Manufacturing perfect lenses: what if the solution came from statistics?

An excellent articulation between the in silico, minimal, and production phases is presented, enabling progressive validation and clear risk management with quantified GO/NO-GO/PIVOT criteria.

The proposed causal chain (DoE → Regression → Sensitivity Analysis → RSM → Tolerance Reallocation) is a classical and rigorous methodological sequence in robustness engineering, well established in the literature of Design for Six Sigma (DFSS) and Taguchi’s Robust Design. The application to optics represents a natural and promising extension.

The attempt to formalise the tolerance problem through a multivariate statistical approach is conceptually appealing and could, in principle, reduce the costly empiricism of optical design iterations.

Immediate captive market: manufacturers of precision optical systems (smartphone lenses, surveillance cameras, medical instruments) spend 15–25% of their cost of goods sold on tolerance-related rework and scrap. A 20% yield improvement on a line producing 1 million units per year represents savings of €500,000–2 million per year, justifying a tooling budget of €50,000–200,000.

A statistical approach (DoE plus multivariate regression) is applied to a classic industrial problem (optical tolerancing): the methodological originality is strong, and the approach has been little explored in the optical literature.