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High-Fidelity Fabrication of HAR Microlens Arrays via Adaptive Toolpaths, Intelligent Feedback, and Elliptical Vibration Cutting

Project description

High-aspect-ratio microlens arrays for advanced optical systems

High-aspect-ratio microlens arrays (HAR MLAs) are essential for advanced optical systems but face fabrication challenges due to errors and defects. Supported by the Marie Skłodowska-Curie Actions programme, the AIMLA project will develop an integrated closed-loop system combining adaptive toolpaths, FEM simulations, PIML, and elliptical vibration cutting. This system will optimise cutting processes by ensuring consistent engagement and minimising deflection while predicting tool wear and chatter. Key goals include less than 1 µm form deviation and over 85 % chatter suppression. Final validation will confirm reproducibility across MLA geometries. This project aims to advance precision optics manufacturing, create scalable methodologies for SMEs, and provide training in adaptive toolpath design and machine learning, supporting Europe’s manufacturing goals.

Objective

High-aspect-ratio microlens arrays (HAR MLAs) are essential for next-generation optical systems, yet their fabrication is limited by surface errors, tool-induced defects, and scalability issues on standard 3-axis ultra-precision machines. This project develops a fully integrated closed-loop framework combining curvature-adaptive volute spiral toolpaths, FEM simulations, physics-informed machine learning (PIML), and elliptical vibration cutting (EVC). Adaptive toolpaths ensure constant cutter engagement and uniform scallop height, while inverse modeling and geometric compensation minimize deflection and misalignment errors. Thermo-mechanical FEM simulations, validated experimentally on a KERN Evo system with piezo-dynamometer monitoring, capture cutting forces and stress fields to guide optimization. FEM outputs and force signals train PIML models embedding physical laws to predict tool wear, chatter, and surface deviations under sparse data. These models drive semi-automated adaptive corrections, targeting <1 µm form deviation, <10 nm roughness, >85% chatter suppression, ≥85% tool wear detection, and >70% burr reduction. Final validation with high-resolution profilometry and CMM confirms reproducibility across MLA geometries. Research will be conducted under Prof. Erhan Budak (Sabancı University), a leader in machining dynamics and PIML, with a secondment under Prof. Eiji Shamoto (Nagoya University), pioneer of EVC. This interdisciplinary fellowship will advance precision optics manufacturing, establish scalable methodologies for SMEs, and provide advanced training in adaptive toolpath design, simulation, and machine learning—supporting Europe’s strategic goals in advanced manufacturing and photonics.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

SABANCI UNIVERSITESI
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 165 205,20
Address
ORTA MAHALLE UNIVERSITE CADDESI N 27 TUZLA
34956 Istanbul
Türkiye

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Region
İstanbul İstanbul İstanbul
Activity type
Higher or Secondary Education Establishments
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Total cost

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