Learn More About Digital Twin Software
What is Digital Twin Software?
In the manufacturing sector, operations often rely on elaborate machine systems. This equipment can perform any number of complex tasks, but also requires constant maintenance, monitoring, and improvements to ensure consistent output. Traditionally, inspections have been done manually, with sizable room for error and oversight. Now, thanks to internet of things (IoT) technology, engineers and factory managers can streamline these processes and continuously optimize machines without the need for rigorous physical inspections. Digital twin software is an advanced form of simulation software, using sensor data from embedded machines to create accurate, real-time simulations of assets. These virtual models allow for intelligent structural assessments and analyses, with actionable insights into machine performance and maintenance needs.
Using digital twin technology, manufacturing teams can design true-to-life virtual representations of their most vital assets and, in some cases, complete systems or factory floors. These products may also have use cases outside of manufacturing, such as in health care and utilities. With these interactive visual twin models, users can compare their initial designs with the actual performance of distributed assets. Some products help to create alerts when anomalies are detected by embedded sensors (e.g., unexpected downtime, overheating) or when components are approaching their scheduled maintenance. Users can conveniently pinpoint the problem areas in large, complicated machines on virtual models, and administer predictive maintenance on the actual assets without the guesswork. Throughout the asset lifecycle, teams can utilize digital twin technology for maximum process optimization, while minimizing costs, waste, and disturbances. The digital twin concept is rapidly transforming factory operations across the globe, and the products in the category empower business teams to realize the benefits in their own operations.
Key Benefits of Digital Twin Software
- Detect equipment issues remotely and proactively
- Minimize downtime and costly process failures
- Increase production yield and overall supply chain outcomes
- Make informed decisions toward design and engineering of machines
Why Use Digital Twin Software?
Digital twin platforms help modernize global manufacturing operations, with unprecedented insights into industrial equipment processes and their many physical components. The digital models made possible with these solutions can help factory workers and supervisors visualize the inner workings of machines and machine parts, with real-time data about asset health, performance, and conditions. In some cases, digital twin technology allows users to simulate events in the manufacturing process (e.g., new connections between machines, updated machine processes and data flows), so they can determine the best course of action before implementing actual changes. Products may offer visual analytics related to these simulations, turning machines and their smallest components into powerful data sources and helping users understand their equipment in ways not previously possible.
Predictive analytics—such as those made possible by predictive analytics software—are a key benefit of digital twin technology. Sudden machine failures can result in substantial losses, as well as delays in production and employee frustrations. The various analytics made possible through these solutions allow users to anticipate the litany of issues that might arise with machine systems and devices, while monitoring overall performance metrics to shape continuous improvements toward processes and designs. Data generated on these products might also assist with predictive waste reduction and predictive quality control of completed products, as well as general optimization of resources. Additionally, if any machine or process issues are identified, users can investigate their root causes by exploring virtual models and the data gathered across embedded sensors. This can help manufacturing teams make lasting improvements wherever possible, rather than simply patching up the symptoms and side effects of serious foundational problems. All in all, digital twin technology can give engineers and factory managers a newfound level of control over their distributed assets, helping to protect these critical investments and ensure consistent, optimal production without the unexpected surprises.
Who Uses Digital Twin Software?
Digital twin technology is primarily used in the manufacturing industry, or those businesses that utilize robust production equipment to convert raw materials into finished products or parts. With that being said, this technology is finding new use cases in a number of other industries with complex distributed assets, where virtual simulations may come in handy for prototyping or predictive asset data. As this technology becomes more prevalent, it may be integrated into the business models for additional industries while further cementing its place in manufacturing operations both big and small.
The following are some specific roles or teams that may benefit most directly from digital twin technology in their daily job functions.
Mechatronics engineers — The rise of smart technology has created a strong demand for industry specialists that can apply these modern principles to the design and deployment of industrial systems. Mechatronics engineering is a fast-growing specialization for developing the types of intelligent manufacturing systems that are compatible with digital twin technology. There are a variety of specific job titles that may be included in the field of mechatronics engineers or that may be tasked with similar responsibilities, such as senior design engineer, equipment engineer, and automation specialist.
Machinists — Machinists are often assigned to set up, operate, and maintain computer and mechanically operated machines that produce parts used in manufacturing. As industry equipment and devices evolve, so do the expertise and job functions of machinists and machine operators. Digital twin technology is increasingly used by these specialists to help prototype machine designs and processes, as well as maintain these systems, across a wide spectrum of industries. Job titles that may fall under the umbrella of machinists include mechanical technician, industrial engineering technician, and plant operator. These roles are also commonplace in highly technical industries such as aerospace. Machinists in these advanced roles or standard manufacturing operations may leverage digital twin technology to gain high-level visibility into their machines and proactively maintain them using data from embedded sensors.
Production managers — Production managers and supervisors oversee the daily operations of manufacturing plants, from managing workers to ensuring speedy repairs related to production problems. Digital twin technology can provide these individuals with timely operational data related to critical assets, so they can make informed decisions about equipment maintenance and process flows. The products in this category may become invaluable tools for production managers of operations utilizing smart technology or those planning to integrate this technology across physical assets. Some specific job titles that fall under the umbrella of production managers might include manufacturing process engineer and assembly supervisor. For many factory operations, supervisory teams hold a great deal of responsibility with regard to machine upkeep and optimization. Digital twin platforms can empower these individuals with the most timely and specific insights into smart machinery so they can ensure the operation runs smoothly and produces at the highest possible level.
Digital Twin Software Features
The products in this category offer diverse capabilities related to the design and analysis of virtual twins, and require the implementation of IoT sensors within those physical assets being replicated. A number of digital twin products offer additional capabilities related to smart technology deployments, and digital twin features may be included as part of a larger solution. The following are some common features in this emerging technology space.
Remote asset monitoring — IoT device management software enables real-time visibility into all connected assets, from their overall status to their performance and battery health. For complex industrial machinery, this data may not be sufficient for the full extent of monitoring and maintenance needs. Digital twins of these assets take remote monitoring a step further, with detailed visualizations of machinery and the many critical components inside of them. Machine operators and floor managers can leverage these products for deep visibility into equipment health, resource usage, and overall equipment effectiveness (OEE). These solutions may also provide logs of abnormalities and incidents related to individual parts or machine systems as a whole, with interactive models for easy reference of the parts in question.
Predictive maintenance and analytics — Thanks to the real-time feedback from intelligent sensors, product behavior and usage data feeds into digital models of manufacturing equipment, along with the conditions affecting these assets. This allows for remote diagnostics and proactive maintenance decisions before sudden downtime or critical damage occurs. Depending on the product, users may be able to configure custom alerts and analytics for each machine and its corresponding virtual replica. Not only can this help with proactive repairs, but also continuous process and machine optimization to help improve results and reduce the chances of disturbances.
IoT prototyping — Certain digital twin platforms can offer assistance with computer-aided design (CAD) for prototypes of machines and machine updates involving IoT capabilities. As smart technology comes to the forefront of manufacturing strategies, it can be a precarious process to test and deploy these capabilities without disrupting production or causing issues with equipment. Using digital twin technology, engineers may be able to simulate these developments and validate the functionality of concepts through fully operational virtual prototypes of individual machines and systems of machines. This can help manufacturing teams move forward with confidence in their physical deployments, with an accurate sense of what the outcomes may be.
Event simulations — Along the lines of IoT prototyping, users of digital twin technology may desire to test-run different events that may occur with manufacturing equipment. A number of solutions include machine learning and/or artificial intelligence technology that informs simulations of these events, based on previous events and related analytics. How would a machine respond when a nearby machine shuts down, or when a component breaks or overheats? Simulations of these events using digital twins can help manufacturing teams prepare for such scenarios, and proactively adjust processes if certain simulations reveal less desirable outcomes. Conversely, if simulations lead to positive results, it may instill confidence in continuing certain processes or making planned changes to equipment and workflows. Virtual simulations can allow for a variety of unique tests and insights without the need for disrupting operations and risking unwanted outcomes.
Software and Services Related to Digital Twin Software
The following solutions may be useful for those manufacturing teams considering digital twin technology in their business, and may offer capabilities similar to those seen in these state-of-the-art solutions or complement these platforms in some way.
Product and machine design software — Product and machine design software is a subset of CAD software built specificially for modeling of parts, components, and assemblies, such as those seen in manufacturing processes. These 3D models are similar to those created on digital twin platforms. Well before factory machines are integrated into an IoT environment, manufacturers and equipment suppliers may utilize product and machine design tools to create blueprints of these machines, their containing parts, and the products they will eventually help create. These solutions may integrate with digital twin platforms to allow for importing or exporting of asset models.
Computer-aided manufacturing software — Computer-aided manufacturing software, or CAM software, helps manufacturing teams program their machines to work as intended on their shop floors. Factory machines may perform any number of repeating processes, such as cutting, milling, or roughing of materials. CAM tools allow users to program these production operations using existing CAD models of the machines in question, then deliver this workflow information to the machines as needed. In some cases, these tools offer machine process monitoring and/or optimization features that may complement the functionality of digital twin technology.
Manufacturing execution systems — Manufacturing execution system software, or MES software, assists manufacturers with monitoring the production process using real-time data from across shop floor equipment and other data sources. These tools may allow for the creation of production plans and schedules along with the allocation of resources, both human and material. Production managers and supervisors can use these platforms to gain visibility over production output and efficiency, so they can adjust strategies and processes as well as address any issues as they arise. The production data and insights that are generated within these platforms can complement the machine insights offered by digital twin platforms, allowing for the most comprehensive monitoring of manufacturing operations and the many resources involved.
IoT analytics software — Once factory equipment and other assets are enabled with smart technology, it can allow for an unprecedented level of data collection, with observations about work processes, environmental conditions, machine performance, and more. IoT analytics software helps to make sense of this constant flow of data, both structured and unstructured. A primary example of these capabilities is drawing actionable insights from time series data, which can help users predict future outcomes of embedded machinery and their surrounding environments and make informed decisions to optimize conditions and performance. The tools in this category may integrate directly with digital twin platforms to create a seamless flow of data and allow for the most accurate analysis of distributed assets and processes. Certain platforms may offer features of both IoT analytics software and digital twin software.
