Interoperability in Smart Manufacturing: Research Challenges
2. Overview of Key Concepts and Paradigms
2.1. Cyber–Physical Systems
2.2. Internet of Things
2.3. Data Analytics and Machine Learning
2.4. Emerging Paradigms in Manufacturing
2.4.1. Smart Manufacturing
2.4.2. Cyber–Physical Production Systems
2.4.3. Industry 4.0
2.4.4. Cloud Manufacturing
3. Interoperability in Manufacturing
3.1. Definitions of Interoperability
- The ability of one system to receive and process intelligible information of mutual interest transmitted by another system .
- Interoperability means the ability of two or more parties, machine or human, to make a perfect exchange of content. Perfect means no perceptible distortions or unintended delays between content origin, processing and use .
- Interoperability among components of large-scale, distributed systems is the ability to exchange services and data with one another .
- The condition achieved between systems when information or services are exchanged directly and satisfactorily between the systems and/or their users .
- The ability to integrate data, functionality and processes with respect to their semantics .
- Interoperability, in a broad sense, refers to the use of computer-based tools that facilitate coordination of work and information flow across organizational boundaries, focusing mainly on inter-enterprise distributed BPs and flows .
- The ability of systems, units, or forces to provide services to and accept services from other systems, units, or forces and to use the services so exchanged to enable them to operate effectively together .
3.2. Challenges in Implementation of Interoperability
- Transfer of data between systems that may be similar or dissimilar (commercially).
- Transfer of data between software made by the same vendor (or creator) but having different versions on the systems.
- Compatibility between different versions of software (newer and older versions).
- Misinterpretation of terminology used or in the understanding of the terminology used for exchange of data or information.
- The use of non-standardized documentation on which the exchange of data is processed or formatted.
- Not testing the applications that are deemed conformant, due to the lack of means to do so between systems.
3.3. Generic Approaches to Implement Interoperability
3.3.1. Syntactic Interoperability
3.3.2. Semantic Interoperability
3.4. Factory Interoperability (Vertical Integration)
3.5. Cloud-Manufacturing Interoperability (Horizontal Integration)
- Behavioral interoperability. This type selects from a list of anticipated responses when requests are received for our services. By setting conditions on the requests, computer simulations with the help of human responses can anticipate the outcomes of the service request and be ready to offer a solution.
- Policy interoperability. This type ensures that all the systems in the cloud follow and conform to the regulations and policies while interacting within the cloud environment.
4. Reference Architectures for Interoperable Manufacturing
- The physical architecture of the components of a system (e.g., automation devices, machines, software, departments).
- The functional architecture representing the set of functions and processes to be accomplished by the systems.
- The allocated architecture describing the mapping between the functional architecture and the physical architecture.
4.1. RAMI 4.0
- Business layer. This layer consists of the business models and information about the business’s components, such as the service and or the products that it offers. The business rules and practices are pre-defined and serve for governing the manner in which business processes are executed.
- Functional layer. The functions of the architecture model are characterized and detailed and their relationships are established. The functions are designed and rendered and are autonomous, not dependent upon the processes or its utilization in the architecture.
- Information layer. This layer controls and governs the data and information that is used in the architecture. It also serves the purpose of analyzing the information that is being exchanged and the level of data quality.
- Communication layer. The communication between layers, systems and all the components that are executed, while at the same time interoperable are described by this layer.
- Integration layer. This layer provides a connection between all the layers of this architecture and the physical components. It also addresses network and software integration.
- Asset Layer. This layer includes all the systems at the physical level such as shop floor machines, the human interaction with the systems as well as other physical objects.
- Business domain. This domain is at the top of the IIRA functional viewpoint and consists of business-related information at the enterprise level. It also manages systems that are used across the business such as Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) in line with objectives and goals pertinent to the business.
- Information domain. This domain consists of functions that are tasked with the purpose of data analysis and assessment of data quality to procure knowledge of all the system’s components.
- Application domain. This domain builds on the information logged by the information domain and applies logic to it to improve system performance and efficiency of operations.
- Operations domain. This domain assesses the operations that are planned and ongoing, performs scheduled maintenance and diagnostics, with the end goal being ensuring optimal performance.
- Control domain. This domain consists of functions that control the implementation of processes at the physical system with feedback.
4.3. IBM Industry 4.0 Reference Architecture
- The Edge integrates the Plant and the Enterprise layers, with the ability to connect with legacy systems and equipment and also the protocol that they follow. The Edge can work independent of the Plant and Enterprise layers, without any integration by controlling the systems components at the device level directly. Smart devices and equipment can communicate all the way up through to the Enterprise level from the Edge when integrated. The Edge undertakes the task of data analytics and transformation and communicates this information through the layers.
- The Hybrid Cloud or the Platform consisting of the Plant and the Enterprise levels perform plant wide and enterprise wide analytics. The Plant level can direct MES to take appropriate decisions based on the analyzed data and the Enterprise level can similarly make decisions based on factories, locations, etc.
4.4. NIST Service-Oriented Architecture for Smart Manufacturing
5. Real-World Implementation of Industry 4.0 Components
6. Future Scope and Recommendations
Conflicts of Interest
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|Integration of shop floor, business processes, and cloud services for plug-and-produce work (P1)||✓||✓||✓||✓|
|Fully-integrated industry—inter-enterprise, cloud-based publication and sharing of services (P1)||✓|
|Service representation, composition, discovery, registration, and matching (P1)||✓|
|Service definition—shift of focus from macro to micro services in manufacturing SOA (P1)||✓|
|Representation of complex capabilities such as cognitive systems or analytics as services (P1)||✓|
|Standardized taxonomy of manufacturing processes as services (P1/P2)||✓||✓||✓|
|Asynchronous and interoperable communication mechanisms and publish/subscribe API (P1)||✓||✓||✓||✓|
|Need for ‘smarter’ objects to execute more complex services (P2)||✓|
|Standardized description of objects as prerequisite for standardization of services (P2/P4)||✓||✓|
|Common language (e.g., OPC-UA) for standardizing object and services (P2)||✓||✓||✓||✓|
|Mechanisms for orchestration, filtering, aggregation, and sharing of cloud services (P3/P4)||✓||✓||✓|
|New models for the economics of acquisition, implementation, and integration (P3)||✓|
|New models for automated, on-the-fly creation and governance of value networks (P4)||✓||✓|
|Horizontal integration of product life-cycles through micro-services (P4)||✓|
|Need for autonomous, self-/environment-aware, intelligent devices for service-orientation (P4)||✓|
|Concurrent optimization of order-to-cash and design-manufacture-maintenance cycles (P4)||✓|
|Lack of horizontal integration and reconfigurable manufacturing capabilities in ISA-95 (P5)||✓|
|Integration of ISA-95 into a cloud-based architecture (P5)||✓|
|Transition from thousands of enterprise-wide applications to consistent cloud-based services (P6)||✓|
|Lack of modularity of legacy manufacturing systems hindering ‘composability’ (P6)||✓|
|PX: Proposition X|
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Zeid, A.; Sundaram, S.; Moghaddam, M.; Kamarthi, S.; Marion, T. Interoperability in Smart Manufacturing: Research Challenges. Machines 2019, 7, 21. https://doi.org/10.3390/machines7020021
Zeid A, Sundaram S, Moghaddam M, Kamarthi S, Marion T. Interoperability in Smart Manufacturing: Research Challenges. Machines. 2019; 7(2):21. https://doi.org/10.3390/machines7020021Chicago/Turabian Style
Zeid, Abe, Sarvesh Sundaram, Mohsen Moghaddam, Sagar Kamarthi, and Tucker Marion. 2019. "Interoperability in Smart Manufacturing: Research Challenges" Machines 7, no. 2: 21. https://doi.org/10.3390/machines7020021