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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Required parameters for modelling heterogeneous geographically dispersed manufacturing systems.

Mark Goudswaard1, Chris Snider1, Martins Obi1

  • 1University of Bristol, Queen's Building, University Walk, BS8 1TR, UK.

Procedia CIRP
|February 6, 2023
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Summary

Global crises necessitate rethinking manufacturing strategies. Agent-based brokering of Additive Manufacturing (AM) offers a solution, but requires considering AM capability diversity for effective coordination.

Keywords:
Additive ManufacturingAgent-Based ManufacturingBrokering

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Area of Science:

  • Manufacturing Systems Engineering
  • Supply Chain Management
  • Additive Manufacturing (AM) Technologies

Background:

  • Global events like COVID-19 expose vulnerabilities in traditional manufacturing and supply chains.
  • Existing manufacturing models are often homogeneous, failing to capture the diverse capabilities of Additive Manufacturing (AM).
  • Future manufacturing faces volatility from supply chain disruptions, trade shifts, and natural disasters, demanding adaptable solutions.

Purpose of the Study:

  • To conceptualize the reality of Additive Manufacturing (AM) systems.
  • To identify essential parameters for modeling and coordinating diverse AM capabilities.
  • To explore levels of abstraction, performance metrics, and human roles in agent-based manufacturing.

Main Methods:

  • Conceptual framework development for agent-based Additive Manufacturing (AM) brokering.
  • Identification and elucidation of key parameters for diverse AM system modeling.
  • Discussion of abstraction levels, performance metrics, and human-agent interaction in manufacturing.

Main Results:

  • Additive Manufacturing (AM) offers a potential solution to manufacturing system volatility.
  • Modeling diverse AM capabilities is crucial for effective agent-based brokering.
  • Key parameters for successful AM system modeling and coordination have been identified.

Conclusions:

  • Agent-based brokering of Additive Manufacturing (AM) can address production needs at various scales.
  • Acknowledging and modeling the diversity of AM capabilities is imperative for realizing brokered AM.
  • Further research into abstraction levels, metrics, and human roles is needed for advanced agent-based manufacturing systems.