The rapid emergence of AI-assisted engineering tools has led to a wave of experimentation in which engineers can now generate functional prototypes of pressure vessel software in a fraction of the time it once took, often by relying on large language models (LLM’s) and automated code generation systems.
But the speed of this shift introduces a critical concern: correctness. Pressure vessel analysis is not a casual engineering task; calculations are governed by strict physical laws, safety margins, and regulatory standards. When an AI system generates code for stress analysis, wall thickness, or fatigue estimation, it may produce outputs that look plausible but contain subtle errors in assumptions, unit conversions, or boundary conditions. Unlike human-written programs, these mistakes may not be immediately obvious.
Traditionally, engineering software in this domain is developed through rigorous design reviews, peer verification, and adherence to established codes such as ASME Boiler and Pressure Vessel Code. However, AI-generated tools often bypass this disciplined process. If an engineer uses an LLM to assemble a calculation engine without fully deriving or understanding each formula, the validation chain becomes fragmented. Who, then, ensures that the equations are correctly implemented and consistently applied?
This raises a difficult question of liability. If a failure occurs—whether a design overestimates allowable stress or underestimates corrosion risk—responsibility becomes blurred. Is the engineer accountable for not verifying the AI-generated logic, or does the fault lie with the tool provider? Current legal and professional frameworks are not well equipped for such scenarios. Moreover, regulatory bodies require demonstrable validation and traceability, which black-box generation methods inherently weaken.
Ultimately, AI can be a powerful assistant in engineering workflows, but it cannot replace the obligation to understand and validate critical calculations from first principles. In safety-critical domains just as static equipment, efficiency must never come at the expense of rigor. As these tools become more common, the engineering community will need clearer standards for auditing AI-generated code, ensuring that innovation does not outpace responsibility.
