The idea of a digital twin is to address real-world problems by rendering the physical world visible within a digital environment. Three key technologies are essential for creating a digital twin:

1. Virtualization: This involves modeling the physical world in a virtual environment.
2. Synchronization: It entails the real-time reflection of changing data from the real world.
3. Simulation: This component uses the virtual model and real-time data to generate the optimal solution for the problem at hand.

A common real-world example of a digital twin is car navigation. In this case, the road network is modeled in a virtual environment, and real-time data, such as your car’s location and surrounding traffic, is synchronized and then simulated to assist in finding the best route to your destination.

Digital twins on the manufacturing floor also aim to address real-world problems by “visualizing” the physical site. This includes the virtual modeling of workers and robots moving within existing equipment or space, as previously mentioned, and providing real-time visibility into equipment operation, inspections, and detecting defects or failures. In essence, this process begins with the collection of all data on the manufacturing floor.

To implement a smart factory digital twin, the first step is to establish an environment for collecting and seamlessly connecting all data from the manufacturing floor. Once you have a real-time view of everything in the factory within a virtual digital space, industrial AI can be used to analyze the data, predict failures in advance (predictive maintenance), streamline processes, and achieve advanced smart manufacturing.

Data CAMP simplifies the initiation of this process. Data CAMP is an integrated solution for collecting, analyzing, monitoring, and predicting data from various equipment and sensors on the manufacturing floor.

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Advantages of Data CAMP for Flexible and Easy Data Management

[Data Collection]

Gather all data simultaneously using Data CAMP, which is compatible with a range of industrial protocols

Collecting data is no simple task. Thanks to advancements in industrial AI, machine learning, and the Industrial Internet of Things (IIoT), manufacturing sites are already generating tens to hundreds of terabytes of data per day. Nevertheless, many of the devices in the manufacturing process employ diverse communication protocols, necessitating the development of different interfaces to unify this data.

Fortunately, Data CAMP accommodates most of the communication protocols used by industrial shop floor equipment. This allows you to gather a wide array of data simultaneously, including inspection images, inspection results, CCTV footage, acoustic data, vibration data, PLC data, and more.

Furthermore, Data CAMP supports various output options. It can generate multiple files, maintain its own database, and seamlessly integrate with other systems.For instance, it features protocols for connecting with various higher-level systems, allowing you to reprocess and transmit data conveniently.

[Preprocessing]

Effortlessly transform diverse data formats without the need for intricate coding

Data generated by different machines often comes in varying formats. To gain a comprehensive overview of your factory’s status, you would typically need to manually convert and combine these formats, which are often incompatible.

Data CAMP offers a data preprocessing feature that automatically converts data into various formats of your choice and delivers them to you. Most importantly, you can easily configure these settings to your desired format and conditions using a user-friendly interface without complex coding or additional software development.

The “Low-Code/No-Code” operation means that even engineers with expertise in manufacturing processes but limited familiarity with data collection and analysis, can effectively employ Data CAMP to achieve digital transformation on the manufacturing floor.

• Low-code refers to a development platform designed to simplify software development by allowing users to create applications through ‘dragging and dropping’ features with some data management and backend knowledge. No-Code, on the other hand, is a platform that enables application development using provided templates, eliminating the need for any coding expertise.

Low-code/no-code operation with a simple UI-UX also ensures the reliability of the application. Human coding increases the probability of mistakes (human errors), which can have a cascading effect on equipment on the manufacturing floor and cause the entire system to stop. However, this is unlikely to happen in Data CAMP because you only need to change the settings through pre-made recipe files, so there is less chance of human error. Even if the recipe file is miswritten, it will only cause some pipelines to stop working and won’t have a cascading effect on other systems.

[Monitoring]

Access a web-based overview of your factory’s status from anywhere, anytime.

Traditional manufacturing sites typically store data generated by various pieces of equipment inside each machine. This meant that when a problem occurred with a piece of equipment, an engineer had to physically access and open the data stored inside the equipment to diagnose the cause of the problem. This cumbersome process made it challenging to respond swiftly to all situations in a vast factory with limited manpower.

Now, with Data CAMP, you can easily view and respond to all data on the production floor at a glance, web-based, and without time and location constraints. You can monitor the facility status by using an inspection device and process it in real -time on one screen. Furthermore, you can access data for a specific number of product units (lots) or individual production units and review the summarized status.

[Analytics]

Empower your business through the utilization of data analysis features

Collecting data is futile unless you utilize it for your business. Data CAMP processes the collected data using statistical process control (SPC) analysis techniques to identify abnormalities in products and facilities. It detects trends in the data to pinpoint irregularities and displays their location and trend of occurrence. You can also assess your processes’ efficiency and measure your current equipment’s quality level.

For instance, you can evaluate the quality of optical images being captured with a machine-learning artificial intelligence model trained on existing data (Image Quality Analysis). If a camera on the manufacturing floor fails to capture the right angle or if image focus and brightness change due to aging lighting, Data CAMP triggers an alarm so that an on-site engineer can take the necessary action.

The user-friendly structure of these analytics makes them very easy to use—simply utilize the ‘Rule Manager’ to access the analytics you need. Data CAMP stores structured data in a format preset by the user and offers query, statistical analysis, and visualization functions for that data.

[Real-time Anomaly Detection]

Enhance quality control seamlessly by incorporating LISA Solution

Data CAMP collaborates with LISA to identify anomalies in video, optical images, and time series data in real-time. We select the most appropriate deep learning algorithm following a comprehensive consultation with the customer.

Our consulting process includes the following steps:

1. Data Collection: We determine the data collection method and frequency in our initial discussions with the customer.
2. Data Preprocessing and Verification: We preprocess the collected data to make it usable and ensure that it is being collected normally through various means, such as charts.
3. Select Data to Use: We identify the data that aligns with your specific objectives from the collected data.
4. Training and Deployment: From the chosen data, we find a suitable model, train it, and deploy it effectively.
5. Monitoring: We continually monitor the deployed model’s behavior and verify the anomaly detection’s effectiveness.

Over the past 3 years, Data CAMP Has Never Experienced Downtime

Data CAMP is developed using Rust, a programming language known for its exceptional speed, akin to C, and for its capacity to facilitate memory ownership checks and safe parallel programming.

1. Memory Ownership Stability

Rust conducts comprehensive memory ownership checks during compilation, eliminating the possibility of memory leaks. Memory issues are a significant concern for factory applications. For example, failing to release allocated memory for an internal application problem can gradually consume more memory over time. Eventually, the system may reach a point where it cannot allocate any more memory, causing system freezes.

On a manufacturing floor that operates around the clock, minimizing downtime is crucial, making the resolution of memory leaks in applications a top priority. This concern will only become more critical as manufacturing sites continue to produce tens of terabytes of data daily with advancing technology.

2. Error-Free Parallel Processing

Efficiently processing data from various machines on a manufacturing floor simultaneously requires “parallel processing.” Parallel processing entails dividing a program’s computational workload into multiple parts and executing calculations for each part on multiple processors concurrently, a practice known as parallel programming.

As computers increasingly incorporate multiple processors, the significance of parallel programming—running different segments of a program concurrently—grows. Historically, implementing parallel programming has been challenging and prone to errors.

On the other hand, Rust excels at making the implementation of parallel programming safe and straightforward. Memory ownership and type system checks convert elusive bugs in parallel programming into compile-time errors, rather than run-time errors. This feature allows developers to rectify issues during the programming phase, rather than discovering and addressing them post-deployment.

Consequently, Data CAMP’s reliability has been firmly confirmed. Throughout three years of operation spanning 30 production lines at the secondary battery plant, there hasn’t been a single instance of shutdown caused by issues within the Data CAMP application.

Meet the Cloud-Based New Data CAMP with Enhanced Security

In 2024, Data CAMP is set to undergo a transformation into a more secure cloud service. Data collected and preprocessed from various factory equipment will be transmitted to the cloud, allowing you to create and customize dashboards. This means you can oversee and improve your processes in real-time, no matter where you are, whether it’s at your main office or an international branch.

Security is also a top priority, with the implementation of self-securing features, including homomorphic password encoding.

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Reference

• Digital Twin
• Artificial intelligence in industry
• Smart manufacturing
• Artificial intelligence in industry
• Machine learning
• How is the Industrial Internet of Things (IIoT) different from the Internet of Things (IoT)?
• Communication protocol
• Low-code development platform
• No-code development platform
• Statistical process control
• Rust
• Compiler
• What Is Ownership?
• Fearless Concurrency
• Cloud computing
• Homomorphic encryption
• Federated learning