ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   12/06/2019

Editor's comments and enhancements are shown in green. [ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 46 ID: Use Case Name: Adaptable Factory
Application
Domain
Manufacturing
Deployment
Model
Cyber-physical System, Embedded System
StatusPoC
Scope(Semi-)Automatic change of a production system’s capacities and capabilities from a behavioral and physical point of view
Objective(s)The objective is to enable flexible production resources which enable fast reconfiguration and adaptation to changing situations, context, and requirements which facilitate optimized resource usage under uncertainty.
Short
Description
(up to
150 words)
Rapid, and in some cases completely automated, conversion of a manufacturing facility, by changing both production capacities and production capabilities. This use case describes the adaptability of an individual factory by (physical) conversion and/or adaption of a factory’s and its machines behavior in order to adjust to changing situations like disruptions, material quality variation, production of new products, etc.
A prerequisite is a modular and thereby adaptable design for manufacturing within the factory. The result is a need for intelligent and interoperable modules that basically adapted to an altered configuration on their own, and standardized interfaces between these modules.
Complete Description Use Case description taken from [1,2,3]. Plug & Play – using a home computer and a USB cable, it is easy to connect new devices and use them almost immediately without any additional effort. The flexibility that has been available for quite a while on desktop computers is now gaining importance for industrial production. Demands on adaptability of production infrastructure are already rapidly increasing. Shorter and shorter product and innovation cycles require investment decisions for new production facilities that reflect future demand for production and process changes, where possible. In addition, the growing volatility of orders is hindering the optimal utilization of manufacturing lines with increasing frequency. Flexibility and adaptability will become increasingly important criteria in decisions regarding construction and operation of new production facilities.

One example is product labeling. Various printing technologies are available, for example tampon printers (transferring ink from the printing form to the product using an elastic tampon), inkjet printers and/or laser printers. In an adaptable factory this type of operating equipment can be connected directly to the automated production process. Simply put, the material to be printed says: “Print me”, and the tampon printer will ask: “Is the material to be printed greaseless?” The ink jet printer will then ask about the material characteristics, because it uses heat for the drying process, for example. A laser printer will ask about the material receiving the label to ensure sufficient contrast.

Use Case description taken from [1,2,3]. Plug & Play – using a home computer and a USB cable, it is easy to connect new devices and use them almost immediately without any additional effort. The flexibility that has been available for quite a while on desktop computers is now gaining importance for industrial production. Demands on adaptability of production infrastructure are already rapidly increasing. Shorter and shorter product and innovation cycles require investment decisions for new production facilities that reflect future demand for production and process changes, where possible. In addition, the growing volatility of orders is hindering the optimal utilization of manufacturing lines with increasing frequency. Flexibility and adaptability will become increasingly important criteria in decisions regarding construction and operation of new production facilities.

One example is product labeling. Various printing technologies are available, for example tampon printers (transferring ink from the printing form to the product using an elastic tampon), inkjet printers and/or laser printers. In an adaptable factory this type of operating equipment can be connected directly to the automated production process. Simply put, the material to be printed says: “Print me”, and the tampon printer will ask: “Is the material to be printed greaseless?” The ink jet printer will then ask about the material characteristics, because it uses heat for the drying process, for example. A laser printer will ask about the material receiving the label to ensure sufficient contrast.

Key aspects
The application scenario for adaptable factories describes the rapid, and in some cases completely automated con-version of a manufacturing facility, by changing both production capacities and production capabilities. The key concept for implementation is a modular and thereby adaptable design for manufacturing within the factory. Intelligent and interoperable modules that basically adapted to an altered configuration on their own, and standardized interfaces between these modules allow for quick and simple conversion to adapt to changes in the market and customer demands. Whereas the application scenario Order-Controlled Production emphasizes flexible use of existing manufacturing facilities by means of intelligent connectivity, this scenario describes the adaptability of an individual factory by (physical) conversion.

Today, when creating a production line, the focus is usually not only on quality, but also maximization of productivity and profitability of a pre-conceived product range. Individual components are connected statically and are capable of producing the pre-conceived functionalities and projected volumes. Frequently, a system integrator takes care of coordinating the individual components and developing a control system for the entire facility. However, if the order level is driven by strong product individuality or high fluctuation in demand, companies can no longer rely on the advantage of particular production lines. In this case, modular, order-oriented and adaptable manufacturing configurations become more attractive: For example, they increase overall utilisation or ability to deliver products. At the same time, however, the demands on individual machines or manufacturing modules increase. Even more important than high variance of specific manufacturing steps will be the ability to combine individual modules with ease and in any situation. In order to achieve this, the modules must contain a self-description regarding their ability to be combined or converted into a machine or plant very rapidly and robustly. The following examples illustrate these requirements:

  • A new network-enabled field device, for example a drive with a new version of firmware, is hooked up to the production line. The new device must be provided automatically with network connectivity and be made known to all online subsystems. The participating systems must correspondingly be updated.
  • An unconfigured field device is introduced to production, for example to quickly replace another defective device. The field device now must be individualized and parameterized due to the information located in the software components.
  • A production facility is converted or modified because a new product variation is planned. The control and software related changes must be detected and automatically transmitted to all participating systems.
  • After conversion of a plant, it should be possible to move software components for process management around the decentralized control units, while observing certain criteria, such as output or availability.
  • A (new) function of the Manufacturing Execution System (MES) is inserted or altered, for example the visualization of a situation not previously required. The visualization should be done automatically and access to the necessary information from the field level should also be automatic.

    This requires the mechanical engineer to design the internal development processes accordingly. Modular machines require “modular” engineering, based on libraries of re-usable modules (“platform development”). Machine architecture must be designed such that combinable mechatronic modules are created, including the Plug & Produce capability of production modules using interoperable interfaces and adaptive automation technology. This requires development of concepts for “services” across manufacturer boundaries, such as archiving, alerting or visualising, as well as a low-cost integration of MES functions.

    Effect on value chains
    Value added is shifted from the system integrator to the machine provider or its supplier, because the machines or components are enhanced so that they are easier to integrate. The type and quality of system integration change. The present focus on (production) technology shifts to a stronger focus on organization and business processes related to production processes. In extreme cases, the system integrator could become obsolete if intelligent, self-configuring and interoperable manufacturing modules can be created at the level of the machine suppliers.

    Value added for participants
    For manufacturing companies, a quick, inexpensive and reliable conversion of manufacturing becomes possible, so that they can react quickly to changes in customer and market demands. Increasing standardization and modularization also expand the possibilities for combining manufacturing entities of various providers and therefore realizing the most economic solution for each individual module.

    Machine modularization opens up new areas with scale effects for machinery manufacturers.

StakeholdersComponent suppliers (sensors, actuators), Machine builders, system integrators, plant operators (manufacturer)
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities
Key
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives




AI Features Task(s)Automatic reasoning (e.g. [7,8]), AI (task) planning (e.g. [4,6]), distributed coordination and negotiation (e.g. [5])
Method(s)
Hardware
Topology
Terms &
Concepts Used
Standardization
Opportunities
Requirements
Standardization needs for setting up this use case is currently under further investigation. Some initial intentions on standardization needs are the following: a vocabulary with formal semantic for symbolic reasoning about production capabilities across different vendors, standardized negotiation mechanisms, standardized autonomy classes of components, machines, etc. Quality model for trustful learned models and automatic behavior resulting from it.
Challenges
& Issues
Societal Concerns Description Enabling flexible and autonomously reconfigurable production systems ease human-machine configuration, facilitate optimized machine use, reduce failures through autonomous compensation, optimized product quality through prediction techniques.
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case
Originator
Organization
Link
1 Working Group on Research and Innovation of the Plattform Industrie 4.0. Aspects of the Research Roadmap in Application Scenarios, Working Paper, German Federal Ministry for Economic Affairs and Energy, 2016. Link
2 Working Group on Research and Innovation of the Plattfom Industrie 4.0 and Alliance Industrie du Futur: Plattform Industrie 4.0 & Alliance Industrie du Futur : Common List of Scenarios. 2018. Link
3 Communication Promoters Group of the Industry-Science Research Alliance and German National Academy of Science and Engineering. Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final report of the Industrie 4.0 Working Group,, April 2013 Link
4 Christoph Legat and Birgit Vogel-Heuser. A configurable partial-order planning approach for field level operation strategies of PLC-based industry 4.0 automated manufacturing systems. Engineering Applications of Artificial Intelligence 66:128-144, DOI: 10.1016/j.engappai.2017.06.014,02017.
5 Birgit Vogel-Heuser, Jay Lee, and Paolo Leitao. Agents enabling cyber-physical production systems. at – automatisierungstechnik 63(10). DOI: 10.1515/auto-2014-1153, 2015.
6 Jens Otto and Oliver Niggemann. Automatic Parameterization of Automation. Software for Plug-and-Produce. AAAI Workshop on Algorithm Configuration, 2015
7 Christoph Legat, Christian Seitz, Steffen Lamparter und Stefan Feldmann. Semantics to the Shop Floor: Towards Ontology Modularization and Reuse in the Automation Domain. IFAC Processings, Vol. 47, Issue 3, pp. 3444 – 3449, Doi:10.3182/20140824-6-ZA-1003.02512, 2014.
8 Martin Ringsquandl, Steffen Lamparter, Sebastian Brandt, Thomas Hubauer, and Raffaello Lepratti. Semantic-Guided Feature Selection For Industrial Automation Systems. International Semantic Web Conference. Springer, Cham, 2015.

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013