By John Gøtze
Today’s economy calls for fast and flexible production of products with small batch sizes, essentially down to a batch size of one. This means that production systems must be adaptive and flexible and support many different product variants or many different products. The answer to these requirements has become known as Smart Manufacturing.The base of smart manufacturing is digitalization. In smart manufacturing, the digital representation of the production system is fundamental.
Managing Information in Smart Manufacturing
Digitalizing the entire life cycle of the production system - covering design, construction, installation, operation, maintenance, and retirement - enables related departments and enterprises to share the latest and accurate information about the production system and its operation.
Until now, such information has been distributed in different forms of documents like drawings, lists and datasheets. Even if these documents have been digitalized, this approach has challenges:
- information is fragmented and often even stored in different data formats used by different engineering tools depending on the situation,
- information is represented in different data structures and it is identified differently (e.g. using different denominations for the same assets or for the same data points), and
- information is hard to validate and keep up-to-date, and can too easily cause errors, mistakes and even lead to dangerous situations in the production process.
The conventional approach requires re-input and conversion of information when using it in different engineering tools, and the latest information updated in one engineering tool is not automatically reflected in the same data in another engineering tool:
The Digital Factory (DF) framework, which has been developed by IEC TC 65/WG 16 Digital Factory, is an international standard numbered IEC 62832 addressing such challenges by providing a common reference for digitization of data related to production systems. QualiWare is a member of the WG16 working group.
The standard defines common rules for utilizing data based on dictionaries. A data dictionary consists of computer-understandable data attributes and classifications as its basic elements and is known as Common Data Dictionary (CDD). A data dictionary is an ontology providing a classification of assets and properties for describing these assets in a semantically unambiguous way. Data dictionaries were originally invented to provide product data and to support procurement of products. Companies use the definitions from dictionaries to provide standardized descriptions of their products, so that interested customers understand the product characteristics. Product data based on dictionaries provide semantically rich product descriptions. This approach supports comparison of the characteristics of different products and matching of the characteristics of a product with the original requirements, allowing it to also be applied in system engineering workflows.
The DF framework defines rules for structuring data using data dictionaries (not limited to CDD) that meet specific requirements for citing the contents, but it does not define the data dictionaries themselves. The DF framework aims to construct a digital representation of the entire production system, called a Digital Factory, and to utilize the information widely in various situations.
Whereas many international standards related to smart manufacturing specify data dictionaries itself, or a system that includes system configuration, communication/information security, hardware/software implementation, the DF framework is developed as a standard for integrating information beyond these systems. The fundamental idea of the DF framework is to use data dictionaries as a common base for identifying and for providing semantic information for engineering data, as illustrated below:
The DF framework specifies model elements and their usage rules for constructing and managing a Digital Factory, a digital representation of a production system.
The DF framework is a framework for creating and managing a Digital Factory. Consequently, defining the DF framework as an international standard facilitates developing interoperable engineering software and tools and enables multiple enterprises to use information in a borderless fashion collaboratively.
Dictionary-based information for the engineering of production systems is classified into three categories:
- meta-type information
- type information
- instance information
Meta-type information is provided in data dictionaries as a base for asset descriptions. At this level, the syntactic, semantic, and structural standards are defined for the description of assets. Type information is provided in libraries or e-catalogs providing information about product types and component types. At the level of instance information, the descriptions of production systems or parts of a production system are provided in Digital Factories.
A Digital Factory is a computer-based digital representation of an existing or planned production system. Its information may be shared and utilized among various activities and software programs of enterprises involved in constructing and managing the production system. The contents are added, changed, deleted, and then shared during various engineering activities as the production system life cycle progresses. The relationship between a Digital Factory and enterprise activities in the life cycle of production systems is shown below:
A Digital Factory is a collection of DF assets each of which is representing an individual component of a real-world production system. DF asset links represent relationships between the components. A DF asset can represent not only the characteristics of the equipment in the real world but also its role. The production system’s represented component can be a part, device, machinery, and control system.
Each DF asset is created based on the information in an electronic catalog called library provided by the represented equipment manufacturer.
The libraries’ contents are interpreted by definitions in the data dictionaries managed by international standardization organizations or consortia.
An enterprise owning the production system creates its own DF dictionary which is a data dictionary that contains necessary definitions, and its own DF library which includes necessary catalog information, for creating and managing a Digital Factory.
The Digital Factory Framework is published by IEC in three parts:
- IEC 62832-1:2020
Industrial-process measurement, control and automation - Digital factory framework - Part 1: General principles
- IEC 62832-2:2020
Industrial-process measurement, control and automation - Digital factory framework - Part 2: Model elements
- IEC 62832-3:2020
Industrial-process measurement, control and automation - Digital factory framework - Part 3: Application of Digital Factory for life cycle management of production systems
QualiWare customers can use the QualiWare platform and its advanced UML functionality to implement the complete IEC 62832 set of standards.
In future blogs we will discuss how the Digital Factory Framework can become an essential part of a holistic enterprise architecture for smart manufacturing.