Launching the Exponential Industry APIs, starting with a simple low latency application programming interface (API) for statistical processing control (SPC).
Exposing and leveraging third-party APIs is critical to the success of Industry 4.0 just like Web 2.0 companies used APIs to create new business models and gigantic businesses. Also, the cloud manufacturing wars heat up between Microsoft and Amazon, and commentary on Italy’s n...
It’s arrived: Commoditization for industrial process control
With the advent of industrial process-control commoditization has come technological advancements that have expanded the boundaries of modern manufacturing–right to the computing edge. Traditionally, administrators had to walk out to a control system–USB stick in hand–and apply an update manually. Today, thanks to the combined work of Intel Corporation, Schneider Electric, and Red Hat, manufacturers can enjoy an edge-ready, software-defined, industrial control system that relieves the burden of manual effort and runs on commodity hardware and a commodity operating system and uses commodity automation techniques.
☁️🧠 Automated Cloud-to-Edge Deployment of Industrial AI Models with Siemens Industrial Edge
Due to the sensitive nature of OT systems, a cloud-to-edge deployment can become a challenge. Specialized hardware devices are required, strict network protection is applied, and security policies are in place. Data can only be pulled by an intermediate factory IT system from where it can be deployed to the OT systems through highly controlled processes.
The following solution describes the “pull” deployment mechanism by using AWS services and Siemens Industrial AI software portfolio. The deployment process is enabled by three main components, the first of which is the Siemens AI Software Development Kit (AI SDK). After a model is created by a data scientist on Amazon SageMaker and stored in the SageMaker model registry, this SDK allows users to package a model in a format suitable for edge deployment using Siemens Industrial Edge. The second component, and the central connection between cloud and edge, is the Siemens AI Model Manager (AI MM). The third component is the Siemens AI Inference Server (AIIS), a specialized and hardened AI runtime environment running as a container on Siemens IEDs deployed on the shopfloor. The AIIS receives the packaged model from AI MM and is responsible to load, execute, and monitor ML models close to the production lines.
Bringing Scalable AI to the Edge with Databricks and Azure DevOps
The ML-optimized runtime in Databricks contains popular ML frameworks such as PyTorch, TensorFlow, and scikit-learn. In this solution accelerator, we will build a basic Random Forest ML model in Databricks that will later be deployed to edge devices to execute inferences directly on the manufacturing shop floor. The focus will essentially be the deployment of ML Model built on Databricks to edge devices.
Improving Image Resolution At The Edge
Edge Learning Classify Tutorial - In-Sight 3800 Vision System
HAYAT HOLDING uses Amazon SageMaker to increase product quality and optimize manufacturing output, saving $300,000 annually
In this post, we share how HAYAT HOLDING—a global player with 41 companies operating in different industries, including HAYAT, the world’s fourth-largest branded diaper manufacturer, and KEAS, the world’s fifth-largest wood-based panel manufacturer—collaborated with AWS to build a solution that uses Amazon SageMaker Model Training, Amazon SageMaker Automatic Model Tuning, and Amazon SageMaker Model Deployment to continuously improve operational performance, increase product quality, and optimize manufacturing output of medium-density fiberboard (MDF) wood panels.
Quality prediction using ML is powerful but requires effort and skill to design, integrate with the manufacturing process, and maintain. With the support of AWS Prototyping specialists, and AWS Partner Deloitte, HAYAT HOLDING built an end-to-end pipeline. Product quality prediction and adhesive consumption recommendation results can be observed by field experts through dashboards in near-real time, resulting in a faster feedback loop. Laboratory results indicate a significant impact equating to savings of $300,000 annually, reducing their carbon footprint in production by preventing unnecessary chemical waste.
Industrial defect detection at the edge
🦾 How to Make a Cost-effective Flexible Robotic Solution for Low-volume Production
Despite its process flexibility, the unified cell is still a production system that requires a level of investment that might not be the best suited for scenarios where one wants to produce short production runs of products that don’t share common applications (e.g. joining equipment) therefore require reconfiguration of its applications for every production run. To address this problem, we need a production system that can be reconfigured with multiple process capabilities while reusing as much equipment as possible to allow good utilisation of investment-heavy resources while meeting the machine safety directives and technical requirements.
By lessening the complexity of the hardware architecture, we can significantly increase the capabilities and ways of using the equipment that makes it financially efficient even for low-volume tasks. Moreover, the further development of the solution can be mostly in the software part, which is easier, faster and cheaper than hardware R&D. Having “chipset” architecture allows us to start using AI algorithms - a huge prospective.
Smart Factory with TI TDA4VM
Knowing the status and health of factory machinery is critical to an organization’s success. It takes factory operator vigilance to regularly monitor equipment and take action if anomalous behavior is detected. However, it can be fatiguing for personnel to constantly monitor equipment, and if an issue is missed, weeks of downtime for costly repairs can be the result.
This is where the power of computer vision on the edge can be invaluable. Using a computer model trained to detect nominal and off-nominal behavior, operators can be alerted of issues, rather than having to be constantly on the lookout. And with the inferencing being done at the edge, privacy is maintained, and organizational leadership can breathe easy that perhaps sensitive images won’t be sent to the cloud for remote inferencing.
New NVIDIA IGX Platform Helps Create Safe, Autonomous Factories of the Future
NVIDIA today introduced the IGX edge AI computing platform for secure, safe autonomous systems. IGX brings together hardware with programmable safety extensions, commercial operating-system support and powerful AI software — enabling organizations to safely and securely deliver AI in support of human-machine collaboration. The all-in-one platform enables next-level safety, security and perception for use cases in healthcare, as well as in industrial edge AI.
TELUS: Solving for workers’ safety with edge computing and 5G
Together with Google Cloud, we have been leveraging solutions with the power of MEC and 5G to develop a workers’ safety application in our Edmonton Data Center that enables on-premise video analytics cameras to screen manufacturing facilities and ensure compliance with safety requirements to operate heavy-duty machinery. The CCTV (closed-circuit television) cameras we used are cost-effective and easier to deploy than RTLS (real time location services) solutions that detect worker proximity and avoid collisions. This is a positive, proactive step to steadily improve workplace safety. For example, if a worker’s hand is close to a drill, that drill press will not bore holes in any surface until the video analytics camera detects that the worker’s hand has been removed from the safety zone area.
Nokia strengthens partnership with Microsoft to enhance performance at the mission critical industrial edge
Nokia today announced plans to integrate Microsoft Azure Arc capabilities into the Nokia MX Industrial Edge (MXIE) platform, unlocking the potential of mission critical applications for Industry 4.0 use cases. Through the integration, Nokia MXIE and private wireless solution customers have seamless access to the full Azure ecosystem offering on MXIE.
Aimed to support industries including automotive, manufacturing, energy, logistics and government, the powerful combination will enable use cases by allowing customers to run applications in the traditional cloud, as well as directly on their premises. Collaboration in these areas will provide numerous benefits such as increasing worker safety through AI and automation, while decreasing the amount of needed backhaul with local data processing.
Where is 'The Edge' and why does it matter?
The Edge is not a place – It is an optimization problem. Edge computing is about doing the right things in the right places. As with all optimization problems, getting to the “right” answer requires considering a number of tradeoffs that are specific to your situation and then applying the right technology to maximize the benefits for the cost you are willing to pay.
Part of what makes Edge confusing is that definitions of “The Edge” tend to focus on technologies rather than on use cases. Since use cases span a very wide range of requirements and the boundaries between those use cases don’t map directly to technologies, definitions in terms of technology can be difficult to use.
Manufacturing Shifts To AI Of Things
Preventive maintenance is an important part of smart manufacturing, but this is just the beginning. AIoT can be deployed in many different areas in a factory to further increase productivity. For example, it can be used for incoming inspection. Traditionally, the quality control department performs sample inspection. Instead of inspecting 100% of the components used to build a device, only a sample — say 10% — will be audited. With the installation of a 3D HD camera, AIoT can inspect 100% of the components and screen out defective parts at an early stage. Additionally, a robotic arm can pick out defective components or those of different colors and/or shapes, further reducing reject rates.
AIoT also can be used to improve worker safety, resulting in lower worker compensation payments. For example, a warehouse can be equipped with AIoT cameras to ensure only authorized workers wearing appropriate safety equipment can enter the warehouse.
How ALPLA has adopted CrateOM for Smart Manufacturing
Nokia launches first off-the-shelf, mission-critical Industrial Edge to accelerate the enterprise journey to Industry 4.0
Nokia today announced it has launched the industry’s first cloud-native, mission-critical industrial edge solution to allow enterprises to accelerate their operational technology (OT) digitalization initiatives and advance their journey to Industry 4.0. The new Nokia MX Industrial Edge is a scalable application and compute solution designed to meet the mission-critical digital transformation needs of asset-intensive industries such as manufacturing, energy, and transportation. It uniquely combines compute, storage, wired/wireless networking, one-click industrial applications and automated management onto a unified, on-premise OT digital transformation platform.
AI in the Process Industry
When applying AI to difficult problems in plants, approaches differ depending on whether AI researchers can access useful information derived from similar problems. This article first discusses how to search and identify useful research and literature. If well established AI research is available, the next step is simply to choose an appropriate AI platform. If not, the most serious bottleneck for the problem-solving task arises: how to integrate plant domain knowledge and AI technology. This article presents a solution to the latter case. This solution enables plant engineers to make full use of AI geared for themselves, not for data scientists. AI-based control, which is one of the promising AI applications for plants and is expected to solve difficult problems in plants, is also discussed.
AWS IoT SiteWise Edge Is Now Generally Available for Processing Industrial Equipment Data on Premises
With AWS IoT SiteWise Edge, you can organize and process your equipment data in the on-premises SiteWise gateway using AWS IoT SiteWise asset models. You can then read the equipment data locally from the gateway using the same application programming interfaces (APIs) that you use with AWS IoT SiteWise in the cloud. For example, you can compute metrics such as Overall Equipment Effectiveness (OEE) locally for use in a production-line monitoring dashboard on the factory floor.
Real-World ML with Coral: Manufacturing
For over 3 years, Coral has been focused on enabling privacy-preserving Edge ML with low-power, high performance products. We’ve released many examples and projects designed to help you quickly accelerate ML for your specific needs. One of the most common requests we get after exploring the Coral models and projects is: How do we move to production?
- Worker Safety - Performs generic person detection (powered by COCO-trained SSDLite MobileDet) and then runs a simple algorithm to detect bounding box collisions to see if a person is in an unsafe region.
- Visual Inspection - Performs apple detection (using the same COCO-trained SSDLite MobileDet from Worker Safety) and then crops the frame to the detected apple and runs a retrained MobileNetV2 that classifies fresh vs rotten apples.
Trash to Cash: Recyclers Tap Startup with World’s Largest Recycling Network to Freshen Up Business Prospects
People worldwide produce 2 billion tons of waste a year, with 37 percent going to landfill, according to the World Bank.
“Sorting by hand on conveyor belts is dirty and dangerous, and the whole place smells like rotting food. People in the recycling industry told me that robots were absolutely needed,” said Horowitz, the company’s CEO.
His startup, AMP Robotics, can double sorting output and increase purity for bales of materials. It can also sort municipal waste, electronic waste, and construction and demolition materials.
SLAM for the real world
To take the next leap forward, the robotics industry needs software that is reliable and effective in the real-world, yet flexible and cost effective to integrate into a wider range of robot platforms and optimized to make efficient use of limited compute, power and memory resources. Creating ‘commercial-grade’ software that is robust enough to be deployed in thousands of robots in the real world, at prices that make that scale achievable, is the next challenge for the industry.
Tree Model Quantization for Embedded Machine Learning Applications
Compressed tree-based models are useful models to consider for embedded machine learning applications, in particular with the compression technique: quantization. Quantization can compress models by significant amounts with a trade-off of slight loss in model fidelity, allowing more room on the device for other programs.
The realities of developing embedded neural networks
With any embedded software destined for deployment in volume production, an enormous amount of effort goes into the code once the implementation of its core functionality has been completed and verified. This optimization phase is all about minimizing memory, CPU and other resources needed so that as much as possible of the software functionality is preserved, while the resources needed to execute it are reduced to the absolute minimum possible.
This process of creating embedded software from lab-based algorithms enables production engineers to cost-engineer software functionality into a mass-production ready form, requiring far cheaper, less capable chips and hardware than the massive compute datacenter used to develop it. However, it usually requires the functionality to be frozen from the beginning, with code modifications only done to improve the way the algorithms themselves are executed. For most software, that is fine: indeed, it enables a rigorous verification methodology to be used to ensure the embedding process retains all the functionality needed.
However, when embedding NN-based AI algorithms, that can be a major problem. Why? Because by freezing the functionality from the beginning, you are removing one of the main ways in which the execution can be optimized.
How Edge Analytics Can Help Manufacturers Overcome Obstacles Associated with More Equipment Data
Big data is transforming a variety of sectors, ushering them into the era of Industry 4.0. However, having access to raw data and knowing what to do with it are at completely different ends of the digitalization spectrum. To help manufacturers understand, and overcome, some of the challenges associated with smart manufacturing, Martin Thunman, CEO and co-founder of leading low-code platform for streaming analytics, automation and integration for industrial IoT, Crosser shares his insight.
FPGA comes back into its own as edge computing and AI catch fire
The niche of edge computing burdens devices with the need for extremely low power operation, tight form factors, agility in the face of changing data sets, and the ability to evolve with changing AI capabilities via remote upgradeability — all at a reasonable price point. This is, in fact, the natural domain of the FPGA with an inherent excellence in accelerating compute-intensive tasks in a flexible, hardware-customizable platform. However, much of the available off-the-shelf FPGAs are geared toward data center applications in which power and cost profiles justify the bloat in FPGA technologies.
Intelligent edge management: why AI and ML are key players
What will the future of network edge management look like? We explain how artificial intelligence and machine learning technologies are crucial for intelligent edge computing and the management of future-proof networks. What’s required, and what are the building blocks needed to make it happen?
Gaining an Edge on Line Control
Edge control provides access to real time OEE and information visualization that changes the value calculation. With edge control, end-users can easily tie together existing equipment, other legacy controllers and new external sensing. The combined raw data can be analyzed at the edge to generate information needed by operators to take fast informed action, and it is the foundation for more advanced production line integration, with the ultimate goal of insight-driven and adaptive operation.
Intel Accelerates AI for Industrial Applications
The human eye can correct for different lighting conditions easily. However, images collected by camera can naturally vary in intensity and contrast if background lighting varies as well. We’ve seen scale challenges observed by factories trying to deploy AI for defect detection based on the exact same hardware, software and algorithm deployed on different machines on the factory floor. Sometimes it took months for factory managers and data scientists to find out why they were getting great results on one machine with high accuracy, low false positive and false negative rates, while on the next machine over the AI application would crash.
Evolving control systems are key to improved performance
For decades, the control system was constrained by physical hardware: hardwired input/output (I/O) layouts, connected controllers and structured architectures including dedicated networks and server configurations. Now, the lower cost of processing power and sensing, the evolution of network and wireless infrastructure, and distributed architectures (including the cloud) are unlocking new opportunities in control systems. Additionally, emerging standards for plug-and-produce, such as advanced physical layer (APL) and modular type package (MTP) interfaces, will drive significant changes in the way plants design and use control systems over the next decade.
Industrial DataOps: Unlocking Data and Analytics for Industry 4.0
As an approach to data analytics, DataOps is all about reducing the time to high-accuracy analyses using automation, statistical process control, and agile methodologies so that manufacturers are able to use the data they collect quicker and with a higher degree of confidence.
The role of DataOps in Industry 4.0 is to take all of the info created and collected by machines, like IIoT devices, and effectively condense them into refined, usable business “fuel” to drive decision-making, rather than be left to sit in a data warehouse, unexamined.
Edge-Inference Architectures Proliferate
What makes one AI system better than another depends on a lot of different factors, including some that aren’t entirely clear.
The new offerings exhibit a wide range of structure, technology, and optimization goals. All must be gentle on power, but some target wired devices while others target battery-powered devices, giving different power/performance targets. While no single architecture is expected to solve every problem, the industry is in a phase of proliferation, not consolidation. It will be a while before the dust settles on the preferred architectures.
Sensor Fusion: The Swiss Army Knife of Digitalization
With the proper communication protocols and network architecture in place, smart sensor technology and the data it provides can be the bulwark on which digital transformation is built.
If industrial control systems are the brains of a plant, then sensors are its eyes and ears. Simply put, without sensors there would be nothing for SCADA, DCS, or PLCs to respond to. That’s why increasingly intelligent or ‘smart’ sensors packing more onboard processing power, the ability to monitor new variables, and digital communication capabilities are playing such an important role in helping plant operators and enterprise level planners alike to see better and respond to problems with more finesse.
Facilitating IoT provisioning at scale
Whether you’re looking to design a new device or retrofitting an existing device for the IoT, you will need to consider IoT provisioning which brings IoT devices online to cloud services. IoT provisioning design requires decisions to be made that impact user experience and security for both network commissioning and credential provisioning mechanisms which configure digital identities, cloud end-points, and network credentials so that devices can securely connect to the cloud.
Industrial automation unites the best of OT and IT
As operational and information technology roles progressively overlap in the industrial automation space, a hybrid operational technology/information technology (OT/IT) solution becomes increasingly necessary.