## 3 Experimental Campaign  
### Experimental Set-Up  
We opted to assess TTool's pose detection accuracy in actual workshop fabrication conditions. Due to the algorithm's significant reliance on human-machine interaction and its sensitivity to real woodworking constraints (e.g., metallic objects, noise, varying lights, and abrupt change of backgrounds), the designed evaluation is based on live-captured feeds instead of rendered benchmarks on synthetic datasets. The evaluation source code is shared with the community and can be found in the public repository of the project [45]. In addition to gauging the accuracy and precision of the estimated 6 DoF poses, we include a preliminary designed form to evaluate the user experience (UX). The cognitive load and interface usability represent important components of a supervised 6 DoF pose detector, such as TTool. Users are engaging with displays and contemporary operating power woodworking tools. The UX benchmark is designed to fuse state-of-the-art references in the field of UX satisfaction benchmarks [46; 47; 48; 49] but also physical load evaluators, such as the popular NASA-TLX index [50]. However, it is important to note that the current participant pool is limited to one person with no prior construction experience but who is a competent digital literate. Consequently, further testing with a more diverse and larger population sample size is essential for a comprehensive assessment of TTool's UX. To assess the precision of TTool's pose detection, we utilize a mold that is meticulously referenced to a 3D model via the use of fiducial markers of type STag [51]. This particular fiducial marker is chosen due to its capacity to perform accurate pose detection, especially in cluttered views and steep view angles compared to other artificial markers [52; 53; 54]. This mold serves as a replica of a 3D model in execution, featuring accurately replicated slots and hole placements where physical tool heads can be inserted and compared. Once the mold is manufactured by computer numerical control (CNC), additional scanning is performed with the inserted tools to calibrate all fabrication inaccuracies and correct them in the 3D model (see Figure 12). As part of this process, the user inserts the tool head into the designated slots. The alignment for this comparison is feasible, as TTool's camera is calibrated via the detection of fiducial markers. This results in both the computed pose of the tool and the digital twin model sharing common reference points in the same coordinate system. Hence, GT can be determined by identifying common reference points between the tool head model and its negative counterpart, such as the spinning axis. This alignment method en  
Figure 12: Illustration of the CAD drawing for the probing plate, in which the blue stripes indicate tags calibrated to the locations of the slots. Following fabrication, the recalibration of slots and holes was performed using scans from physical tools inserted into the corresponding slots. sures a straightforward and reliable means of obtaining accurate measurements, as shown in Figure 13.  
The user is tasked with determining the pose of 162 tool heads using TTool's pipeline. The evaluation exercise comprises four sessions, each consisting of five assessment cycles per tool, with each cycle encompassing the utilization and pose detection of nine distinct tools. As shown in Figure 14, (a,b) is a self-feeding bit (of \(\diameter 50\) and \(\diameter 40\) mm), (c) a broad-point drill bit, (d) a twist drill bit, (e) a spade drill bit, (f,g) an auger drill bit (of \(\diameter 20\) and \(\diameter 34\) mm), (i) a circular saw with (a blade of 190 mm), (l) a sword saw, and finally (m) a chain saw. Further, the varying diameters of the self-feeding and auger drill bits are introduced to stress the system's capacity under the same typology of tools but with slightly different dimensions (i.e., a maximum of 2 cm in diameter difference).