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Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience.

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This summary is machine-generated.

Manufacturing resilience is crucial amid global disruptions. Testing machine learning (ML) models with adversarial attacks can reveal vulnerabilities and enhance system robustness against unforeseen events.

Keywords:
adverserial attacksadverserial trainingdeep neural networksdiscrete-event simulation environmentmachine learningmanufacturing systemresiliencesupply network

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Area of Science:

  • Industrial Engineering
  • Cybersecurity
  • Operations Management

Background:

  • Recent global events like the COVID-19 pandemic and geopolitical conflicts have severely disrupted industrial manufacturing operations.
  • These disruptions manifest as supply chain interruptions, workforce absenteeism, and volatile order volumes, highlighting a critical need for enhanced manufacturing resilience.
  • Existing manufacturing systems often lack the adaptive capacity to effectively manage unpredictable, large-scale disruptions.

Purpose of the Study:

  • To investigate novel methods for bolstering manufacturing resilience in the face of contemporary global disruptions.
  • To explore the potential of adversarial attacks on machine learning (ML) models as a strategy for identifying and improving manufacturing system robustness.
  • To provide actionable insights for enhancing the adaptability and stability of industrial manufacturing processes.

Main Methods:

  • The study proposes subjecting the machine learning (ML) model of a manufacturing system to deliberately crafted adversarial attacks.
  • These attacks are designed to probe the prediction capabilities of the ML model under simulated stress conditions.
  • Analysis focuses on the model's failure points and response patterns to identify weaknesses relevant to resilience.

Main Results:

  • Adversarial attacks on ML models successfully identified critical vulnerabilities within the simulated manufacturing system.
  • The insights gained from these attacks directly correlate with potential failure modes during real-world disruptions.
  • Simulated attacks provide a quantifiable measure of system resilience and highlight areas for targeted improvement.

Conclusions:

  • Proactively testing ML models with adversarial attacks is a viable strategy for enhancing manufacturing resilience.
  • Understanding system vulnerabilities through simulated attacks can preemptively address weaknesses exposed by supply chain and workforce disruptions.
  • This approach offers a proactive framework for building more robust and adaptable industrial manufacturing systems.