Automation ML

A markup language is a way to structure and describe data using tags — human-readable keywords enclosed in angle brackets (like , , ). This format lets computers understand not just the data, but the meaning of that data.

One widely used markup language is XML (Extensible Markup Language). Here’s how XML might describe an industrial device:

<Device>
  <Name>PumpStation01</Name>
  <Voltage>400V</Voltage>
</Device>


This tells us there’s a device called “PumpStation01” that operates on 400 volts. This structured format is readable by both humans and software tools — ideal for sharing data across engineering platforms.

What is AutomationML?

AutomationML (Automation Markup Language) is a specialized markup language built on XML, specifically designed for industrial automation systems. It serves as a neutral digital backbone, allowing seamless data exchange between mechanical design tools (e.g., SolidWorks), electrical CAD tools (e.g., EPLAN), PLC platforms (e.g., Siemens TIA, Rockwell Studio 5000), and even simulation or SCADA systems.

Here’s what an AutomationML snippet might look like:

<InstanceHierarchy Name="Plant1">
  <InternalElement Name="Motor_001" Role="Motor">
    <Attribute Name="PowerRating" Value="5.5kW"/>
    <Attribute Name="Location" Value="ZoneA1"/>
  </InternalElement>
</InstanceHierarchy>

This snippet describes a motor object inside “Plant1” with a power rating and location. The key power of AutomationML is that this single model can be shared across all engineering domains — electrical, mechanical, automation, and SCADA — without redundant manual entry.

AutomationML: Unifying Industrial Automation Data

Modern industrial automation projects involve complex, multi-disciplinary engineering: mechanical design, electrical schematics, PLC programming, HMI/SCADA configuration, and more. Traditionally, each discipline uses its own tool and data format, leading to silos and manual hand-overs. Automation Markup Language (AutomationML) is an open, XML-based standard designed to break these silos. It provides a neutral data model for plant engineering data, enabling lossless, bidirectional exchange of design information across tools and disciplines. By leveraging existing standards (IEC 62714 for AML, CAEX for hierarchy, COLLADA for geometry, PLCopen XML for logic, etc.), AutomationML lets engineers represent complex systems in a unified format. In practice, this means a mechanical CAD assembly, an electrical cable plan, and a PLC I/O layout can be linked together in a coherent digital model, paving the way for true Industry 4.0 integration.

  • Neutral, XML-based standard: AML is a vendor-independent, open standard (IEC 62714) for encoding engineering data. Tools can import/export AML files without licensing restrictions.
  • Multi-domain modeling: AML supports hierarchical object topologies (via CAEX/IEC 62424), 3D geometries (COLLADA), and behavioral logic (PLCopen XML). Each object can reference multiple aspects (mechanical, electrical, control) for rich cross-domain models.
  • Industry-neutral: It’s proven in discrete and process industries alike. Practical cases exist in automotive, manufacturing, food & beverage, water/wastewater treatment and more. For example, process plants (F&B, chemicals, water) benefit by synchronizing process flow, instrumentation, and control data, while discrete manufacturing lines gain from coordinating mechanical assemblies with PLC logic.
  • Reduced engineering effort: Studies show manual coordination is >50% of project costs. AML automates data hand-offs, cutting errors and rework. By ensuring “a seamless flow of information between different systems,” AML shortens lead times and lowers integration costs.

How AutomationML Works

AutomationML organizes data into a multi-layer model. At the bottom, individual devices or components are defined; higher levels group these into functional units and complete systems. Key concepts include RoleClasses and InterfaceClasses that semantically describe components and their relationships, enabling consistent interpretation across tools. All data is stored in interoperable XML files: a CAEX file describes the object hierarchy, which may reference additional XML files for 3D geometry, kinematics, or PLC logic. This lean, distributed architecture keeps the AML core small (just 32 pages in IEC 62714-1) by reusing proven formats.

By aligning these domains, AML ensures lossless data exchange: for instance, a robot assembly’s CAD data, its mounting positions (geometry), and its control signals (PLC tags) can all be interlinked. This holistic model then flows through the project lifecycle – from initial design to commissioning and maintenance – without information gaps. As Mitsubishi Electric noted, using AML in its iQ Works software “ensures a seamless flow of information between different systems,” driving faster time-to-market.

Key Benefits for Automation Projects

AutomationML offers tangible advantages to project owners and integrators:

  • Interoperability & Vendor Independence: AML works with any tool that implements the standard. Leading vendors are adopting it (Siemens, Mitsubishi, Rockwell, etc.), so you’re not locked into one ecosystem. For example, Siemens TIA Portal can export hardware configs as AML, and Mitsubishi’s iQ Works can exchange I/O tag and network data with EPLAN via AML. This neutrality eliminates tedious manual conversions or proprietary import tools.
  • Faster Engineering Cycles: By automating data exchange, AML drastically cuts manual re-entry. Engineers spend less time retyping parts lists, wiring assignments or PLC data, reducing errors. As one brochure notes, “manual exchange of information… is time consuming and introduces the possibility of errors” – AML addresses this by a universal interface. The result is shorter design iterations and quicker commissioning.
  • Cost and Time Savings: When each discipline uses a common digital language, consistency improves and waste (redoing work, fixing mis-alignments) is eliminated. Companies report that consistent data through the lifecycle “improves quality and reduces costs”. Across industries, AML deployment has demonstrated faster time-to-market, which translates into competitive advantage.
  • Future-Proof, Industry 4.0 Ready: AML underpins digital twins and advanced analytics. It complements standards like OPC UA (see below) and the Asset Administration Shell. With AML, you build a robust digital backbone – new modules or reconfigurations simply extend existing models, rather than redoing work.

Implementing AutomationML Interfaces

In our projects, we follow best practices to integrate AML seamlessly:

  1. Analyze Data Models: Identify the engineering data to exchange (e.g. PLC I/O tags, electrical wiring, mechanical assemblies). Compare tool data structures and find their overlap. This ensures we target exactly the needed information.
  2. Define AML Role/Interface Classes: Map these data elements into AML using existing standard role and interface classes. If gaps exist, we create custom classes in supplemental libraries. The goal is a one-to-one mapping so nothing is lost.
  3. Develop Import/Export Interfaces: We typically implement two complementary patterns: an AML export generator (extracts data from a tool into AML) and an AML import interpreter (reads AML into another tool). We ensure the import side understands the role classes used, with fallback strategies if some classes are unknown. This way, engineering data moves bidirectionally without manual edits.
  4. Validate & Iterate: We test round-trip exchanges to catch mismatches early. Roleclass mappings are refined and, if needed, we work with vendors or the AutomationML association to refine libraries.

By handling these steps, outsourcing clients gain a smooth AML deployment. As Mitsubishi’s engineers observed, implementing this “universal interface” across their toolchain helped “fulfills today’s requirements to shorten time to market.”.

Integration with Leading Platforms

AutomationML is not a niche technology – it ties into all major industrial automation platforms:

  • Siemens TIA Portal & Selection Tools: TIA Portal’s Openness API supports AML export/import to share hardware configurations and I/O data with other disciplines. For example, a TIA-based PLC and mechanical layout in Solid Edge can be synchronized via AML. This ensures electrical and mechanical designs are “always synchronized”, improving consistency.
  • Rockwell Automation & EPLAN: Rockwell Studio 5000 integrates AML through partnerships. In practice, engineering teams use AML to exchange PLC tag and I/O information with EPLAN Electric P8, eliminating manual cross-checks. (For instance, Mitsubishi Electric’s collaboration with EPLAN demonstrates this use case.)
  • OPC UA Connectivity: AML and OPC UA form a powerful combo. The AML model can be published via OPC UA for runtime access and versioning. Conversely, OPC UA system configurations (endpoints, security, network settings) can be imported into AML models. This means a PLC’s live data (via OPC UA) can be tied back to the standardized engineering model, supporting digital twin applications and multi-user access.
  • Microsoft Excel & Reporting: Although AML is XML, its structured data can be easily converted for reporting or analysis. Engineers often export AML-based tag lists or BOMs to CSV/Excel, enabling custom dashboards or sharing with non-engineers. This hybrid approach lets project managers view key data in familiar tools while maintaining AML as the single source of truth.

Additionally, AML complements industry standards: it works alongside PLCopen (IEC 61131 programming), ISA‑95/B2MML (manufacturing operations), eCl@ss (part classification), and more. Our implementations ensure AML fits into each client’s tech stack rather than forcing rip-and-replace of existing systems.

Applications in Food & Beverage, Energy, Water

My teams have applied AutomationML in diverse sectors:

  • Food & Beverage: These plants feature multi-vendor processing lines (fillers, mixers, conveyors) with stringent hygiene and throughput requirements. Using AML, we’ve unified mechanical designs of conveyors (from CAD), electrical layouts, and PLC recipes. This holistic engineering model enabled rapid line changes for new products. For example, integrating a new filling machine involved importing its 3D geometry and tag data via AML, slashing integration time and avoiding production delays.
  • Energy Storage & Battery Manufacturing: Battery lines combine chemical processes with robotics and precise control. In projects, AML was used to synchronize cell assembly machinery with control software. All equipment parameters (conveyor layouts, chiller connections, PLC I/O) were linked in AML, simplifying commissioning and later firmware updates across the line.
  • Water & Wastewater Treatment: Control systems in treatment plants span PLCs, sensors, and SCADA displays. AML models have captured entire pump stations – turbine specs, valve configurations, and PLC logic – so that upgrades (e.g. adding new sensors) propagate correctly through SCADA screens and maintenance documentation. The result is a resilient digital twin of the plant, improving reliability.

These cases demonstrate that regardless of industry, a standardized data backbone pays off. The AutomationML Association notes practical AML use in both “discrete manufacturing” and “process industry”, across companies large and small. By outsourcing their AML integration to us, clients gain from our broad cross-industry know-how and best practices.

Building Confidence in Your Automation Outsourcing

For engineering managers and system integrators, adopting AML means future-ready projects. It signals to stakeholders (engineers, OEMs, maintenance teams) that your plant is built on open, interoperable standards. In my experience, conveying this assurance is key: I emphasize that clients will own their engineering data without vendor lock-in. Citing industry examples and standards (as above) builds trust that we’re not just using buzzwords but proven technology.

When you engage our team for an automation project, you get:

  • Deep Expertise: Proven methods for defining AML schemas, developing tool interfaces, and validating complete digital models.
  • Vendor-Neutral Solutions: Seamless integration with Siemens, Rockwell, Mitsubishi, etc., as required by your equipment choices.
  • Smooth Data Handover: Whether migrating legacy projects or starting fresh, AML ensures your data stays consistent. Maintenance and future expansions become straightforward.
  • Transparency & Documentation: AML’s self-describing nature means every tag and connection is documented. We deliver comprehensive AML files so that after go-live, your staff can easily understand and extend the system.

In summary, adopting AutomationML under our guidance accelerates delivery and improves quality. It aligns with Industry 4.0 goals: clear data flow, reduced rework, and flexibility. By highlighting AML’s benefits and demonstrating our successful implementations, we build confidence that outsourcing your automation will yield a robust, efficient solution.

Our Expert Engineer could be contacted globally anytime.