Yeonseok Lee

2papers

2 Papers

21.9LOMay 11
Separation Logic for Verifying Physical Collisions of CNC Programs

Yeonseok Lee

Safety verification in Computer Numerical Control (CNC) machining has traditionally relied on simulation-based methods that require repetitive tests when requirements change. This paper introduces a formal verification framework that conceptualizes the physical CNC workspace as a Spatial Heap, treating physical occupancy as a managed logical resource. Central to our approach is a Parser-Prover Handshake that decouples machine kinematics from formal logic. By mapping tool trajectories and safety buffers into a discrete spatial model prior to evaluation, the framework enables the use of Separation Logic (SL) to verify safety via formal triples. Within this model, physical collisions are redefined as logical Spatial Data Races, detected through the failure of the separating conjunction to establish disjointness. Furthermore, we extend the methodology to collaborative environments using Concurrent Separation Logic (CSL), where physical hand-offs are verified as formal ownership transfers. This approach provides a scalable, mathematically grounded alternative to geometric simulation, offering a foundation for autonomous, zero-collision manufacturing.

41.1LOMay 11
Correct-by-Construction G-Code Generation: A Neuro-Symbolic Approach via Separation Logic

Yeonseok Lee

This paper proposes a neuro-symbolic framework for G-code generation by integrating the GLLM neural method (Abdelaal et al., 2025) with our established Separation Logic (SL) verifier. We introduce a two-component architecture where GLLM serves as a creative generator and the SL Prover, utilizing the Spatial Heap model, acts as a deterministic verifier. By defining physical collisions as logical Spatial Data Races - violations of the separating conjunction in SL - the framework translates proof failures into structured mathematical feedback. These failures are condensed into minimal bounding boxes that act as precise spatial directives for GLLM's iterative self-correction. This synergy establishes a self-correcting generative cycle that reduces the need for manual oversight, supporting the production of verified G-code to enhance safety in autonomous manufacturing.