Mateo Leco

About

Mateo Leco is a leading researcher in intelligent manufacturing, specializing in robotic machining and in-process quality control. His work focuses on integrating data-driven modeling and active learning to enhance precision in robotic countersinking operations—a critical process in aerospace and high-value manufacturing. Leco’s major contributions include developing a perturbation signal-based Gaussian process regression model that predicts part quality during machining, reducing reliance on post-process inspection. This approach, detailed in his 2021 paper (38 citations), enables real-time error compensation, significantly improving manufacturing efficiency. In his 2022 study (25 citations), he introduced a two-step machining framework that combines active learning with in-process error prediction to achieve “right-first-time” outcomes, addressing robot-related inaccuracies that often compromise dimensional tolerances. By shifting quality assurance from end-of-line checks to real-time correction, Leco’s work reduces waste and cycle times. His achievements demonstrate a powerful synergy between machine learning and manufacturing, offering scalable solutions for high-precision industries. With growing citation impact, Leco is recognized for advancing the frontier of adaptive, data-driven robotic manufacturing.

Research Focus

Key Achievements

Top Papers

1. 1
   A perturbation signal based data-driven Gaussian process regression model for in-process part quality prediction in robotic countersinking operations

   38 citations · 2021
2. 2
   A two-step machining and active learning approach for right-first-time robotic countersinking through in-process error compensation and prediction of depth of cuts

   25 citations · 2022

Key Collaborators

Contact & Links