IT OT Convergence
Assembly Line
Aftermarket Sensors Boost Yield In Wafer Fabs
Third-party sensors are being added into fab equipment to help boost yield and to extend the life of expensive tools, supplementing the sensors that come with equipment used in fabs.
Sensors for temperature, pressure, humidity, gas concentrations, vibration and current can be found throughout the process tools and associated sub-systems. Third-party sensors are often added after installation and initial process development. Those sensors also can be applied to sub-fab intake and outtake pipes to monitor parameters that illuminate issues affecting process variability and defectivity. In addition, equipment teams can use instrumented wafers as a source for equipment set-up, post maintenance qualification and for predictive maintenance algorithms.
The good news is that integrating 3rd-party sensor data into factory data management systems is straightforward, especially when sensors use established SEMI standards like SECS/GEM. Even if they don’t adhere to those standards, the barrier to connect them to a factory’s data infrastructure is low. But to maximize their usefulness, several industry experts emphasized the importance of traceability and the ability to correlate data from different sources in context.
Toyota shifts into overdrive: Developing an AI platform for enhanced manufacturing efficiency
Our goal was to build an AI Platform that enabled factory floor employees, regardless of their AI expertise, to create machine learning models with ease. This would facilitate the automation of manual, labor-intensive tasks, freeing up human workers to focus on higher-value activities such as process optimization, AI implementation in other production areas, and data-driven decision-making. By the time we completed implementing the AI platform earlier this year, we found it would be able to save us as many as 10,000 hours of mundane work annually through manufacturing efficiency and process optimization.
The speed of build and processing was also a big appeal for us . In particular, Google Kubernetes Engine (GKE), its Autopilot and Image Streaming provide flexibility and speed, thereby allowing us to improve cost-effectiveness in terms of operational burden. We measured the communication speed of containerization during the system evaluation process, and found that Google Cloud was four times faster scaling from zero than other existing services. The speed of communication and processing is extremely important, as we use up to 10,000 images when creating the learning model. When we first started developing AI technology in-house, we struggled with flexible system scaling and operations. In this regard, too, using Google Cloud was the ideal choice.
Currently, the development team compiles work logs and feedback from the manufacturing floor, and we believe that the day will soon come when we will start utilizing generative AI. For example, the team is considering using AI to create images for testing machine learning during the production preparation stage, which has been challenging due to a lack of data. In addition, we are considering using Gemini Code Assist to improve the developer experience, or using Gemini to convert past knowledge into RAG and implement a recommendation feature.
Energy Monitoring for Manufacturers Gets Easier
In a session on the benefits of energy monitoring at Inductive Automation’s Ignition Community Conference 2024, Becca Gillespie, managing director of Energy Systems Network’s Energy Insights, noted how the savings from energy monitoring can provide critical justification for needed automation investments. For example, one manufacturer Gillespie worked with was able to save 20% on their energy bill by learning how much the installation of VFDs (variable frequency drives) would save them. She explained that this manufacturer installed VFDs on all the fans in their facility to optimize the speed at which they ran instead of always running at full speed.
The optimal network concept for artificial intelligence
What requirements does the use of AI therefore place on automation and networking? The following points should be kept in mind:
- Large amounts of data have to be transported from the field to the AI system.
- The result of the AI operation has an effect on the process to be controlled.
- High-precision time synchronization is essential for processing and evaluating distributed data from the field level.
A concept in which all of these requirements can be met in a single network is ideal. The mechanisms of Time Sensitive Networks in combination with Profinet form the optimal architecture for AI applications. Compared to separate networks for field communication and IT, there are significant advantages for existing and new applications.
Inside Siemens’ Bad Neustadt factory: A firsthand look at IT/OT convergence in action
The Bad Neustadt factory produces multi-axis electrical motors for Motion Control Drive systems—a high variance and high-volume production achieved through a Make-to-Order approach. This highly individualized method produces 500,000+ configurable variants for customers per year. In fact, they estimate that the factory changes the product setup process every seven minutes on average and is approaching being a lot-size of one factory where everything is made to order. To keep pace with this incredible demand, factory management at Bad Neustadt must continuously refine their production processes to meet client expectations and maintain competitiveness.
One major use case is streamlining anomaly detection for end-of-line testing. Referred to as a “reduced test effort”, it entails using historic data to derive rules for determining if a specific motor needs to be tested. The goal is to minimize testing without sacrificing the quality of the motors shipped. By applying AI algorithms, they can calculate the number of tests needed for a specific motor configuration. Combining historical data with real-time information from the factory floor, the intelligent algorithm dynamically defines the number of tests, resulting in reduced time and money spent on testing. In fact, the EWN achieved 16% less end-of-line tests for motors.
Safeguarding CNC Machines in Networked IT/OT Environments
In a networked IT/OT environment, threats can infiltrate CNC machines even without direct access from an attacker. IT users engaging with vulnerable websites or falling victim to phishing emails can introduce threats into the corporate network, ultimately spreading to the OT side and affecting individual machines.
To safeguard CNC machines in networked IT/OT environments, several immediate steps can be implemented. Firstly, conducting a comprehensive inventory of all CNC tools is essential to maintain a clear and up-to-date understanding of the network’s composition. Segregating the CNC machine network into its own segment is advisable, allowing for more straightforward monitoring of traffic flows, with the ability to cut off access if necessary. Additionally, deploying intrusion prevention systems (IPS) and firewalls adds an extra layer of protection to enhance overall security.
Next big thing in smart factories? Control systems virtualization
Virtualization technology has dramatically changed the way IT resources are used, and services are delivered, enhancing efficiency, flexibility, and scalability. However, the benefits of virtualization have yet to benefit industrial operations in any significant way. Industrial Automation and Control Systems (IACS) hardware resources in these environments continue to exist as discrete resources. With digitization, the number of such hardware resources has risen rapidly and so has the time and expense of monitoring, updating, and troubleshooting, which could require extended downtimes and result in productivity losses.
Manufacturing facilities stand to gain a lot by virtualization. They can consolidate Programmable Logic Controllers (PLC), Industrial PCs (IPC), Human Machine Interfaces (HMI), Gateways, and other physical compute resources currently on their factory floors onto local virtual machines which run on a hyperconverged compute and storage infrastructure.
Why Convergence of IT/OT Needs to Include ET
The increased sophistication of intelligent devices and associated software drive the need for tighter integration of the IT and OT domains to gain new insight from known information. However, in the digital data environment of IIoT, engineering technology, those technologies that create virtual models must be included in the convergence conversation. While inclusion of ET may have been implied in the past, its use in the current and future work environment cannot be underestimated as modeling tools become essential to managerial or technical decision making.
He shared a real-world example of IT/OT/ET converging at Lilly. An HMI graphics card needed replacement due to a defective component. The replacement card required a driver that was incompatible with the existing version of Windows in that operation. A Windows upgrade would affect other applications. In this case, the obsolescence of a single component on a graphics card cascaded into a project to upgrade the HMI and all other affected applications and interfaces. This seemingly simple issue required extensive IT/OT/ET collaboration to solve.
In his presentation, Mr. Ruth shared an IT/OT/ET convergence story that has spawned a whole new service business Convergence of IT/OT Needs ETfor Danfoss, the Smart Store. Per Ruth, it’s all about Big Data and a new way of doing business enabled by IIoT technology to increase operational effectiveness and control, and reduce costs. This supplier of refrigeration equipment, compressors, and controllers for supermarkets required an integrated solution to help customers view operational data at a more granular level.
Unlocking the Full Potential of Manufacturing Capabilities Through Digital Twins on AWS
In this post, we will explore the collaboration between Amazon Web Services (AWS) and Matterport to create a digital twin proof of concept (POC) for Belden Inc. at one of its major manufacturing facilities in Richmond, Indiana. The purpose of this digital twin POC was to gain insights and optimize operations in employee training, asset performance monitoring, and remote asset inspection at one of its assembly lines.
The onsite capture process required no more than an hour to capture a significant portion of the plant operation. Using the industry-leading Matterport 3D Pro3 capture camera system, we captured high-resolution imagery with high-fidelity measurement information to digitally recreate the entire plant environment.
The use of MQTT protocol to natively connect and send equipment data to AWS IoT Core further streamlined the process. MQTT, an efficient and lightweight messaging protocol designed for Internet of Things (IoT) applications, ensured seamless communication with minimal latency. This integration allowed for quick access to critical equipment data, facilitating informed decision making and enabling proactive maintenance measures.
Throughout the plant, sensors were strategically deployed to collect essential operational data that was previously missing. These sensors were responsible for monitoring various aspects of machine performance, availability, and health status, including indicators such as vibration, temperature, current, and power. Subsequently, the gathered operational data was transmitted through Belden’s zero-trust operational technology network to Belden Horizon Data Operations (BHDO).
Harnessing Machine Learning for Anomaly Detection in the Building Products Industry with Databricks
One of the biggest data-driven use cases at LP was monitoring process anomalies with time-series data from thousands of sensors. With Apache Spark on Databricks, large amounts of data can be ingested and prepared at scale to assist mill decision-makers in improving quality and process metrics. To prepare these data for mill data analytics, data science, and advanced predictive analytics, it is necessary for companies like LP to process sensor information faster and more reliably than on-premises data warehousing solutions alone.
Automating Quality Machine Inspection Infused with Edge AI and Digital Twins for Device Monitoring
In this post, we will discuss an AI-based solution Kyndryl has built on Amazon Web Services (AWS) to detect pores on the welding process using acoustic data and a custom-built algorithm leveraging voltage data. We’ll describe how Kyndryl collaborated with AWS to design an end-to-end solution for detecting welding pores in a manufacturing plant using AWS analytics services and by enabling digital twins to monitor welding machines effectively.
Kyndryl’s solution flow consists of collecting acoustic data with voltage and current from welding machines, processing and inferencing data at the edge to detect welding pores while providing actionable insights to welding operators. Additionally, data is streamed to the cloud to perform historical analysis and improve operational efficiency and product quality over time. A digital twin is enabled to monitor the welding operation in real-time with warnings created to proactively manage the asset when predefined thresholds are met.
TeamViewer to drive smart factory innovation with strategic investments in manufacturing analytics and IoT
TeamViewer, a leading global provider of remote connectivity and workplace digitalization solutions, today announced strategic investments in two pioneering companies for smart factory solutions: Sight Machine and Cybus. With a total investment of a low double-digit million EUR amount, the company is strengthening its commitment to the digital transformation of industrial working environments and the convergence of Information and Operation Technology (IT & OT).
Securely sending industrial data to AWS IoT services using unidirectional gateways
Unidirectional gateways are a combination of hardware and software. Unidirectional gateway hardware is physically able to send data in only one direction, while the gateway software replicates servers and emulates devices. Since the gateway is physically able to send data in only one direction, there is no possibility of IT-based or internet-based security events pivoting into the OT networks. The gateway’s replica servers and emulated devices simplify OT/IT integration.
A typical unidirectional gateway hardware implementation consists of a network appliance containing two separate circuit boards joined by a fiberoptic cable. The “TX,” or “transmit,” board contains a fiber-optic transmitter, and the “RX,” or “receive,” board contains a fiber-optic receiver. Unlike conventional fiber-optic communication components, which are transceivers, the TX appliance does not contain a receiver and the RX appliance does not contain a transmitter. Because there is no laser in the receiver, there is no physical way for the receiving circuit board to send any information back to the transmitting board. The appliance can be used to transmit information out of the control system network into an external network, or directly to the internet, without the risk of a cyber event or another signal returning into the control system.
Yokogawa and FPT Software Ink Global Partnership to Advance DX Solution Offerings
Specifically, FPT Software will strengthen Yokogawa’s IT capabilities by providing support in the areas of application development, system maintenance, infrastructure deployment, infrastructure operation, and the offering of software as a service. Yokogawa will leverage its solid foundation of OT know-how and experience to support FPT Software in applying its technologies to the field, while continuously enhancing its own DX-related solutions and services. As a result, the customers of both companies will be able to experience a broader array of enhanced and differentiated DX services that make combined use of OT and IT.
OT-IT Integration: AWS and Siemens break down data silos by closing the machine-to-cloud gap
AWS announced that AWS IoT SiteWise Edge, on-premises software that makes it easy to collect, organize, process, and monitor equipment data, can now be deployed directly from the Siemens Industrial Edge Marketplace to help simplify, accelerate, and reduce the cost of sending industrial equipment data to the AWS cloud. This new offering aims to help bridge the chasm between OT and IT by allowing customers to start ingesting OT data from a variety of industrial protocols into the cloud faster using Siemens Industrial Edge Devices already connected to machines, removing layers of configuration and accelerating time to value.
Customers can now jumpstart industrial data ingestion from machine to edge (Level 1 and Level 2 OT networks) by deploying AWS IoT SiteWise Edge using existing Siemens Industrial Edge infrastructure and connectivity applications such as SIMATIC S7+ Connector, Modbus TCP Connector, and more. You can then securely aggregate and process data from a large number of machines and production lines (Level 3), as well as send it to the AWS cloud for use across a wide range of use cases. This empowers process engineers, maintenance technicians, and efficiency champions to derive business value from operational data that is organized and contextualized for use in local and cloud applications, unlocking use cases such as asset monitoring, predictive maintenance, quality inspection, and energy management.
The Blueprint for Industrial Transformation: Building a Strong Data Foundation with AWS IoT SiteWise
AWS IoT SiteWise is a managed service that makes it easy to collect, organize, and analyze data from industrial equipment at scale, helping customers make better, data-driven decisions. Our customers such as Volkswagen Group, Coca-Cola İçecek, and Yara International have used AWS IoT SiteWise to build industrial data platforms that allow them to contextualize and analyze Operational Technology (OT) data generated across their plants, creating a global view of their operations and businesses. In addition, our AWS Partners such as Embassy of Things (EOT), Tata Consulting Services (TCS) Edge2Web, TensorIoT, and Radix Engineering have made AWS IoT SiteWise the foundation for purpose-built applications that enable use cases such as predictive maintenance and asset performance monitoring. Through these engagements with customers and partners, we have learned that the main obstacles in scaling digital transformation initiatives include project complexity, infrastructure costs, and time to value.
With newly added APIs, AWS IoT SiteWise now allows you to bulk import, export, and update industrial asset model metadata at scale from diverse systems such as data historians, other AWS accounts, or – in the case of AWS Independent Software Vendors (ISV) Partners – their own industrial data modeling tools.
To collect real-time data from equipment, AWS IoT SiteWise provides AWS IoT SiteWise Edge, software created by AWS and deployed on premises to make it easy to collect, organize, process, and monitor equipment at the edge. With SiteWise Edge, customers can securely connect to and read data from equipment using industrial protocols and standards such as OPC-UA. In collaboration with AWS Partner Domatica, we recently added support for an additional 10 industrial protocols including MQTT, Modbus, and SIMATIC S7, diversifying the type of data that can be ingested into AWS IoT SiteWise from equipment, machines, and legacy systems for processing at the edge or enriching your industrial data lake. By ingesting data to the cloud with sub-second latency, customers can use AWS IoT SiteWise to monitor hundreds of thousands of high-value assets across their industrial operations in near real time.
Industrial Automation Software Management on AWS—Best Practices for Operational Excellence
Operational and maintenance tasks can become complex, and change control becomes challenging as the number of PLCs and robotics or other automation systems increases. Problems arise when the right version and right configuration of the code is not found. While code and configuration management is a standard DevOps practice for software development, these practices are not as common in the world of industrial automation, primarily due to lack of good tooling. These challenges can now be solved through systematic, secure, and easily accessible solutions in the AWS cloud.
One such solution is Copia Automation’s Git-based source control (Git is an open-source DevOps tool for source code management). Copia Automation brings the power of a modern source control system (specifically, Git) to industrial automation. The Copia solution is deployed in Amazon’s own AWS account. In this type of deployment model, Amazon is responsible for managing and configuring its own infrastructure needed to run Copia’s software.
🇺🇸 Why Build a New Factory in the US? Logistics, Not Politics
Siemens is almost as excited about the guts of the Fort Worth facility as it is about the demand that supports the additional capacity. The company has digitally simulated the entire process of setting up a new plant, including the construction design, the layout of the factory floor and the product development but also the day-to-day manufacturing workflows. “We optimize it, we shift it around and when we like it — not before that — we start bringing in excavating machines on the site or putting machines into it,” Busch said. This lets Siemens get the construction right the first time — which is important at a time of high inflation — but it also sets up a virtuous cycle of productivity improvements whereby plant managers can test out tweaks digitally and carry them out with much less equipment downtime, and sensor-packed equipment can yield insights from the field that spark yet more tweaks.
Digital simulation can be game changer — for Siemens itself and for its customers. For example, when a beverage manufacturer rolls out a new product, the viscosity of the liquid will affect the speed at which it can run its filling machines. Traditionally, this was just a trial and error process that resulted in a lot of spilled beverages. “What we can do is we can simulate it — the viscosity and whatnot, the whole plant. And then you just have a new mixture and you run it seamlessly without fooling around,” Busch said. It’s almost like a video game but for a factory — and much more sophisticated.
Embracing the Unified Namespace Architecture with Litmus Edge
What exactly is a Unified Namespace (UNS)? The UNS offers a structured approach to organizing and connecting data across all layers of a business. It is particularly noteworthy because of its values-driven nature, which is a powerful influence behind its growing popularity.
Several companies, including Starbucks (food and beverages), Richemont (luxury goods) and Stada (life sciences) are already using the UNS architecture to improve their operations. So, if you’re here because you’re considering the UNS for your business too, you’re in good company. We wrote this article to help on your path.
Often mistaken for being a technology, the UNS embodies the principles of an Event-driven Architecture (EDA). In EDA, applications interact by exchanging events without being directly connected to each other. They rely on an intermediary called an event broker, which acts like a modern-day messenger.
The UNS stands out for 4 key reasons -
- It serves as the single source of truth (SST) for all data and information in your business.
- It structures and continually updates data across the entire business.
- It acts as the central hub where all data-connected smart components communicate.
- It lays the foundation for a digital future.
Exploring Manufacturing Databases with James Sewell
Adopting open-source Industrial IoT software
Siloed solutions and ad-hoc efforts to tap into the fourth industrial revolution by funding one-time AI/ML and digitalisation projects in manufacturing fell short of their promises. Enterprises did not address the fundamental challenges behind the lagging security, updates and maintenance in industrial hardware, but only focused on applying the latest technologies. Legacy install bases and a lack of standardisation prevented industrial transformation from occurring. To fully reap the benefits of Industry 4.0, the industrial factory has to close the gaps between Operational Technology (OT) and IT. The convergence between the two domains calls for a transition from legacy stacks with closed standards and interfaces to modern IT solutions and the embrace of open-source software.
Should every machine owner have secure remote service?
In an ideal scenario, a new machine is seamlessly installed, equipped with a scalable and easy-to-deploy remote access strategy, promptly connecting to a secure, zero-trust remote service system. When inevitable issues arise, the OEM promptly dispatches experienced service providers to assist the customer’s machine needs. In most cases, there would be no need for service personnel to physically visit the location and in fact industrial machinery OEMs who are best-in-class for using remote service experience a reduction of more than 80% of their service technician travel episodes. Remote access enables service issues to be resolved efficiently resulting in minimal downtime, because machines are up and running in hours, not days or weeks.
How Git-Based Source Control Drives IT/OT Convergence
The topic of robust data management is often overlooked in the convergence conversation; however, it is an area of IT expertise that can be easily applied to OT processes, yielding huge benefits. Git-based source control coupled with formalized review practices, a staple in traditional software development, represents an opportunity unmatched in driving OT team productivity and increased code quality.
Using Git repositories and processes as a framework for OT source control can align IT and OT. From setup, participating IT team members gain immediate visibility into crucial OT systems, their file structures, and the processes used to develop control programs. Likewise, OT teams realize the benefits of securing and tracking code changes, unlocking easy review workflows, and quick code recovery during incidents.
Git-based version control is not common in industrial automation environments. The backbone of OT networks are the PLC control systems that drive manufacturing machinery. PLC systems are often written in visual languages (i.e., ladder logic and function block diagrams) using proprietary development tools. The result is a collection of local binary files on an engineer’s desktop or control devices.
Recently Copia Automation has developed new tools to unlock Git’s full power for these file formats. When using Copia, automation professionals can track all changes, visualize the file outside the development environment, and see the highlighted differences between the versions. Add in the power of Git branching and merging, and Copia delivers a source control framework that enables engineers to build code together, collaborate more effectively, and review all program changes quickly and thoroughly.
IT vs. OT: The Difference Between Information Technology and Operational Technology
For organizations with a heavy reliance on OT assets, including manufacturers, IT/OT convergence offers the potential for cost savings and resource efficiencies. It allows insights provided through sales and inventory data to be fed into the operational side of the business, enabling manufacturing equipment and power use to be optimized.
When IT and OT are seamlessly integrated, factory operators have more direct control over their manufacturing processes and the ability to monitor their operations. They can easily analyze data from complex systems in real-time, unleashing a new level of improved decision-making and operational efficiency.
Emerson Unveils Architecture Vision for ‘Boundless Automation’
Building on a deep legacy of industry-leading digital automation expertise through its Plantweb™ digital ecosystem, global technology and software company Emerson (NYSE: EMR) today shared its vision of a new software-defined automation architecture designed to catalyze the future of modern manufacturing.
This next-generation architecture will empower companies through “boundless automation” to manage, connect and deliver operational technology (OT) and information technology (IT) data seamlessly and easily across the enterprise. Moving data freely and securely across OT and IT domains – from the intelligent field to the edge and cloud – will enable operational and business performance optimization across the enterprise.
Emerson, drawing upon decades of automation leadership through its preeminent Plantweb digital ecosystem, shared this vision to accelerate manufacturing today at its Emerson Exchange convening nearly 3,000 industrial experts to discuss emerging ways to optimize business and sustainability performance through advanced automation. This vision follows Emerson’s latest expansion of Plantweb with the Aspen Tech industrial software portfolio.