Janez Gotlih

About

Janez Gotlih is a researcher focused on advancing the precision and versatility of industrial robotics, particularly in machining and automation. His primary contributions lie in improving robotic accuracy through structural stiffness analysis and optimization. His 2020 paper, "Accuracy improvement of robotic machining based on robot’s structural properties," with 32 citations, is a cornerstone of his work, demonstrating how understanding a robot’s inherent stiffness can enhance machining quality. Gotlih’s 2017 study on "Determination of accuracy contour and optimization of workpiece positioning for robot milling" (19 citations) further refined workpiece placement within a robot’s workspace to maximize precision. He has also explored soft robotics, using a fully fractional generalised Maxwell model to describe viscoelastic behavior in dielectric elastomer actuators (2022, 8 citations). His research extends to practical applications like robotic deburring (2021, 6 citations) and stiffness calibration (2018, 4 citations). Notably, Gotlih integrates modern tools such as SIEMENS NX and KUKA robots for 3D welding (2024, 3 citations) and contributes to assistive robotics, combining machine learning and collaborative systems (2024, 2 citations). His work bridges theoretical modeling and real-world industrial solutions, making him a key figure in enhancing robotic reliability and efficiency.

Research Focus

Key Achievements

Top Papers

1. 1
   Accuracy improvement of robotic machining based on robot’s structural properties

   32 citations · 2020
2. 2
   Determination of accuracy contour and optimization of workpiece positioning for robot milling

   19 citations · 2017
3. 3
   Using a Fully Fractional Generalised Maxwell Model for Describing the Time Dependent Sinusoidal Creep of a Dielectric Elastomer Actuator

   8 citations · 2022
4. 4
   Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table

   6 citations · 2021
5. 5
   Experiment Based Structural Stiffness Calibration of a Virtual Robot Model

   4 citations · 2018
6. 6
   Developing 3D Welding Process with SIEMENS NX and KUKA Robot Manipulator

   3 citations · 2024
7. 7
   The Development of Assistive Robotics: A Comprehensive Analysis Integrating Machine Learning, Robotic Vision, and Collaborative Human Assistive Robots

   2 citations · 2024
8. 8
   Kinesthetic Trajectory Learning of a Collaborative Robot UR 10e with APLC S7-1500

   2 citations · 2023

Key Collaborators

Contact & Links