Conference Agenda

Seidel's third-order aberration theory describes five primary monochromatic aberrations in rotationally symmetric systems: spherical aberration, coma, astigmatism, field curvature, and distortion. In contrast, Hamilton’s eikonal function includes a sixth term that depends only on field coordinates. An object-dependent term also appears in first-order (paraxial) theory, alongside defocus and magnification. This field-dependent, quadratic phase term is common in Fourier optics and vanishes in telecentric 4f systems or in the incoherent limit. Although this term is often labeled as a “piston” term in aberration theory textbooks, implying that it has no effect on imaging, this is misleading because such terms can have real physical consequences. Intensity variations arise from differences in optical path length between interfering object points. In the first order, this manifests as telecentric deviation; in higher orders, it manifests as field-dependent chief ray variations. At the pupil plane, this corresponds to spherical pupil aberration. All terms of the eikonal function, whether dependent on the pupil or the field, can be seamlessly incorporated into non-isoplanatic imaging equations. This enables a comprehensive description of imaging systems under arbitrary coherence.

The calibration of the sphericity of spherical sections at PTB is traceable

to a highly polished silicon ball standard calibrated with PTB’s sphere interferometer.

This article describes, how the traceability is experimentally

established and how a topography section of the silicon ball is aligned with the

reference objective for calibration.

Classical optical elements such as lenses, mirrors, prisms, or beam splitters are traditionally categorized according to their optical effect on incident light. This functional classification is highly useful for optical design and remains deeply established in optical engineering. Modern fabrication technologies such as ultra-precision machining (UPM), single-point diamond turning (SPDT), deterministic polishing, and selective laser etching (SLE), however, increasingly enable highly integrated optical systems in which multiple optical and mechanical functional surfaces are generated within one preserved fabrication reference frame. Such systems can achieve relational positioning and orientation accuracies that are often difficult or extremely costly to realize through conventional multi-element assembly approaches. This contribution introduces Multi-Surface Monoliths (MS-Monoliths) as a new optical architecture class for such highly integrated systems. The paper further presents how PanDao bridges these fabrication possibilities toward optical designers by enabling MS-Monoliths to be considered, optimized, and compared during technological supply-chain optimization. PanDao identifies the optimum technological supply chain at minimum fabrication cost and fabrication risk, including optical surface generation, center grinding, coating, optical cementing, protective chamfers, mounting facets, circumference shaping, testing, and associated manufacturability and risk propagation across the complete realization chain

This paper introduces a constraint-based workflow for the unbiased development of optical systems, addressing the growing complexity of modern optical design. The proposed approach conducts a systematic exploration of the initial design space, capturing all feasible solutions that satisfy the specified constraints. Hereby, constraints are used as tools to limit the solution space and bridge requirements across domains, enabling a comprehensive evaluation of every possible initial design. Each candidate design is then optimized and assessed, facilitating the identification of optimal solutions.

This study focusses on linking sensor-based process data with product quality in CNC-grinding processes using both data-driven modeling and optimization methods. This approach enables early identification of key influencing factors and potential error sources, improving process control and quality assurance. The goal is to achieve XAI-supported optimization of grinding processes with limited data using real machines and quality data from Design of Experiments in combination with symbolic regression and Bayesian optimization.