Monday, March 13, 2023

Digital Twin in Manufacturing Industry

The key success of Digital Manufacturing, offered by Industry 4.0, is the ability to optimize the current processes and improve the efficiencies. One of the ways we can achieve this goal is, using Digital Twin, a virtual replica of a physical object, process or system that simulate, analyze and optimize the performance.

What is a Digital Twin?
Digital Twin is a virtual model of a physical asset or process that is created using data from sensors, machines, and other sources. It is a complete digital representation of the physical asset that includes its geometry, structure, materials, and performance characteristics. The digital model is connected to the physical asset in real-time, allowing engineers and operators to monitor its behavior, predict its performance, optimize its operations, perform scenario planning, and simulate what-if analysis.

Elements of Digital Twin
There are three key elements attributed to any digital twin:

Digital Twin Modeling (Static): To develop a digital twin, it must be designed and modeled representing the physical asset or process. This requires talking to stakeholders in identifying parameters to be monitored, process to be optimized, and ease of use. A single model is developed to represent a unique parameter. Multiple twins can be instantiated by using a single model if the parameters are identical and similar. The outcome is a static digital twin artifact representing the physical asset.

Digital Twin Simulation (Dynamic): The static digital twin is made dynamic by integrating and wiring the live data from physical asset or using simulated data. Many intermediary components and steps are required to collect the data from the physical asset, consolidate, curate, contextualize, formulate, and feed them into the digital twin. A robust and fail-safe pipeline is required to continuously feed the data into the twin.

Digital Twin Experiments (Intelligent): The extent to which the complete potential of a digital twin can be utilized is determined by the methodology used to conduct experiments on it. This involves integrating Machine Learning and Optimization models with the digital twin, predicting the performance of the physical asset, optimizing the control parameters, and carrying out experiments using either real-time or simulated data.

How does Digital Twin work?
The Digital Twin technology works by collecting data from sensors, PLCs, IoTs, and other sources, such as production databases and historical records, to create a virtual model of the physical asset or process. The data is then processed and streamed into the digital twin. Machine learning algorithms and other analytical tools are integrated with the twin to harness the full power of the digital replica that closely mirrors the physical asset. The virtual model is continuously updated with real-time data, allowing engineers and operators to monitor its performance, optimize its operations, and control the process.

Benefits of Digital Twin for Manufacturing
Digital Twin technology can bring a host of benefits to a manufacturing company. Few of them are:

  1. Improved quality: Digital Twin technology allows manufacturers to simulate and optimize the process before or during the production. This helps to identify potential problems, eliminate quality issues, and reduce wastes.
  2. Increased productivity: Digital Twin technology can help optimize the product formulation and process control variables, identify and eliminate process inefficiencies, and maximize the product yield.
  3. Reduced downtime: Digital Twin technology can help in predicting equipment failures and maintenance needs, thereby reducing downtime and production losses.
  4. Lower costs: Digital Twin technology can help achieve lower production cost with the increased productivity, improved quality, and reduced plant downtime.
  5. Better visibility and collaboration: Digital Twin technology helps visualize the plant or process holistically, from remote, in near real-time or real-time. It helps executives, engineers, and operators to collaborate more effectively, resulting in better decision making and improved outcomes.

Implementing Digital Twins in Manufacturing
Every manufacturing company is different. Even within one manufacturing company, different categories of products may be produced at different facilities. So, it is important to analyze, plan, design, and implement digital twin based on the products being manufactured. To design and implement digital twins effectively, several steps must be taken:

  1. Define the objective: The first step is to identify the objective for using a digital twin for a particular plant or process. This could be to optimize a specific process, reduce downtime, or improve product quality. Defining the objective will help to focus the effort and ensure that the digital twin is used effectively.
  2. Identify the data sources: Digital twins rely on data to simulate the performance of equipment or processes. Therefore, it is essential to identify the data sources required to create an accurate digital twin. This is one of the most challenging parts since a manufacturing plant may have data collected and stored at Operational Technology (OT), Information technology (IT), and Cloud levels. Post identification, it is necessary to consolidate, curate, contextualize, and formulate the data so that it can readily be consumed by the digital twin.
  3. Choose the right software: There are many software solutions available for creating digital twins, and it is essential to choose the right one for the specific needs of the manufacturing company. Factors to consider include the level of detail required, the complexity of the process being simulated, and the ease of use of the software.
  4. Build the digital twin: Once the data sources have been identified, and the software has been selected, the next step is to build the digital twin. (Refer to the Elements of Digital twin.) This involves creating a virtual representation of the physical object or system, creating pipeline to stream the data, integrating analytical tools, and configuring the simulation to replicate the real-world performance accurately.
  5. Test and validate: Similar to software development, it is essential to test and validate the digital twin against the real-world performance., after building it. This will help to identify any discrepancies or gaps and ensure that the digital twin provides an accurate representation of the physical system.
  6. Monitor and optimize: Design, development, and implementation of a digital twin is not a one-time activity. After the implementation, it has to be closely and continuously monitored and reviewed for its performance with the physical system. Corrective actions have to be taken in case of any glitches or drifts. Besides that, the digital twin can be updated when the requirement changes, and enhanced when more data is made available.

Challenges in implementing Digital Twin
While Digital Twin technology is expected to revolutionize the manufacturing industry, there are several challenges that need to be addressed before it can be widely adopted. Some of the key challenges are:

  1. Data quality and availability: Digital Twin technology relies on accurate and reliable data to create a virtual model. Ensuring data quality and availability is a major challenge, especially in older facilities or with legacy equipment that may not have sensors or provisions to install them. This can happen because of merger of companies, acquisition of existing plants, or revamping of part of the plant. The effective implementation of digital twin demands a robust Data Strategy within the organization.
  2. Integration with existing systems: Integrating Digital Twin technology with existing systems, such as Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Relational Database Management System (RDBMS), and Cloud can be complex and require significant investment.
  3. Security and privacy: Digital Twin technology relies on data that may be sensitive or proprietary, such as formulation and ingredients data, custom-built algorithms, etc. Ensuring the security and privacy of the data and other digital assets are critical and requires robust cybersecurity measures.
  4. Skills and expertise: Digital Twin technology requires specialized skills and expertise, such as machine learning, optimization, development, and simulation. Identifying and employing the needed resources with the necessary skills can be significantly challenging and time-consuming.
  5. Cost: Implementing Digital Twin technology can be expensive, especially in older facilities with legacy equipment. It first requires retrofitting the equipment with necessary sensors, probes, camera, and IoT devices, without impacting the ongoing production process, collect and consolidate the data with other data sources.

Digital twins are becoming an essential tool for manufacturing companies to optimize processes, reduce downtime & waste, and maximize the production yield. By implementing digital twins effectively, manufacturers can gain a competitive advantage by improving efficiencies, reducing costs, and improving customer satisfaction. However, it is essential to follow the steps outlined above to ensure that digital twins provide accurate representations of the physical system and provide meaningful insights.

About the author
Socrates Krishnamurthy is a Principal Data Scientist working at The Kraft Heinz, a Consumer-Packaged Goods (CPG) company, building Bonsai Project and Digital Twins for its Manufacturing Plants and Supply Chain process. He is a well-experienced, tech-savvy guy with 35 years of experience in multi-national companies across the Globe. He has in-depth experience in Mechanical, Power Plant, Consumer and Investment Banking, Risk Architecture, P&C Insurance, and CPG industries.