<p>For many years, silicon Physical Unclonable Functions (PUFs) have been seen as a promising and innovative security technology that was making steady progress. Today, Static Random-Access Memory (SRAM-)based PUFs offer a mature and viable security component that is achieving widespread adoption in commercial products. They are found in devices ranging from tiny sensors and microcontrollers to high-performance Field-Programmable Gate Arrays (FPGAs) and secure elements where they protect financial transactions, user privacy, and military secrets.</p><h2>Physical Unclonable Function (PUF)</h2><ul><li>A PUF provides a way to authenticate electronic devices or chips and generate cryptographic keys from their unclonable silicon fingerprint.</li><li>It uses uncontrollable deep-submicron variations from manufacturing to derive a stable, unclonable, and unique device identity.</li></ul><h2>Benefits of SRAM PUF</h2><ul><li>No need to program keys or obtain entropy externally.</li><li>No secrets stored in device when power is off.</li><li>Keys and identifiers based on unclonable silicon structure.</li><li>Uses existing, standard hardware (SRAM) as PUF.</li></ul><h2>Reliability and Certification</h2><ul><li>Advanced error correction.</li><li>Based on years of extensive reliability testing across technology nodes and operating environments.</li><li>Devices using SRAM PUFs have been government vetted and certified EMVCo-Visa and CC EAL6+.</li></ul><h2>Application Markets</h2><ul><li>Smartcard, FPGA</li><li>Sensors, microcontrollers</li><li>Mobile, automotive, IoT</li></ul><h1>The SRAM-based PUF</h1><p>Due to deep submicron manufacturing process variations, every transistor in an Integrated Circuit (IC) has slightly different physical properties. These lead to small but measurable differences in terms of electronic properties, such as transistor threshold voltages and gain factor. Since these process variations are not fully controllable during manufacturing, these physical device properties cannot be copied or cloned.</p><p>Threshold voltages are susceptible to environmental conditions such as temperature, and voltage so their values cannot be used directly as unique secret keys or identifiers.&nbsp;</p><p>The behavior of an SRAM cell, on the other hand, depends on the difference of the threshold voltages of its transistors. Even the smallest differences will be amplified and push the SRAM cell into one of two stable states. Its PUF behavior is therefore much more stable than the underlying threshold voltages, making it the most straightforward and most stable way to use the threshold voltages to build an identifier.</p><h2><span class="fr-img-caption fr-fic fr-dib" style="width: 643px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1591264573768-SRAM_behavior2.png"><span class="fr-inner">Figure 1: Extracting a strong secret key from SRAM behavior.&nbsp;</span></span></span></h2><h2>SRAM PUF Behavior</h2><p>An SRAM memory consists of a number of SRAM cells. Each SRAM cell consists of two cross-coupled inverters that each are built up by a p- and n-MOS transistor. When power is applied to an SRAM cell, its logical state is determined by the relation between the threshold voltages of the p-MOS transistors in the invertors. The transistor that starts conducting first determines the outcome, a logical &#39;0&#39; or &#39;1&#39;.</p><p>It turns out that every SRAM cell has its own preferred state every time the SRAM is powered resulting from the random differences in the threshold voltages. This preference is independent from the preference of the neighboring cells and independent of the location of the cell on the chip or on the wafer.</p><p>Hence an SRAM region yields a unique and random pattern of 0&#39;s and 1&#39;s. This pattern can be called an SRAM fingerprint since it is unique per SRAM and hence per chip. It can be used as a PUF.</p><p>Keys that are derived from the SRAM PUF are not stored &#39;on the chip&#39; but they are extracted &#39;from the chip&#39;, only when they are needed. In that way they are only present in the chip during a very short time window. When the SRAM is not powered there is no key present on the chip making the solution very secure.</p><h2>PUF Reliability</h2><p>The deep submicron process variations that determine PUF behavior are frozen during manufacturing and do not change afterwards. Hence the preference of the SRAM cells is persistent and stable over time.</p><p>However, there is still a degree of noise. A small number of the cells, close to equilibrium are unstable and display a seemingly random startup preference. So, each time the SRAM starts up, a slightly different pattern emerges. This noise component is dependent on temperature, voltage ramp, and operating conditions.</p><p>The noise of SRAM-based PUF responses has been exhaustively characterized and tested under a wide variety of circumstances and foundry processes:</p><ul><li>Temperatures ranging from -55&deg;C to +150&deg;C (-67&deg;F to 300&deg;F)</li><li>Voltage variation +/-20%</li><li>Humidity up to 80%</li><li>EMC tests at 3V/m (EN55020 0.15&ndash;150 MHz and IEC 61000-4-3 80-1000MHz)</li></ul><p>In particular, the SRAM PUF has been qualified for automotive, industrial and military use in collaboration with customers and partners.</p><p>Millions of measurements have been performed. Under all these circumstances the average noise level of the SRAM-based PUF response was found to be lower than about 15%. Despite this amount of noise, it is possible to reconstruct a high-entropy device unique and reliable key every time the SRAM is powered. This can be done by applying error correction techniques, such as &#39;helper data algorithms&#39;<sup>[1]</sup> or &#39;fuzzy extractors&#39;<sup>[2]</sup>. These algorithms perform two main functions that will be explained below: error correction and privacy amplification.</p><h2>Error Correction</h2><p>Error correction techniques for cryptographic key reconstruction require an enrollment phase and a reconstruction phase. In the enrollment phase (a one-time process) the PUF response is mapped onto a codeword of an error correcting code. Information about the mapping is stored in the Activation Code (AC) or helper data. The AC is constructed such that it does not leak any information about the key. It should be stored in memory that is accessible by the PUF algorithms, but it can be stored off-chip as it is not sensitive. Any change to the AC, malicious or not, will prevent key reconstruction. The AC is only valid for the chip on which it was created.</p><p>Each time the device runs an authentication protocol and needs the secret PUF key, a new noisy PUF measurement is carried out and the PUF key (without noise) is extracted from the AC and this new PUF response. This is called the reconstruction phase. Both enrollment and reconstruction phases are illustrated in Figure 2.</p><p>The error correction algorithms have been designed to reconstruct the key with an average error rate of less than 10<sup>-12</sup> and an error rate of less than 10<sup>-9</sup> under extreme circumstances of 25% average noise.</p><h2><span class="fr-img-caption fr-fic fr-dib" style="width: 707px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1591264692705-Enroll_reconstruct.png"><span class="fr-inner"><span contenteditable="true">Figure 2: Enrollment and reconstruction phase for the generation of PUF keys. Note that R is the initial PUF response during enrollment while R&#39; is a PUF response in the field with a noise component.</span></span></span></span></h2><h2>Privacy Amplification and Security</h2><p>Secret keys provide security based on the fact that they are completely random and hence unpredictable. Physical measurements, such as PUF responses, have a high degree of randomness, but are usually not completely uniformly random. Privacy amplification is used to generate uniformly random keys.</p><p>By combining error correction and privacy amplification, a 1kByte SRAM PUF response can be turned into a 256-bit uniformly random key and only approximately 0.5 kByte is needed for a 128-bit key with full randomness. A typical SRAM PUF contains so much entropy that only a few dozen bytes are needed to provide a collision-free globally unique identifier that can be used as a unique (but noisy) electronic chip ID (ECID) or as a serial number.</p><p>Dedicated security labs and security teams at customers have analyzed the security of Intrinsic ID&#39;s SRAM PUF against various invasive and non-invasive physical attacks without revealing any weaknesses. Attacks with Scanning Electron Microscopes, Lasers, FIBs and probes have not been successful. Side-channel attacks have not led to any leakage of sensitive information.</p><h2>Aging</h2><p>Accelerated aging tests have been performed on SRAM PUFs to investigate the noise level as a function of time. By using a patented anti-aging technique<sup>[3]</sup> a 25-year lifetime can be guaranteed for SRAM PUF technology.</p><h1>SRAM PUF Implementation &ndash; QuiddiKey &amp; BroadKey</h1><p>Intrinsic ID has bundled the above error correction, randomness extraction, security countermeasures and anti-aging techniques into its products. They extract cryptographic keys from the SRAM PUF in a very secure manner and are available as Hardware IP (netlist) called QuiddiKey&reg;, firmware (ANSI C Code) called BroadKey&trade;, or a combination of these by using BroadKey&#39;s hardware interface. These hybrid solutions combining hardware and software offer great efficiency when already integrated hardware accelerators, such as for (a-)symmetric cryptography, are used in combination with SRAM PUF technology.</p><p>The hardware IP is small and fast &ndash; around 25K gates / 50K cycles &ndash; and connects to common interconnects, such as AMBA&reg; AHB, APB as well as proprietary interfaces. A Built-In Self-Test (BIST), diagnostics, and health checks are included in the logic. A driver is provided to ease integration with software. Since it is pure digital, single clock logic it synthesizes readily to any technology.</p><p>Software reference implementations start from 4KB of code and are available for major platforms, such as ARM&reg;, ARC&reg;, Intel&reg;, MIPS and RISC-V. Software implementations can be used to add PUF technology to existing products by a firmware upgrade. Intrinsic ID also provides versions of BroadKey that are pre-integrated with ARM&reg; TrustZone&reg;.</p><p>Both QuiddiKey hardware and BroadKey software solutions can be optimized for low footprint, low latency or low memory use depending on the application. Re-using or integrating with existing crypto cores and random number generators can further enhance performance and reduce footprint. Intrinsic ID solutions come with comprehensive product specifications and integration guidelines including reference code that illustrates usage of the API offered to the application programmer.</p><h2>Requirements</h2><p>These Intrinsic ID products use uninitialized SRAM. This can be a separate SRAM block or a part of a bigger existing SRAM. Standard SRAM suffices. To store the Activation Code (AC), access to a storage medium is needed. This can be embedded Non-Volatile-Memory (NVM), a separate memory on the board, e.g. flash, or cloud storage. For the firmware version BroadKey, a microcontroller is needed for which a C-compiler exists. The PUF algorithms can be stored in any NVM e.g., flash, ROM.</p><p>Note that SRAM is embedded in almost any microprocessor and SoC, in every technology node and is part of the standard manufacturing process. There is no need for time-consuming qualification and chip testing, as extensive testing by Intrinsic ID and its partners has shown that technology reliably scales down to the smallest technology nodes currently available.</p><div id="ftn1"><h2>Use in the Field</h2><p>SRAM-based PUFs are commercially available from semiconductor companies. They are already deployed in a wide range of microcontrollers, FPGAs and smart card controllers. In other markets, software implementations<sup>[4]</sup> have enabled rapid deployment of this technology even as a retrofit solution. Intrinsic ID has partnered with leading semiconductor companies and developed solutions for protecting embedded systems, sensors, and controllers. More information about our SRAM PUF deployments can be found on our website: <a href="https://www.intrinsic-id.com" target="_blank">https://www.intrinsic-id.com</a>.</p><h1>Conclusion</h1><p>SRAM-based Physical Unclonable Functions have been successfully implemented in commercial products. SRAM PUFs combine high security and reliability with low cost, low footprint designs, and easy implementation. They have been deployed in many devices, from tiny microcontrollers and sensors to high performance FPGAs and secure elements.</p><p>Numerous implementations have consistently demonstrated the reliability and security of the technology. SRAM PUFs are a mature and robust technology, designed for security and based on solid theoretical foundations. SRAM PUFs have gained their credibility in high security markets and are now gaining traction in markets ranging from low-cost IoT applications to high-end security solutions for government, defense, and the payment industry.</p><div id="ftn1"><h2>References</h2></div></div><p>[1] J.-P. Linnartz and P. Tuyls, &quot;New shielding functions to enhance privacy and prevent misuse of biometric templates,&quot; in International Conference on Audio and Video-based Biometric Person Authentication (AVBPA&#39;03), ser. LNCS, J. Kittler and M. S. Nixon, Eds., vol. 2688. Heidelberg: Springer-Verlag, 2003, pp. 393&ndash;402.</p><div id="ftn2"><p>[2] X. Boyen, &quot;Reusable cryptographic fuzzy extractors,&quot; in ACM Conference on Computer and Communications Security (CCS&#39;04). New York, NY, USA: ACM, 2004, pp. 82&ndash;91. AND Y. Dodis, L. Reyzin, and A. Smith, &quot;Fuzzy extractors: How to generate strong keys from biometrics and other noisy data,&quot; in EUROCRYPT&#39;04, ser. LNCS, C. Cachin and J. Camenisch, Eds., vol. 3027. Heidelberg: Springer-Verlag, 2004, pp. 523&ndash;540.</p><p>[3] R. Maes and V. van der Leest, &quot;Countering the effects of silicon aging on SRAM PUFs&quot;, Proc. IEEE Int. Symp. Hardw.-Oriented Secur. Trust (HOST), pp. 148-153 available at <a href="https://www.intrinsic-id.com/wp-content/uploads/2017/05/PUF_aging.pdf" rel="noopener noreferrer" target="_blank">https://www.intrinsic-id.com/wp-content/uploads/2017/05/PUF_aging.pdf</a>&nbsp;</p><p>[4] H.Hodson &ndash; New Scientist, &quot;Silicon fingerprint on chips could make any gadget unhackable&quot; June 6, 2016 <a href="https://www.newscientist.com/article/mg23030771-400-physical-quirks-in-silicon-chips-are-key-to-unhackable-devices/" rel="noopener noreferrer" target="_blank">https://www.newscientist.com/article/mg23030771-400-physical-quirks-in-silicon-chips-are-key-to-unhackable-devices/</a> or &quot;Chip design quirks make our lives more secure&quot; in the June 11 printed issue page 24.</p></div>

Low-cost and strong secret key storage technology is critical today to enable affordable and effective security systems.

SRAM PUF: The Secure Silicon Fingerprint

The developments that took industrial robotics from automation to collaboration.

Industry 4.0 Deep Dive. Part 4: Evolving Industrial Robots

<p>The introduction of cellular IoT technology has helped transform animal tracking solutions from a nice-to-have to an efficient solution that will save farmers time and money.</p><p>Since we declared 2019 to be the <u><a href="https://blog.nordicsemi.com/getconnected/2019-the-year-of-cellular-iot?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">year of cellular IoT</a></u>, we have been on the lookout for interesting use cases. Farming and agriculture seem well suited for an IoT-led transformation, and given its inherent long-range requirements, cellular is a perfect match. Now, we are starting to see exciting devices come to market.</p><h2>Taking animal tracking to the next level</h2><p>There is little new about tracking herds of animals via tags. Previous iterations of the technology have often been large and heavy, and needed to be hung around the necks of cattle or other animals. Needless to say, this is not a cost effective solution for many use cases.</p><p>Thanks to low-cost, small cellular IoT technology, things are changing. Finnish startup company <u><a href="https://anicare.fi/en/" target="_blank">Anicare</a></u> has launched a tracking device that can be attached to an animal’s ear.</p><p>The benefits are many, from reduced physical impact on the animal to lower cost and reduced resource usage.&nbsp;Other solutions may require the setup of a custom base station or gateway to communicate with the tags in the field. By using&nbsp;the nRF9160, coverage is provided using existing cellular infrastructure.</p><p>Perhaps best of all, the Anicare Healtag also monitors the health of the animal, not just the location.</p><h2>New possibilities for health monitoring</h2><p>By using Nordic’s multimode cellular IoT module to send data to the Cloud, the herd can be remotely monitored and any animal that requires treatment can be quickly identified and located. Despite its small size, the tag incorporates both an accelerometer and thermal sensor to measure activity and temperature on an hourly basis. Any significant changes can indicate illness, injuries or possibly a predator attack. Such changes are reported via NB-IoT, with GPS providing location information.</p><p>There are also time and cost savings thanks to the low-power nature of this device. Previous tag solutions often consumed significant power, resulting in frequent battery changes. The low energy consumption of Nordic’s compact nRF9160 system-in-package contributes to the Anicare Healtag offering a battery lifetime of up to two years.</p><h2>The business case for connected farming</h2><p>Managing hundreds or even thousands of animals with limited time and resources presents a severe challenge to livestock farmers. As modern farmers have to rear more animals to make a profit, the reduced personal attention may result in health issues going unidentified and untreated. This is not desired from an animal welfare nor a financial perspective.</p><p>Connected farming may also aid in determining the cause of animal casualties. Using the last known position allows farmers to find perished individuals and determine cause of death. This may aid in reducing fatalities and simplify reporting to the government for statistics and compensation purposes.</p><p>In all these cases, cellular IoT-enabled solutions such as Anicare’s Healtag can make a real difference.</p><hr><p>This article was first published on Nordic's&nbsp;<a href="https://blog.nordicsemi.com/getconnected" rel="nofollow" target="_blank"></a><a href="https://blog.nordicsemi.com/getconnected?utm_campaign=2021%20wevolver" rel="nofollow" target="_blank">Get Connected Blog</a>.</p>

The introduction of cellular IoT technology has helped transform animal tracking solutions from a nice-to-have to an efficient solution that will save farmers time and money.

Animal Tracking: Cellular IoT In The Field

<p>As demand for ubiquitous connectivity for IoT devices gets ever stronger, cellular networks can deliver reliable and secure IoT services using existing network infrastructure.</p><h2><strong>Cellular connects people</strong></h2><p>The primary technology for connecting people today is cellular. We carry our smartphones everywhere and get our voice and data over a reliable and secure network.</p><p>Massive investments have been made in spectrum allocations and network deployments to ensure good coverage for the entire population in most countries. The same networks can now be leveraged to connect things.</p><h2><strong>A cellular solution to diverse connectivity needs</strong></h2><p>Traditional cellular options such as 2G, 3G or higher category 4G modems consume a lot of power and don’t fit well with applications where only a small amount of data is transmitted infrequently, such as meters, asset trackers, healthcare equipment, agriculture sensors, parking spaces and street lights.</p><p>Cellular IoT is designed to meet the requirements of such low-power, long-range applications. It takes existing technology that we already use every day for our smartphones and scales it back to meet the needs of low-power devices.</p><h2><strong>The infrastructure is already in place</strong></h2><p>When it comes to&nbsp;analysing&nbsp;cost, the total cost of ownership includes spectrum costs, infrastructure costs, and operational expenses. As cellular networks are already in place, very little new infrastructure needs to be installed.</p><p>The base stations, cell towers, buildings and power supply are already in place, all around the world. The technology also has the potential to cover hundreds of thousands of IoT devices per square kilometre - many more than other communication options.&nbsp;</p><h2><strong>LTE-M and NB-IoT</strong></h2><p>No single technology or solution is ideally suited to all the different potential massive IoT applications, market situations and spectrum availability. As a result, the mobile industry is&nbsp;standardising&nbsp;several technologies, including Long Term Evolution for Machines (LTE-M) and Narrow Band IoT (NB-IoT).&nbsp;NB-IoT is ideally suited for low bandwidth, infrequent communication from a relatively stationary device, while LTE-M suits higher bandwidth or mobile and roaming applications.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 788px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTg5ODE5MDU4MjY5LW1pa2EtYmF1bWVpc3Rlci1DeVZxa21NOTFRVS11bnNwbGFzaC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19"><span class="fr-inner">Photo by <a href="https://unsplash.com/@mbaumi?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" target="_blank">Mika Baumeister</a></span></span></span></p><p>A good application for NB-IoT is the use of remote environmental sensors to measure temperature, wind, pressure and so on. These devices can send regular updates from a fixed location while&nbsp;optimising&nbsp;battery use. Such a device could last for up to ten years, or longer if solar-powered and in the right geographical position.</p><p>Similarly, an asset tracker with condition monitoring through several sensors, which is mobile and roaming from country to country, is well served by an LTE-M solution that offers highway speed mobility, international roaming between countries and operators, and efficient firmware updates.</p><h2><strong>A cellular future for IoT</strong></h2><p>Advantages of cellular connectivity for IoT include:</p><ul><li>The use of open standards based on existing infrastructure, means coverage will reach virtually everywhere where people live</li><li>Many devices can operate simultaneously because of the advanced co-existence mechanisms in the LTE standard and licensed-band operation, as is already proven today with the large number of cellphones used concurrently within a small area</li><li>No limiting regulatory regulations, so you can transmit up to 23dBm and negotiate for as much airtime as you need</li><li>Standard TLS/DTLS security for end-to-end security is supported on top of the on-air encryption of the LTE network aided by the SIM credentials. This keeps data secure from the device to the cloud server</li></ul><p>As cellular networks already cover 90 percent of the world’s population and can now be offered in low complexity, low power variants, cellular technology is a great choice for the world’s IoT needs, both now and in the future.</p><hr><p>This article was first published on Nordic's&nbsp;<a href="https://blog.nordicsemi.com/getconnected" rel="nofollow" target="_blank"></a><a href="https://blog.nordicsemi.com/getconnected?utm_campaign=2021%20wevolver" rel="nofollow" target="_blank">Get Connected Blog</a>. </p>

As demand for ubiquitous connectivity for IoT devices gets ever stronger, cellular networks can deliver reliable and secure IoT services using existing network infrastructure.

What is Cellular IoT?

How big data, advanced analytics, and AI are changing manufacturing.

Industry 4.0 Deep Dive. Part 2: Data-Driven Intelligent Production

<p>Automated systems governing smart manufacturing, and enabled by IoT devices, give us the IIoT (Industrial Internet of Things), which allows for the expansion and streamlining of all aspects of business. In addition, the deployment of AI and robots is particularly useful in industry as they increase product quality, production efficiency and overall speed.</p><p>The introduction of collaborative robots (or cobots) is becoming increasingly common, in industry as well as in laboratory work and commerce. Cobots can take instructions from humans in order to work more productively with them, including instructions that were not originally anticipated in the initial programming. Robots don&#39;t get bored, hungry or tired. The <a href="https://www.rowse.co.uk/blog/post/the-rise-of-artificial-intelligence" target="_blank" title="The Rise of Artificial Intelligence">capabilities of Artificial Intelligence</a> are far beyond human capacity when it comes to such things as miniaturisation and precision measurements, and they deliver vastly superior quality assurance.</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1584368176061-1584368176061.png" style="width: 1080px;" class="fr-fic fr-dib"></p><p>So what are the benefits we can expect to see as industry moves forward into the 21st century world of cobots and AI?</p><h2>1. Direct Automation</h2><p>IIoT connects all IoT enabled devices to the factory floor, integrating manufacturing processes with big data and making them programmable via a logic controller. Increased use of precision sensory equipment means that information can be generated, recorded and analysed for all aspects of the production process, covering anything from temperature to item picking and packaging. Programmable logic controllers with AI capacity for deep learning can then respond automatically to the seamlessly generated information, and make alterations to the minutest function without recourse to human intervention. The big data analytics processed by AI can substantially improve performance across the entire production process, and can be operated remotely.</p><h2>2. 24/7 Production</h2><p>Human beings are biological organisms and need regular maintenance, e.g. food and sleep. For any production facility to continue working round the clock it is necessary to introduce shifts, using three human workers for every 24 hour period. Robots don&#39;t get tired or hungry, and are capable of working on the production line 24/7. This allows the expansion of production capabilities, which is increasingly necessary to meet the demands of worldwide customers. Furthermore, robots are more efficient in many areas such as the assembly line, and picking and packing departments. They can greatly reduce turn-round times in many areas of the business operation.</p><h2>3. Safety</h2><p>Human beings are fallible and prone to making mistakes, especially if they are tired or distracted. Errors and accidents do occur on the factory floor and in any construction or processing environment; a tendency which can be all but eradicated by AI and robotic assistance. Remote access control means a reduction in human resources, especially when the work is dangerous or requires superhuman effort. Even regular working environments will cut down on the incidence of industrial accidents and lead to an overall improvement in safety. In addition, more advanced sensory equipment integrated with IIoT devices make the installation of safety guards and barriers a simpler and more effective measure to protect human lives.</p><h2>4. Lower Operational Costs</h2><p>Many companies are viewing the introduction of AI into the manufacturing industry with trepidation, as it requires a huge capital investment. On the other hand, the ROI is significant and increases as time goes on. Once intelligent machines begin to take over the daily activities of a factory floor, businesses will benefit through considerably reduced operating costs, with predictive maintenance helping additionally to reduce machine downtime.</p><p>These days, consumers are increasing their demand for unique, personalised or customised products, while continuing to expect the best value. With such advances as 3D printing and IIoT connected devices, it is becoming simpler and cheaper to meet these needs, and using virtual or augmented reality design techniques means that the whole production process will be more cost-effective. Integrating machine learning and CAD means that systems can be designed and tested in a virtual model before they are put into production, thus reducing the cost of trial-and-error machine testing.</p><h2>5. Greater Efficiency</h2><p>IIoT enables the collection of vast amounts of data and advanced analytics which can be used to gain insights into consumer behaviour. Trends can be predicted, patterns recognised and market developments forecast across time, socioeconomic sectors and geographic markets, as well as taking into account political developments, macroeconomic cycles and even weather patterns. AI has the capacity with machine learning to anticipate information, to refine processes, and to track incongruities, all the way down the supply chain from source to finished product. This is particularly aided by such technologies as&nbsp;<a href="https://www.rowse.co.uk/blog/post/rfid-in-manufacturing" target="_blank" title="RFID In Manufacturing">RFID tracking</a>, which enable materials to be tracked without having to go through a physical process such as a bar code reader.</p><h2>6. Quality Control</h2><p>AI is also extremely useful for carrying out predictive maintenance on machinery and equipment. Using sensors to track performance and operating conditions, machines can learn to predict malfunctions and failures, and take action to remedy them before they occur. This can result in faster feedback, helping companies to eradicate unplanned downtimes.</p><p>Sensors can also detect the mot microscopic defects, scanning them at resolutions far beyond the capacity of human vision, thus improving productivity and increasing the percentage of items that will pass quality control. AI helps to speed up many routine processes and improves accuracy to an enormous extent. This obviates the requirement for quality control and in-process inspection by human beings, which is time-consuming and often fallible.</p><h2>7. Quick Decision Making</h2><p>When IIoT is coupled with cloud computing and virtual or augmented reality, companies can share simulations, confer on production activities and exchange critical or important information in real-time, irrespective of geographical location. The data gathered from sensors and beacons helps to determine consumer activity, allowing companies to anticipate future needs and make rapid decisions on production, as well as speeding up the exchange between manufacturers and suppliers.</p><hr><p>This post was originally posted on <a data-fr-linked="true" href="https://www.rowse.co.uk/blog/post/7-manufacturing-ai-benefits" rel="noopener noreferrer" target="_blank">www.rowse.co.uk</a>.</p>

As the world becomes increasingly automated, the IoT (Internet of Things) is already transforming our domestic and business lives. Nowhere is this more apparent than in the use of AI and robotics in the manufacturing industry, with all the benefits offered by Industry 4.0.

IoT

Latest Posts

SRAM PUF: The Secure Silicon Fingerprint

Industry 4.0 Deep Dive. Part 4: Evolving Industrial Robots

Animal Tracking: Cellular IoT In The Field

What is Cellular IoT?

Industry 4.0 Deep Dive. Part 2: Data-Driven Intelligent Production

7 Benefits of AI In Manufacturing

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