Track 1: Design a Submodel Template
Asset Administration Shell (AAS) provides a standardized way to describe assets through submodel templates, ensuring clear and interoperable asset management. This track helps participants learn how to design submodel templates using AAS Package Explorer (AASPE) and apply them to real-world use cases. It also explores the use of AI to streamline template design and automate AAS generation, empowering developers to create more efficient and scalable asset models.
This developer-track focuses on below points:
- Submodel Template Standard Process and Design.
- Modelling of AAS using AASPE.
- Leveraging AI for Submodel Template Design and to Generate AAS.
This track is designed to familiarize you with the process of designing submodel templates and how to standardize them through the IDTA process. Additionally, you'll learn how to model submodel templates using the AAS Package Explorer and explore how to leverage AI for submodel template design and AAS generation.
AAS Architects, AAS Developers
- Laptop
- Installed AASX Package Explorer (You can install it from here)
Imagine a company that manufactures high-end packaging machines. The company aims to provide product information in a standardized, machine-readable format using the Asset Administration Shell (AAS). This digital representation of the packaging machines ensures consistency, interoperability within the company's digital ecosystem. The process begins by designing a submodel for these packaging machines, capturing key product information and specifications. Throughout this track, participants will take on the role of AAS developers within the company, tasked with designing submodels to ensure consistent and effective digital twin for the packaging machines.
- Open AAS Package Explorer (AASPE).
- Load the provided AAS template file:Template.aasx
- Use the information provided in the Developer_Track_submodel_Values.docx to fill in the necessary values of the SME for the submodels. Follow the instructions below to model the AAS using AASPE
- Once you've entered all the required data, save the AAS template for future use and integration.
- File > Save your completed AAS file.
This tutorial walks you through the process of using the AAS Generator to map the data from Word documents to generate Asset Administration Shell (AAS) submodels in a JSON format. The generator ensures the submodels comply with AAS specifications and standards.
- Open the AAS Generator Link.
- Use the following Word documents to map the values to the respective submodels:
- SMT_Namplate.docx for the Nameplate submodel.
- SMT_Techincal_Data.docx for the Technical Data submodel.
- Use the following prompt to map the data from the Word documents and generate the submodels:
- Prompt: Analyze and extract the relevant information from the Word documents SMT_Namplate.docx and SMT_Technical_data.docx. Map the extracted data to their respective submodels within the Asset Administration Shell (AAS). Then, generate a complete AAS that includes both the Nameplate and Technical Data submodels. Ensure that all attributes from the documents are accurately mapped, and that both submodels include the required fields such as id, idShort, and submodelElements, following the correct AAS schema structure.Finally, provide the output as a single downloadable JSON file containing the AAS with the Nameplate and Technical Data submodels.
- Download the JSON files for both submodels and open them using the AAS Package Explorer (AASPE) for further validation.
As a machine builder, you want to track the key performance indicators (KPIs) for your machine. To achieve this, you will design a new submodel that contains all the necessary information to calculate the KPIs for the machine. You will use the AAS Generator to create this submodel based on the ISO 22400 standard for KPIs.
- Open the AAS Generator Link.
- Use the following prompt to generate the submodel for Machine KPIs:
- Prompt: Generate an Asset Administration Shell (AAS) JSON file named "MachinePerformanceIndicators" based on ISO 22400 Key Performance Indicators (KPIs). Each KPI should be represented as a SubmodelElementCollection (SMC). For each KPI and its parameters, ensure the following:
- Add a semantic ID to each KPI SMC, using the format: "https://admin-shell.io/idta/MachinePerformanceIndicators/{KPI_idShort}".
- Add a semantic ID to each property within the SMC, using the format: "https://admin-shell.io/idta/MachinePerformanceIndicators/{property_idShort}".
The KPIs and their corresponding sub-parameters are as follows:
-
Overall Equipment Effectiveness (OEE)
- Formula: OEE = Availability * Effectiveness * QualityRate
- Sub-parameters: Availability, Effectiveness, QualityRate
-
Availability
- Formula: Availability = APT / PBT
- Sub-parameters: ActualProductionTime (APT), PlannedBusyTime (PBT)
-
Throughput Rate
- Formula: ThroughputRate = PQ / AOET
- Sub-parameters: ProducedQuantity (PQ), ActualOrderExecutionTime (AOET)
-
Utilization Efficiency
- Formula: UtilizationEfficiency = APT / AUBT
- Sub-parameters: ActualProductionTime (APT), ActualUnitBusyTime (AUBT)
-
Net Equipment Effectiveness (NEE)
- Formula: NEE = AUPT / PBT * Effectiveness * QualityRate
- Sub-parameters: ActualUnitProcessingTime (AUPT), PlannedBusyTime (PBT), Effectiveness, QualityRate
-
Worker Efficiency
- Formula: WorkerEfficiency = APWT / APAT
- Sub-parameters: ActualPersonnelWorkTime (APWT), ActualPersonnelAttendanceTime (APAT)
-
Scrap Ratio
- Formula: ScrapRatio = SQ / PQ
- Sub-parameters: ScrapQuantity (SQ), ProducedQuantity (PQ)
-
Production Process Ratio
- Formula: ProductionProcessRatio = APT / AOET
- Sub-parameters: ActualProductionTime (APT), ActualOrderExecutionTime (AOET)
-
Quality Ratio
- Formula: QualityRatio = GQ / PQ
- Sub-parameters: GoodQuantity (GQ), ProducedQuantity (PQ)
-
Mean Time to Failure (MTTF)
- Formula: MTTF = TotalOperatingTime / NumberOfFailures
- Sub-parameters: TotalOperatingTime, NumberOfFailures
Please ensure that the semantic IDs for each element are generated according to the pattern described above. Output the structure as a downloadable JSON file.
- Download the JSON files for both submodels and open them using the AAS Package Explorer (AASPE) for further validation.