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Updated: Feb 27, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

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

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Adaptive Multi-scale PHM for Robotic Assembly Processes.

Benjamin Y Choo1, Peter A Beling1, Amy E LaViers1

  • 1University of Virginia, Charlottesville, Virginia, 22904, USA.

Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference
|July 1, 2017
PubMed
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Adaptive multiscale prognostics and health management (AM-PHM) integrates component health data across manufacturing levels. This enables informed decisions for smarter, more resilient production systems.

Area of Science:

  • Manufacturing Engineering
  • Systems Engineering
  • Reliability Engineering

Background:

  • Prognostics and Health Management (PHM) data is often siloed and not utilized in high-level manufacturing decisions.
  • Existing PHM approaches lack integration across different hierarchical levels of a manufacturing system (component, machine, work cell, production line).

Purpose of the Study:

  • To introduce and describe the Adaptive Multiscale Prognostics and Health Management (AM-PHM) methodology.
  • To demonstrate how AM-PHM integrates component-level PHM data with manufacturing system hierarchies.
  • To enable actionable prognostic and diagnostic intelligence for improved decision-making in smart manufacturing.

Main Methods:

  • Development of the AM-PHM methodology, which leverages hierarchical relationships within manufacturing systems.

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  • Integration of component-level PHM information into a multiscale framework.
  • Application and simulation of the AM-PHM methodology using a canonical robotic assembly process.

    • Main Results:

    • The AM-PHM methodology facilitates the creation of actionable intelligence by connecting component health to higher system levels.
    • Decisions can be made proactively based on the current and projected health state of the manufacturing system.
    • Demonstrated the feasibility of AM-PHM in a simulated robotic assembly environment.

    Conclusions:

    • AM-PHM provides a structured approach to utilize PHM data effectively in smart manufacturing.
    • The methodology enhances decision-making by providing system-wide health awareness across hierarchical levels.
    • AM-PHM supports the development of more robust and efficient manufacturing operations.