Metrology

We report the development of the first optical frequency comb (OFC) based on an in-house Ti:Sa femtosecond laser in Argentina, implemented at the National Metrology Institute (INTI). The system operates at a 1 GHz repetition rate with a broadband spectrum spanning from 500 to 1100 nm. The carrier-envelope offset frequency and the repetition rate are both phase-locked to a cesium atomic clock, establishing a direct link between the national frequency standard and the optical domain. This development represents a major step forward for national metrology and enables frequency-instability measurements of stable lasers within the country. To demonstrate its capabilities, the performance of a Nd:YAG Mephisto laser at 1064 nm was evaluated by measuring the beat note between its second harmonic and the OFC, allowing a direct assessment of its frequency noise and stability.

The reconstruction of structural deformation fields from sparse or indirect measurements represents a key challenge in structural health monitoring, particularly for real-time applications involving lightweight mechanical components. In linear elastic systems, any deformation state can be represented as a linear combination of structural mode shapes through modal superposition. Exploiting this property, the present work proposes a machine learning-based framework for the real-time reconstruction of the deflection field of an aluminium plate subjected to the impingement of a non-stationary water flow. The methodology combines modal superposition with a supervised Random Forest classifier trained on a database of known deformation states generated from a finite element model. For each deformation state, the retained modes are selected through a novel linear-regression-based criterion, in which the optimal modal subset is identified by simultaneously promoting a unit regression slope, a vanishing intercept, and a Pearson correlation coefficient close to unity between the reconstructed and reference deflection fields. The proposed strategy is compared with the previously developed Internal Strain Potential Energy Criterion (ISPEC), providing a direct comparison between an energy-based and a reconstruction-oriented mode selection approach. Experimental deflections are acquired through a vision-based displacement tracking system. Seven measurement points, located close to the clamped boundary and therefore far from the fluid excitation, are used as input to the reconstruction algorithm, whereas additional markers positioned closer to the fluid-loaded region are retained exclusively for validation, providing a more demanding assessment of the methodology. The classifier identifies the active modes from the measured deflection pattern, while the full-field structural response is recovered through modal superposition. Experimental validation demonstrates that the proposed regression-based approach consistently improves the reconstruction accuracy with respect to ISPEC. At the most demanding validation point, located closest to the fluid excitation, the mean reconstruction error is approximately $1.65\mathrm{mm}$ for deflection amplitudes reaching $25\mathrm{mm}$, corresponding to an NRMSE of $10.60\%$, compared with $14.74\%$ obtained using ISPEC, representing a reduction of $28.1\%$. Despite this improvement, the average computation time remains below $0.3\mathrm{ms}$, confirming the suitability of the proposed framework for real-time applications. These results demonstrate that the proposed methodology provides an accurate, computationally efficient, and robust solution for real-time full-field deformation estimation from sparse non-contact optical measurements.

Detecting tool wear in CNC milling is a central challenge for data analytics and sensor integration in Industry 4.0, as gradual degradation increases the risk of tool breakage and downtime and often leads manufacturers to replace tools early and inefficiently. This contribution reviews existing tool wear detection approaches, including machine control data and additional sensors, and addresses the resulting need for effective data reduction and interpretation. An experimental setup on a CNC milling machine collected OPC-UA data and vibration signals, processed via a KNIME-based pipeline. Results show that, in the investigated proof-of-concept experiments, simple aggregated indicators (e.g., power consumption) allow a clear distinction between new-tool and end-of-life (EoL) states under stable process conditions. However, when cutting parameters vary, the evaluated conventional machine-learning classifiers do not achieve satisfactory discrimination. This limitation is associated with parameter-induced signal overlap, the limited training dataset, and the deliberately simple time-domain features used in this study. The study is therefore intended as an exploratory proof-of-concept rather than a comprehensive validation of continuous tool wear progression. Finally, the contribution highlights the potential of generative AI for data preprocessing, showing that large language models can efficiently clean, structure, and interpret raw manufacturing data, reducing engineering effort and improving accessibility of data analytics. The findings provide a feasibility baseline for future studies addressing intermediate wear states, richer feature extraction, and broader industrial validation.