Ford EcoBoost EGR Recall: Welding Process Failure
Ford’s EcoBoost EGR recall shows how insufficient welding depth and supplier process drift can create manufacturing process escapes in high-volume production.
Category: Systems Engineering
Propagation of Complexity in Engineering Systems
Explains how complexity propagates through interfaces, Re-Use, change, and organizational structure in modern engineering systems.
Engineering Judgment in the Age of Artificial Intelligence
Artificial Intelligence supports engineering work, but Engineering Judgment determines causal reasoning, validation, and responsibility for the real-world.
Agentic AI and Digital Twins: A Systems Engineering Requirement for Trust
Agentic AI and digital twins in Industry 5.0 require a systems engineering framework with validated models, defined authority, and traceable evidence to enable trust.
Artificial Intelligence Verification Challenges in Systems
Artificial Intelligence complicates verification through probabilistic inference, simulation limits, and lifecycle drift in engineered systems.
What Do We Actually Mean by Complexity in Engineering Systems?
Defines complexity in engineering systems, distinguishing it from complication and explaining its impact on verification, requirements, and control.
Artificial Intelligence and the Missing Architecture of Responsibility
Artificial Intelligence in operational systems requires clear authority: who activates it, who validates runtime decisions, and who governs drift.
Ford F-150 Recall: Control State Collapse in a Safety-Critical System
Ford’s F-150 recall shows how an unintended downshift can become a systems failure when control logic trusts degraded transmission signals.
Why AI Systems Need Finite Behavioral Envelopes
AI Systems in Engineering development must stay within finite behavioral envelopes. Without defined boundaries, verification becomes incomplete.
When Systems Learn: Adaptation and Integrity in the V-Model
When systems learn, engineering integrity endures. This paper explains how learning and adaptation strengthen the V-Model without breaking verification.