Disassembly is vital for the circular economy, revealing a product’s structure and sequence. It enables assessments of reparability (DIN EN 45554), remanufacturing potential (DIN SPEC 91472), automation (TAA/NA method), and ease of disassembly (Peony Model or ReDiM method). However, to date, diverse data collection standards are used for each of these methods. These standards usually neglect the process forces and torques during manual disassembly, the screwdriver torque data, and the visual documentation. To address this gap, we developed and evaluated a cross-platform application (compatible with multiple operating systems on both mobile devices and desktops) for structured documentation of various manual disassembly process steps. Thereafter, scores for product reparability, remanufacturing capability, automation potential of disassembly, and ease of disassembly are calculated using the same data structure. This cross-platform application ofers a comprehensive way to collect and analyze the gained disassembly data. This solution provides a comprehensive approach for collecting and analyzing disassembly data, which can also be leveraged for machine learning applications based on force-torque data and for designing robotic disassembly cells. This application is tested in the following example of disassembly: a pump and a refrigerator.
Assadi et al. (Thu,) studied this question.