takes care of everything else. Cutting paths of the laser are reduced by half when a common cut allows for one cut instead of two. Bystronic promises material savings of up to 10 percent depending on contour shape and lot size.

It becomes more challenging if you have a new process and you want to find process parameters for cutting or drilling processes. It needs a well experienced operator and a number of trials to find optimal laser process parameters for a new material. Researchers from the Fraunhofer Institute for Laser Technology ILT have collected simulation know-how for such processes for many years. Adequate simulations usually require a workstation and hours of calculation time, but experts have developed a simplified simulation tool for tablet use. In this app the user can play around with beam parameters such waist diameter and see, the processing result directly. This may reduce make-ready times considerably.

The simulation app from the Fraunhofer ILT allows playing around with process parameters with immediate return of the process changes in a neighboring window.

Another example of smart production will be shown by the Schuler AG at EuroBLECH trade fair. With their concept of a “Smart Press Shop” they want to show how networking solutions in forming technology can increase not only process reliability, but also cost-effectiveness in production.

For this purpose the entire system is simulated and optimised, including all press stages and automation components. The systems provide data measured by sensors installed at numerous points, for example to monitor the press force. This data also allows for a continuous operation control and allows for condition-based maintenance.

Alliances & Initiatives

Industry 4.0 is a key issue for German politics and so there are plenty of projects and events arranged in a national and international frame. Particularly engaged are the German Federal Ministry of Economics and Technology (BMWi) and the Federal Ministry of Education and Research (BMBF). Together with industry organisations and companies they have pooled activities and offerings for small and medium sized companies within the “Plattform Industrie 4.0”. If you think humans will disappear completely from the shop floor, you may consider a look at the so-called “Innovationsallianz 3Dsensation”. Founded within the founding initiative “Zwanzig20 – Partnerschaft für Innovation” of the BMBF companies and research institutions meet here to think about the future man machine interaction. It’s about making men machine interaction more intuitive, safer and more efficient.

With a budget of €100 million (US$112.2 million), partners of the consortium want to work on projects in the fields of manufacturing, mobility, health care and security. Of particular focus are 3D technologies that help machines to capture and interpret complex scenarios rapidly.

Risks & Side Effects?

Putting more services on the net and into the cloud brings a number of new risks on the table. So far, viruses and theft of data are more common on office computers. But with the Stuxnet worm that targeted industrial control systems, it is apparent that machine controls are not secure from fraud. On a recent meeting of the Association of German Engineers VDI the association’s director Ralph Appel said that the number of cyber-attacks on industrial plants or infrastructures of larger and smaller companies is much larger than the news reports, since many companies do not even recognise such attacks.

Accordingly, safety concepts are in high demand. One place where such concepts are developed is the Fraunhofer Institute for Secure Information Technology SIT in Darmstadt. There they built a Trusted Core Network (TCN) which tests the integrity of network knots to ensure that there are no foreign invaders. New participants such as robots, computers or machines are verified continuously and can be connected to the network.

Industry 4.0 is much more than hype; Many of its ideas are implemented already. Solutions for separate processes are in widespread use but the conversion of full complex process chains is still rare. The conversion of indirect processes promises quick wins, in particular if you try to drive profits for small lot sizes.

Detlef Zühlke, head of the technology initiative SmartFactoryKL eV and leader of the group Innovative Factory Systems (IFS) at the German Research Center for Artificial Intelligence, DFKI, said recently at a large Industry 4.0 conference in Anaheim, CA, USA, that it will some more two or three years until the first systems will be running. But then it will become a global competition: “It’s a worldwide movement. Those who are too late with it will finally be the first to die on the market.”

The simulation app from the Fraunhofer ILT allows dynamic adjustment of the process parameters, immediately returning process changes in the adjacent window

The simulation app from the Fraunhofer ILT allows dynamic adjustment of the process parameters, immediately returning process changes in the adjacent window.

The simulation app from the Fraunhofer ILT allows dynamic adjustment of the process parameters, immediately returning process changes in the adjacent window.

The simulation app from the Fraunhofer ILT allows dynamic adjustment of the process parameters, immediately returning process changes in the adjacent window.


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Implementing IIOT: The Time Is Now

Implementing IIOT: The Time Is Now

Applying the Industrial Internet of Things (IIoT) to factories confers a host of operational benefits. By Advantech

Machinery and production automation systems need to be advanced enough to deliver high performance, and integrated enough to provide economical operation, yet must be based on mature products and methodologies offering sufficient reliability.

So why push for tightly integrated operational information and other advanced functionalities if individual pieces of machinery are running “good enough”?

The main reason is because harvesting, processing and analysing the correct data helps operational personnel make the best informed choices at their facilities, and enables management to optimise strategic plans throughout multiple locations. Simply put, advanced data analytics improves efficiency, reduces maintenance, and creates a safer work environment.

Convergent Evolution

Fortunately in recent years, a number of device, communication, and software capabilities have developed in an interrelated manner—making it easier to extract and analyse manufacturing data.

When combined effectively, they can elevate “business as usual” manufacturing to “smart” manufacturing. In fact, in many ways automated manufacturing is already smarter than one might expect.

Machinery and process plants commonly employ control systems with many types of sensors. While the highly-touted Internet of Things (IoT) concept promises that one day all devices will become networked information providers, it turns out that the Industrial IoT (IIoT) already has countless sensors and other devices reporting data to higher level automation systems. Where the IoT is directed toward consumer convenience, the IIoT takes a laser focus on efficiency and safety.

Manufacturers such as Advantech offer a spectrum of hardware and software to facilitate gathering information from the lowest level sensor, or any machine, and routing it over a network to higher level automation, visualisation, and information systems. Automation controllers pre-process and package the raw information from sensors and other field devices. These devices are the “things” in the IIoT.

Industrial wired and wireless networks, working in conjunction with the Internet and cloud services, are the superhighway for moving information. This information moves from field controllers to human machine interfaces (HMIs) located on the plant floor and in control rooms, and from the HMIs to front office PCs and out into the mobile world of smartphones and tablets.

Smart manufacturing is a powerful trend, building on readily available hardware and software to take production operations to the next level of performance.

The Time To Implement The IIoT Is Now

Manufacturing businesses worldwide want to implement the IIoT to gather more data and improve operations. While these objectives have been present for many decades, it’s now much more feasible to implement the IIoT because of the technology advancements as expounded upon below.

Why Implement The IIoT Now?

  • Most new devices offer smart connectivity
  • Methods exist to enable traditional devices to become smart
  • Controllers are proficient at handling smart data
  • Standardised wired and wireless Ethernet networks are economical, powerful, and pervasive
  • Specific industrial networking formats are common
  • Open interfaces and numerous drivers are available to facilitate economic integration
  • Communication methods are suitable for private and public clouds
  • Mobile visualisation offers new ways to bring data to users
  • Big data harvested from the IIoT can be more easily analysed
  • Smart manufacturing adoption can occur in steps, with benefits realised along the way

More often than not, connectivity is the “killer app”. Consumer devices such as phones, watches, appliances, and even sneakers are commonly able to connect and interact with each other.

Similarly, industrial devices have moved from awkward and proprietary communication interfaces to standardised networks and protocols, often Ethernet-based. In today’s market, industrial manufacturing demands connectivity from most devices purchased. Even if the functionality is not immediately needed, it helps to future-proof investments.

For legacy devices using basic analogue and digital signals, or maybe simple serial communications, there are modules that can boost this equipment up on to contemporary networks and protocols. In this way, end users can choose an upgrade path that preserves their existing system, yet provides value by making their “dumb” devices smart, leading to intelligent machinery.

Connecting Islands To The Mainland

Many production plants consist of “islands of automation”. Often, there are many automated skids or systems with minimal interaction among them, even though taken as a whole they form a production line. Sometimes these systems have been assembled and grown over a long period of time.

What they have in common, though, is that each island is operated by one or more controllers. Industrial controllers have more than enough power to perform some data processing, but may not share common communication protocols.

Fortunately, there are many flavours of “gateways” or “bridges” available. These can take the form of dedicated configurable devices, or PCs running various drivers and communication software. These gateways can translate pertinent information from existing systems into a suitable format for higher level integration.

When disparate controllers and the systems they control are capable of being connected, some huge informational advances can be achieved. Such systems can be interconnected to supervisory alarming and historian systems, consolidating key information from a whole production line into a few effective displays or reports.

For many operations, when subsystems are integrated in this way, it is possible to achieve a transfer of upstream and downstream information and improve the production flow. Or, when production goes down it is possible to use the integrated information to identify and eliminate the root cause, promoting overall equipment effectiveness (OEE) tracking.

These are just a few of the benefits of a connected factory. As Jamie Carter puts it, “In the wider economy, the IIoT is critical in reducing unplanned downtime of production facilities and plants.”

Moving Information To The Next Level

Assuming that technical and cost barriers are overcome for gathering information in a smart factory, what are the next steps? The first is typically to make the information visible to operators and managers so that they can make informed decisions.

This used to mean tabular lists or printouts of numbers, but information presented in this manner is difficult for people to process. That is why so many variants of graphical display software and HMI packages have been developed.

Earlier generation HMIs used to just reside locally to their associated factory processes. Today’s HMIs use networking, the Internet, and public or private cloud services to extend their reach to wherever users are. Instead of just a single machine, production line, or factory being coordinated—it is now possible to manage multiple factories across the world in a more organised manner.

The Internet and cloud services are ideal for publishing smart manufacturing information to laptops, tablets, and smartphones, putting the information directly in user’s hands. Many visualisation software packages have features specifically adapted to mobile device operation. It has become especially prevalent and useful for mobile devices to present a streamlined “dashboard” view which shows only the most important information in an easy-to-read format.

End user expectations from HMI packages have soared, due to consumer familiarity with high performance home computers, phones, and tablets. The graphics must be informative and must also look good and easy to use. HMIs that take advantage of multi-touch swipe and zoom gestures position themselves that much close to the everyday user.

Browser-based products like Advantech’s WebAccess are available that offer a familiar user experience, are easily extendable to all types of devices, and are able to publish the information conveniently over the Internet.

Harvesting Big Data

But the smart factory is about much more than just dishing out pretty graphics. At the factory level, the proper flow of status and command information is crucial for manufacturing execution systems (MES) that strive to track and record the production of finished goods. At an even higher level, data is required for enterprise resource planning (ERP) and business logistics systems to be effective.

A real opportunity exists when all of the big data can be harvested from many IIoT sources, and then effectively analysed to reveal inefficiencies that can be overcome or trends that can be intelligently re-vectored.

Gathering enough of the right information can enable users to make discoveries that would be otherwise impossible. Besides just improved throughput, benefits can be found in material costs, energy efficiencies, labour costs, maintenance costs, and the cost of adverse quality.

Keep in mind that implementing smart manufacturing is not an all-or-nothing proposition. If fact, adopting smart technologies and methods can (and often should be) carried out in steps. This reduces the initial cost, and allows an organisation to determine which pieces of the smart factory yield the most benefit for their situation.

The time to implement the IIoT is now, and here are the specific components which make up a typical IIoT implementation in a manufacturing plant.

IIoT Building Blocks

Data flowing through the smart factory can be imagined as a pyramid structure as shown graphically in Figure 1, and as detailed in Figure 2.

Another good reference is ISA-95, which defines industrial automation interface concepts from the lowest (Level 0) to the highest (Level 4) level in terms of both functionality and immediacy. If “Level 0” is considered to be the actual physical process, then the smart manufacturing foundation begins at “Level 1” and consists of the sensors and field devices.

Examples of IIoT building blocks:

  • Smart sensors
  • Network-capable I/O
  • Controllers–PLCs, PACs, DDCs, Proprietary
  • Network switches, media converters, routers, security
  • Visualisation, fixed location
  • Visualisation, mobile
  • Business strategy systems

Traditional sensors were historically hardwired and offered only a single basic process signal, but today’s smart sensors are networked and provide additional process signals and device diagnostics. They can maintain on-board calibration data, and technicians can interact with these sensors remotely. Think of a flow transmitter that also provides temperature and pressure information, and can alarm when the data readings are suspect.

More advanced analysers can simultaneously provide multiple-sensed variables for complex parameters. Barcode readers and RFID tags are key ways to establish material tracking. Many other types of smart sensors and field devices are available, all capable of providing data to higher level systems.

The Highest Levels Of Smart Manufacturing

HMIs are “Level 2” systems that facilitate detailed plant operations. They can be PC-based running software, or a more dedicated hardware type. Plant networks supply HMIs with the information they need, either directly from field devices, or more commonly through I/O and controllers.

These HMIs can be flexibly located in main control rooms, on machines, in maintenance and management locations, or elsewhere. More recently, it has become common to configure consumer-grade or industrial-grade tablets as HMIs and troubleshooting stations that can be carried around the factory.

One of the real game changers in HMI space over the past decade is the emergence of browser-based products. No longer are users tied to specialised hardware, or difficult software installations. Just as PCs and Ethernet successfully leveraged commercial technology into the industrial arena, browser-based products prospered by offering much of the same end user experience as traditional software, but at a lower price point and requiring near-zero configuration on the end user’s device.

These products are capable of providing an HMI interface anywhere within a facility, on all types of mobile devices, and throughout the world via the Internet. Not only that, but they can offer advanced features such as integration with Excel, Google Maps, and video streams.

Comprehensive Smart Manufacturing Solution

Residing above HMIs are “Level 3” MES and “Level 4” ERP systems. These software-based systems typically run on servers located at a given production plant, or even far away in a corporate office. Software systems at each progressively higher level are typically less “real-time” than at lower levels. While MES and ERP systems are a subject of their own, they both require close integration with lower level sensor and control systems in order to be effective.

A comprehensive smart manufacturing solution built on an IIoT foundation is necessary to power operations and business management. These IIoT building blocks can be combined to create real-word applications to deliver specific benefits, as shown in the following example.

Any time there are multiple steps in a process, it is critical to identify which steps are the limiting throughput factor. Similarly, if there is a failure, then operators need information to point them to the root cause. Smart manufacturing will harvest all of the production key performance indicators, and use them to identify bottlenecks that can be improved, and will also facilitate troubleshooting.

At the highest level, data provided via smart manufacturing allows business operators to track, direct and optimise their raw material usage and productive output. Uptime and downtime can be analysed, and inefficiencies identified and wiped out. Without the data provided by smart manufacturing systems, none of this is possible.

Putting Your Data To Work

For today’s factory, superficial good looks aren’t enough to prove that things are running at their best. Instead, additional improvement opportunities must be actively sought to create a smart factory. One way to do this revolves around obtaining more operational data and putting it to work. Any process of improvement is based on quantitative analysis of measurements, and fortunately the IIoT opens up a whole new world of quantifiable data.

Connectivity is no longer a unique luxury, as it has instead become a baseline requirement. Intelligent machinery leads to a connected factory, which in turn provides the platform for smart manufacturing. Businesses everywhere want to leverage the IIoT in the most expedient way possible, and fortunately the technology is available now to make this happen.

The building blocks are smart devices, methods for making legacy equipment smarter, robust networking, and a wide variety of software—all of which are readily available to build into new facilities or integrate into existing operations. The widespread availability and ease-of-use of these enabling technologies allows end users to focus less on how to harvest the data, and concentrate more on improving operations.


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