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Planning Failure Recovery Strategies using Artificial Intelligence in Discrete Manufacturing Automation

Rishi Ahuja

New Delhi, India

36-45

Vol: 12, Issue: 3, 2022

Receiving Date: 2022-08-27 Acceptance Date:

2022-09-25

Publication Date:

2022-12-03

Download PDF

http://doi.org/10.37648/ijrst.v12i03.007

Abstract

Automated Production Systems (aPS) must be more adaptable to adapt to the range of goods because discrete manufacturing is typically small batch and customised; this makes the aPS more error-prone and complex. Strategies for autonomous recovery are needed to improve system performance and decrease downtime brought on by manual intervention. Parts of the control software that treat inevitable failures planned and implemented at design-time carry out automatic recovery. Instead, reputable artificial intelligence planners should produce recovery strategies automatically to reduce engineering effort and handle unforeseen shortcomings. As a result, this study suggests breaking down the functional control software into Control Primitives, which are then used to create generated strategies. The components needed to manually implement the state machines of the various aPS operating modes are the same Control Primitives. Therefore, no further engineering work is required to prepare recoverability during the application development phase. This study presents four methods for modelling and implementing PLCexecutable Control Primitives.

Keywords: Automated Production Systems; artificial intelligence; automation science

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