Fractal’s PYO Autonomous AI solution presentation on Microsoft booth at Hannover Messe ‘24

Fractal is proud to announce that its Production Yield Optimization (PYO) solution will be presented Wednesday, April 24th at 10 am on Microsoft’s stand (Hall 17, Stand G06) at the 2024 Hannover Messe.

PYO is a proven Autonomous AI solution that enables manufacturers to reduce waste and optimize manufacturing output with AI agents that dynamically fine-tune existing control systems parameters.

PYO is built on Azure Machine Learning and leverages industry-standard deep reinforcement learning (DRL) techniques and libraries to translate subject matter experts’ expertise into so-called “reward functions” that allow the AI agent to self-train through a custom-built AI simulator. This expertise transfer is commonly referred to as “Machine Teaching”.

Fractal’s end-to-end DRL experience, accelerators, and best practices help manufacturers customize PYO to their unique needs. This is one of the reasons why companies such as PepsiCo have trusted Fractal for their PYO project on their Cheetos product line.

How does PYO work?

The PYO AI agent design, training, and deployment is a multi-step and iterative process.

PYO technical architecture

Once the initial Machine Teaching process is completed, the manufacturing SMEs expertise will help select the appropriate process training data and define the best reinforcement learning reward function. The AI agent is then trained using the new simulation.

The trained agent is then validated virtually with the simulation. However, in most cases, multiple cycles of simulation fine-tuning, reward function adaptation, agent training, and virtual validation will be required to converge to a satisfactory PYO AI agent. The agent is then trialed and fully deployed in production.

The AI agent sends control signals to the simulation or the production system. The reward function will measure the difference between the expected simulation or system state versus its actual state and will modify the agent’s deep neural network weights accordingly. Depending on the system controlled, this training loop will run between hundreds of thousands to millions of times.

Why should you consider PYO?

  • Optimize production with AI: PYO agents learn, through SME expertise transfer, to optimize production for complex and changing environments. Those agents will help with both manufacturing line-level and human-level challenges.
  • Real-world AI solution: PYO uses DRL and simulations to train the AI agents without the need for pre-existing labeled datasets.
  • Fractal’s end-to-end expertise: Bringing an AI agent from design to deployment requires a large set of data and AI skills. Fractal is a recognized Microsoft Solution Partner with the expertise to support you throughout your data and AI transformation journey.

If you want to learn more about PYO and how it can help you achieve your production goals, come and attend our presentation at the Microsoft booth on Wednesday, April 24th, at 10 am.

You can also check out our PYO solution page here.

Digital twin vs. simulations: the quick cheat sheet

What is a digital twin?

Wikipedia defines a digital twin as a “virtual representation that serves as the real-time digital counterpart of a physical object or process.”

In theory, a digital twin will gather input from connected sensors, machinery, and people to store and display them in a cloud-hosted application. However, besides being able to look back in time what happened when and perform some post-mortem analysis, a digital twin limited to a backward-looking view won’t have much business interest.

Therefore, often digital twins will also integrate simulations of what is represents. Those simulations can be at the device, process, or even plant level. It will allow users to leverage this combination of real-time data and system-level behavior modeling through the simulator for multiple use cases.

Digital twin vs. simulations: the quick cheat sheet

For instance, a digital twin can be used to:  

  • Replay a system behavior based on historical data  
  • Do advanced “what-if” analysis before deciding which path to choose  
  • Train new operators on virtual processes before letting them work on the actual real-life process  
  • Simulate a process to train an AI Agent using Deep Reinforcement Learning (DRL)
  • And more depending on the industry that digital twins are used in 

Digital twins represent a great business improvement opportunity for customers across industries. However, what they are, how they work, how they can positively impact operations, and what technologies are involved with digital twins are often questions customers struggle to answer” – Manish Amin, Data & AI and IoT Principal and Advisor, Microsoft

Digital twin vs. simulator  

A simulator’s scope is often limited to a particular piece of equipment or process, although not always. Once programmed or trained, the simulator will run separately from the real-life process. 

Conversely, a digital twin will often encompass a broader process comprised of multiple pieces of equipment, and it will remain connected to the live system to represent it faithfully. 

Therefore, a simplified way to think about the difference between a digital twin and a simulator is to consider that a digital twin is a simulation whose states (inputs, outputs) are updated to accurately reflect their real-life value. A simulator could end up drifting from real life or even provide wrong data; a digital twin won’t if it remains connected. 

Digital twin vs. simulations: the quick cheat sheet

Conversely, simulators operate separately from a real-life process and can even be developed without an existing process to test hypothesis. 

Enabling technologies for digital twins and simulations 

To build and run a digital twin, several technology blocks are required. 

  1. A simulation engine 
  2. Real-time process data collection technologies 
  3. Cloud and data services to collect, store and analyze the process and simulation data 

How do you build a simulation? 

Digital twin vs. simulations: the quick cheat sheet

There are multiple ways to build a simulator, and the three most used are: 

  • Physics-based simulators
  • Software package-based simulators using products such as AnyLogic or Simulink 
  • AI or data-based simulators that train AI models, most often deep neural networks-based ones  

For the latter approach, data-logging only digital twins can be used to create the dataset necessary to train this AI simulator. The historical process data (both inputs, states, and outputs) that the digital twin recorded can provide the breadth and quantity of labeled data required for those types of AI simulators’ supervised learning. This is one of the areas where a partner with extensive data science experience can significantly help with the speed and quality of the simulation development.

Additional enabling technologies  

Digital twin vs. simulations: the quick cheat sheet

To collect real-time process data, smart sensors using technologies such as Azure IoT are going to be required. Adding intelligence at the edge to existing sensors or deploying new smart sensors such as vision AI ones, we will be able to instrument all the relevant process inputs and outputs. 

This real-time data and the simulator(s) will be hosted on an appropriate cloud platform to enable the above-mentioned use cases. Solutions such as Azure Digital Twin will enable easy integration of those elements and access to device, process, line (or building), or plant (or campus) digital twins. 

“IoT is inextricably linked with digital twins. To create a comprehensive digital twin of a manufacturing environment, one must connect every major process on a manufacturing floor to IoT for process digitization, modeling, and simulation”Mohammad Ahmed, Azure Infrastructure Specialist, Microsoft 

Although this article somewhat oversimplifies both what digital twins are and what is required to build and run them, it provides a base for more in-depth research if a more comprehensive understanding of the subject is required. To help with this additional research, we listed a few links below to get you started.   

Also, as Fractal possesses the end-to-end technological capabilities required to instrument, build, train, deploy, and maintain digital twins, feel free to contact us, if you are interested in learning more about this topic.  

 

Additional resources on digital twins: 

GenAI for field technicians and engineers

Field technicians and engineers are the backbone of various industries, including manufacturing, energy, telecom, and more. They play a crucial role in installing, repairing, maintaining, inspecting, and troubleshooting equipment and systems — often in challenging environments.

One of the most significant pain points they encounter is the difficulty of accessing essential information from thick manuals and field service guides while working in the field. Advances in Generative AI (or GenAI), however, can revolutionize the way field technicians and engineers access information and streamline their daily tasks.

Common challenges faced by field technicians

Field technicians and engineers encounter a lot of challenges daily in their line of work. These challenges vary depending on the industry and environment they operate in, but they include:

  • Complex tasks: Field technicians often deal with intricate machinery and systems that require specialized knowledge and expertise to operate, maintain, and repair.
  • Constantly changing environments: Whether it’s working in remote locations, extreme weather conditions, or high-pressure situations, field technicians must adapt to diverse and unpredictable work environments.
  • Information overload: Technicians are frequently required to consult technical manuals, documentation, and safety guidelines to perform their tasks accurately. The sheer volume of information can be overwhelming.  
  • Time constraints: Efficiency is crucial, and technicians must perform tasks swiftly without compromising safety or quality.
  • Customer expectations: Meeting customer expectations for prompt service and problem resolution is paramount for customer satisfaction and loyalty.
  • Wide range of field technicians’ experience levels: While experienced technicians may be able to quickly find the relevant information, more junior ones can struggle to effectively find it.

How GenAI can help

Recent advances in GenAI offer natural language understanding and generation capabilities, making it an ideal solution for field technicians and engineers. It enables them to use voice and text to ask questions in plain English and receive well-articulated answers grounded in products, systems, and processes documentation.

GenAI for field technicians and engineers

Here are some examples of questions that GenAI could reply to:

  • How do I replace the filter of machine model XYZ?
  • What is the safety process for operation X?
  • What are the potential causes of error code 1234?
  • How do I calibrate sensor model XYZ?

Benefits of using GenAI for field service work

GenAI can help both transform the worker experience and deliver immense value for the customer. Here we discuss a few benefits Gen AI delivers:

benefits of using GenAI

  • Efficiency: GenAI reduces the need to search and browse through lengthy and complex manuals on small screens, allowing technicians to find information more quickly and efficiently.
  • Accuracy: GenAI provides instant and relevant answers based on the latest approved documentation, minimizing the risk of errors and accidents.
  • Convenience: With hands-free and voice-based interaction, technicians can access information without the need to type or tap on mobile devices, enhancing convenience especially in scenarios where typing is not the best option: gloves, greasy hands, etc.
  • Customer satisfaction: Faster and more reliable service delivery translates to improved customer satisfaction, leading to increased trust and loyalty.

FractalGPT for field service work

FractalGPT is a Google Cloud Vertex AI-GenAI so called “LLM”-powered AI chatbot solution that enables field service technicians to quickly find accurate information from company products and process documentation using a natural language interface. It helps enhance the productivity and efficiency of field technicians and engineers.

Some key features and benefits are:

key features and benefits

  • Device compatibility: FractalGPT is accessible on any device, whether it’s a phone, tablet, or laptop, via its responsive web app running on your private cloud tenant.
  • Customization: Tailor FractalGPT to your company’s machines, processes, and policies, to ensure it meets your specific needs.
  • Content breath: FractalGPT can access and analyze any type of documentation, including manuals, guides, datasheets, and more.
  • Contextual answers: It generates precise answers that are tailored to the context and user preferences, improving the user experience.
  • Complex query handling: FractalGPT can handle complex, multi-step queries that require reasoning and inference, ensuring that technicians get the information they need to complete tasks successfully.

    Get started with FractalGPT

    In today’s fast-paced world, field technicians and engineers need efficient solutions to overcome the challenges they face daily. FractalGPT offers a transformative approach to addressing these challenges.

    It is designed to be secure, scalable, and easy to use, making it ideal for businesses of all sizes and across industries. With FractalGPT, your teams can improve productivity while ensuring your data remains secure.

    Leveraging our partnership with Google Cloud and decades of AI expertise, Fractal can swiftly and efficiently assist you in implementing your Generative AI solution using FractalGPT.

    Take the first step in improving your field service operations by deploying your custom-branded FractalGPT in a week.

    Contact us to learn more about FractalGPT.