Asset Administration Shell in practice

What is an Asset Administration Shell?

Industry 4.0 promises more efficient and sustainable manufacturing processes through digitalization. The foundation for this is a seamless, automatic exchange of information between systems and products. This is where the Asset Administration Shell (AAS) comes into play.

An Asset Administration Shell is a vendor-independent standard for describing digital twins. Basically, it is the digital representation of an asset; either a physical product or a virtual object (e.g., documents or software).

The AAS defines the appearance of the asset in the digital world. It describes which information of a device is relevant for communication and how this information is presented. This means the AAS can provide all important data about the asset in a standardized and automated way.

Let us take a look at a practical application to understand the benefits of an AAS:

Use case: AAS as enabler for new services

As part of the ESCOM research project, CONTACT Software collaborates with GMN Paul Müller Industrie GmbH & Co. KG to implement AAS-based component services. The family-run company manufactures motor spindles which are installed by its customers as components in metalworking machine tools and then resold.

Before the project began, GMN had already developed a new sensor technology. It enables deep insights into the behavior of a spindle and provides information on overall operation of the spindle system. The company wants to use this data to offer new, product-related services:

  • Certified commissioning: Before GMN ships its spindles, the components are put through a defined test cycle on the company’s in-house test bench. GMN uses the data from this reference cycle to ensure that motor spindles are installed and commissioned correctly at the customer’s facility.
  • Predictive services: Using the IDEA-4S sensor microelectronics, customers shall be able to continuously record and analyze operating data that provide insights into the availability and operation of the spindles. If necessary, the data can be shared with GMN, for example, for problem analysis. This saves valuable time until the machine is back up and running. In the future, GMN will be able to offer smart predictive services like predictive maintenance.

About GMN Paul Müller Industrie GmbH

GMN Paul Müller Industrie GmbH & Co. KG is a family-owned mechanical engineering company based in Nuremberg, Germany. It produces high-precision ball bearings, machine spindles, freewheel clutches, non-contact seals, and electric drives that are used in various industries. The company manufactures most of these components individually for its customers on site and sells its products via a global sales network.

How do we realize the new services?

To provide such services, companies must be able to access and analyze the sensor data of their machines. Furthermore, machines (or their components) must be enabled to communicate independently with other assets and systems on the shopfloor.

For both tasks, GMN uses CONTACT Elements for IoT. The modular software not only helps the company to record, document and evaluate the reference and usage data of their spindles. It also includes functions that enable users to create, fill and manage the AAS for an asset.

Background

During the implementation of services based on spindle operating data, GMN benefits from the cooperation with a customer. This company installs the spindles in processing machines that GMN uses to manufacture its own products. As a result, GMN can gather the operating data in-house and use it to improve the next generation of spindles.

What role does the AAS play?

For the components to exchange information in a standardized form, an AAS must be created for the spindle at item and serial number level. This is also done using CONTACT Elements for IoT. The new services are mapped in a so-called AAS metamodel. It serves as a “link” to the service offers.

AAS and submodels

The AAS of an Industry 4.0 component consists of one or more submodels that each contain a structured set of characteristics. These submodels are defined by the Industrial Digital Twin Association (IDTA), an initiative in which 113 organizations from research, industry and software (including CONTACT Software) collaborate to define AAS standards. A list of all currently published submodels is available at https://industrialdigitaltwin.org/en/content-hub/submodels.

In CONTACT Elements for IoT, GMN can populate the AAS submodels with little effort. The platform includes a widget developed as a prototype during the research project. It provides an overview of which submodels currently exist for the asset and which are available but not yet created. Through the frontend, users can jump directly to the REST node server and upload or download submodels (in AAS/JSON format).

During the implementation of data-driven service offerings, GMN focuses on the submodels

  • Time Series Data (e.g., semantic information about time series data)
  • Digital Nameplate (e. g., information about the product, the manufacturer’s name, as well as product name and family),
  • Contact Information (standardized metadata of an asset) and
  • Carbon Footprint (information about the carbon footprint of an asset)

Filling the submodels is simple. This is demonstrated by the module Time Series Data. During the reference run of a motor spindle on the in-house test bench, the time series data is recorded by CONTACT Elements for IoT. The platform automatically transfers this data to the AAS submodel of the motor spindle being tested. At the same time, the platform creates a document for the reference run. This allows GMN to track its validity at any time and make it available to external stakeholders.

New services on the horizon

Using Asset Administration Shells allows GMN to realize its service ideas. This currently concerns the commissioning service and automated quality assurance services.

By analyzing the spindle data, the company can identify outliers in the operating data and make suitable recommendations for action. For example, different vibration velocities indicate an incorrect installation of the spindle in the machine or that time-varying processes are occurring. The analysis can also be used to provide insights about anomalies in operating behavior.

Dashboards in CONTACT Elements for IoT increase transparency. They provide GMN with all relevant information about the spindles on the test bench, from 3D models to status data. This overview is extremely valuable, particularly for quality management.

An AAS in our software Elements for IoT.

Summarized

Asset Administration Shells are vendor-independent standards for describing digital twins. They are among the most important levers for implementing new Industry 4.0 business models, as they enable communication between assets, systems, and organizations. The example of GMN demonstrates the practical benefits of the AAS. The company uses it to design new, product-related services based on information from the AAS of its products. GMN can successively improve these services by continuously analyzing operating data in CONTACT Elements for IoT.

Digital authenticity: how to spot AI-generated content

In today’s digital age, we often question whether we can trust images, videos, or texts. Tracing the source of information is becoming more and more difficult. Generative AI accelerates the creation of content at an incredible pace. Images and audio files that once required a skilled artist can now be generated by AI models in a matter of seconds. Models like OpenAI’s Sora can even produce high-quality videos!

This technology offers both opportunities and risks. On the one hand, it speeds up creative processes, but on the other hand, it can be misused for malicious purposes, such as phishing attacks or creating deceptively real deepfake videos. So how can we ensure that the content shared online is genuine?

Digital watermarks: invisible protection for content

Digital watermarks are one solution that helps verify the origin of images, videos, or audio files. These patterns are invisible to the human eye but can be detected by algorithms even after minor changes, like compressing or cropping an image, and are difficult to remove. They are primarily used to protect copyright.

However, applying watermarks to text is way more difficult because text has less redundancy than pixels in images. A related method is to insert small but visible errors into the original content. Google Maps, for instance, uses this method with fictional streets – if these streets appear in a copy, it signals copyright infringement.

Digital signatures: security through cryptography

Digital signatures are based on asymmetric cryptography. This means that the content is signed with a private key that only the creator possesses. Others can verify the authenticity of the content using a public key. Even the smallest alteration to the content invalidates the signature, making it nearly impossible to forge. Digital signatures already ensure transparency in online communication, for example through the HTTPS protocol for secure browsing.

In a world where all digital content would be protected by signatures, the origin and authenticity of any piece of media could be easily verified. For example, you could confirm who took a photo, when, and where. An initiative pushing this forward is the Coalition for Content Provenance and Authenticity (C2PA), which is developing technical standards to apply digital signatures to media and document its origin. Unlike watermarks, signatures are not permanently embedded into the content itself and can be removed without altering the material. In an ideal scenario, everyone would use digital signatures – then, missing signatures would raise doubts about the trustworthiness of the content.

GenAI detectors: AI vs. AI

GenAI detectors provide another way to recognize generated content. AI models are algorithms that leave behind certain patterns, such as specific wording or sentence structures. Other AI models can detect these. Tools like GPTZero can already identify with high accuracy whether a text originates from a generative AI model like ChatGPT or Gemini. While these detectors are not perfect yet, they provide an initial indication.

What does this mean for users?

Of all the options, digital signatures offer the strongest protection because they work across all types of content and are based on cryptographic methods. It will be interesting to see if projects like C2PA can establish trusted standards. Still, different measures may be needed depending on the purpose of ensuring the trustworthiness of digital content.
In addition to technological solutions, critical thinking remains one of the best tools for navigating the information age. The amount of available information is constantly growing; therefore, it is important to critically question, verify, and be aware of the capabilities of generative AI models.

For a more comprehensive article, check out the CONTACT Research Blog.

Data migration to Cloud PLM systems

Challenges and best practices for successful data migration 

More and more companies are adopting cloud-based PLM systems to streamline their product development processes. Whether they are already using an on-premises PLM system and want to switch to a cloud solution or implementing a Cloud PLM system for the first time, one of the biggest challenges is the smooth and secure data migration.
How can this data be reliably transferred to the new system? In this blog post, we examine the challenges and best practices for successful data migration to Cloud PLM systems and offer tips on ensuring a smooth transition without data loss.

What challenges arise during data migration to Cloud PLM systems?

Migrating data to Cloud PLM systems, obstacles can present hurdles that complicate and delay the entire process:

  1. Data quality and consistency
    Legacy data is often incomplete or inconsistent. Missing attributes, invalid values, or duplicate records can hinder the migration process. Particularly with CAD models, missing files or broken references may prevent models from being imported completely
  2. Data scope and complexity
    Depending on the scope and complexity of the data being transferred, the migration process can be very time-consuming. Large datasets, such as entire version histories of CAD data or multi-level BOMs, require significant computing resources and can slow down the migration.
  3. Structural differences between systems
    Data structures in the new Cloud PLM system may differ from those in your legacy system. Attributes, data fields, or relationships between records may be organized differently, requiring data transformation or restructuring before import.
  4. Technical challenges
    Migrating data to a Cloud system brings specific technical issues. For example, along with ensuring file format compatibility, sufficient network bandwidth and data transfer rates must be guaranteed.
  5. Security and compliance requirements
    Strict security and compliance regulations must be followed when transferring sensitive data to the Cloud. Data must be encrypted during transport and storage, and data protection laws such as GDPR must be adhered to.

What key questions should you address before data migration?

Data migration is often underestimated, although it is one of the most critical tasks before a new PLM system goes live. You should address several key questions early to import your legacy data successfully.

First, determine which data objects will be transferred to the new system: Are you migrating CAD assemblies, parts and BOMs, office documents, or projects? It’s also essential to define the scope of the data: Do you want to migrate data from a specific project, a product, a specific company location, or the entire data archive?

You should also decide how much historical data you want to migrate. Do you want to transfer only the latest version or all versions, including the complete audit trail and engineering changes? These aspects are crucial as they influence the scope and complexity of the migration.

You should also carefully examine the content of the data itself. Consider whether all attribute values and CAD parameters are needed or if it’s sufficient to import only some of them. This is important to define which data should be stored in which objects and attributes in the target PLM system.

What makes data transfer with CIM Database Cloud so simple?

  1. User-friendly import tools
    The cloud-based PLM system CIM Database Cloud offers powerful, easy-to-use import tools specifically designed to simplify the migration process. They allow you a quick and efficient import of configuration data such as field selection values (e.g., dropdown fields), as well as PLM data such as CAD documents, parts, BOMs, office documents, projects, and requirement specifications.
  2. Support for various file formats
    CIM Database Cloud supports a wide range of file formats and data sources, making it easy to  import different data objects. These include Excel files, CAD formats, and the ReqIF format for requirement specifications.
  3. Automated validation processes
    CIM Database Cloud includes built-in validation mechanisms that help identify and correct potential errors during the import process. These functions automatically check whether the data is complete and consistent during import, contributing to high data quality.
  4. Iterative Migration Approach
    The platform supports an iterative migration approach, allowing you to import and test data step by step. This helps identify and resolve potential issues early on, without affecting the migration process. This approach reduces the risk of errors and accelerates data migration.
  5. Comprehensive Documentation and Support
    Alongside the migration process, CIM Database Cloud offers extensive documentation and tutorials. These contain clear instructions and examples on how to import and configure different data types. Additionally, customer success managers are available to assist if needed.

Conclusion

Data migration to cloud-based PLM systems is often fraught with many challenges. Successful data migration, therefore, requires careful planning, considering aspects such as data quality, scope, structural differences, and security requirements.
CIM Database Cloud enables you to efficiently migrate your PLM data and make your product development processes future-proof . With user-friendly import tools, support for various data formats, automated validation processes, and comprehensive documentation, companies can ensure the seamless and secure integration of their existing data. An iterative migration approach, combined with extensive preparation, minimizes risks and guarantees a smooth transition to the new system.