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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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Published on: August 29, 2025

Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems.

Benjamin Y Choo¹, Stephen C Adams¹, Brian A Weiss²

¹University of Virginia, Charlottesville, Virginia, 22904, U.S.A.

International Journal of Prognostics and Health Management

|July 25, 2017

Summary

This summary is machine-generated.

Adaptive Multi-scale Prognostics and Health Management (AM-PHM) integrates component health data across manufacturing levels. This methodology enables smarter decisions by leveraging hierarchical Markov Decision Processes for optimized system management.

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Last Updated: Feb 26, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Area of Science:

Manufacturing Systems Engineering
Reliability Engineering
Artificial Intelligence in Manufacturing

Background:

Prognostics and Health Management (PHM) data is underutilized in higher-level manufacturing decisions.
Existing methods struggle with the complexity of large-scale manufacturing systems.
Hierarchical integration of PHM information is needed for effective decision-making.

Purpose of the Study:

To introduce the Adaptive Multi-scale Prognostics and Health Management (AM-PHM) methodology.
To demonstrate how AM-PHM integrates PHM data across manufacturing hierarchies.
To enable actionable prognostic and diagnostic intelligence for improved decision-making.

Main Methods:

Development of the AM-PHM methodology.
Leveraging hierarchical relationships across component, machine, work cell, and assembly line levels.
Application of a hierarchical Markov Decision Process (MDP) approach to manage system complexity and optimize policies.

Main Results:

AM-PHM enables actionable intelligence across the manufacturing hierarchy.
Decisions are informed by the current and projected health state of the system.
The hierarchical MDP approach effectively addresses system complexity.

Conclusions:

AM-PHM provides a structured approach to integrate and utilize PHM information.
The methodology enhances decision-making by considering system-wide health.
Simulated results demonstrate the effectiveness of AM-PHM in industry-inspired scenarios.