ML-supported, flexible screw disassembly framework for the remanufacturing process of complex capital goods

Authored by

Richard Blümel, Sebastian Blankemeyer, Lars Aschermann, Annika Raatz

Abstract

Remanufacturing with efficient dismantling is important for sustainable manufacturing, but condition variability of end-of-life products complicates automated, non-destructive disassembly. Exposure to challenging environmental conditions often results in seized screw connections leading to damage and loss of high-quality fasteners. This work presents a framework for complex capital goods that combines operational data with a manually operated, piezo-actuated hand tool that generates controlled low-frequency vibrations to reduce loosening torque. A data-driven, machine learning-based process selects the optimal vibration parameters from usage data. Tests on specimens and real engines have demonstrated torque reductions of up to 20 %, which decreases the need for rework.

Details

Organisation(s): Institute for Assembly Technology and Robotics
External Organisation(s): MTU Maintenance
Type: Article
Journal: CIRP annals
Volume: 75
Pages: 31-35
No. of pages: 5
ISSN: 0007-8506
Publication date: 2026
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Mechanical Engineering, Industrial and Manufacturing Engineering
Sustainable Development Goals: SDG 9 - Industry, Innovation, and Infrastructure
Electronic version(s): https://doi.org/10.1016/j.cirp.2026.04.053 (Access: Open )