A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

Abstract

For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.

Automated Battery Disassembly—Examination of the Product- and Process-Related Challenges for Automotive Traction Batteries

“…When removing the cover, there is a chance that the large number of screws could be dirty, and thus not directly recognizable, or corroded due to their location outside the housing, thus negatively influencing this very repetitive process. Corrosion protection applications would in turn lead to reduced visibility for vision sensor-based methods of automated disassembly which have already been used for screw detection [32,38]. Therefore, this step was considered challenging by 91.7% of the experts.…”