Current Issues in Error Modeling—3D Volumetric Positioning Errors

2021 ◽  
Vol 1037 ◽  
pp. 77-83
Author(s):  
Andrew V. Kochetkov ◽  
T.N. Ivanova ◽  
Ludmila V. Seliverstova ◽  
Oleg V. Zakharov

The development of additive manufacturing requires the improvement of 3D printers to increase accuracy and productivity. Delta kinematics 3D printers have advantages over traditional sequential kinematics 3D printers. The main advantage is the high travel speed due to the parallel movement of the platform from three pairs of arms. Another advantage is the relatively low cost due to the small number of structural components. However, delta 3D printers have received limited use. The main reason is the low positioning accuracy of the end effector. Errors in the manufacture and assembly of components of a parallel drive mechanism add up geometrically and cause an error in the position of the end effector. These formulas can be applied to a 3D printer as well. However, well-known studies consider deterministic models. Therefore, the analysis is performed for limiting size errors. The purpose of this article is to simulate the effect of statistical errors in displacements and arm lengths on the positioning errors of a platform with the end effector. The article effectively complements the field of error analysis research and provides theoretical advice on error compensation for delta 3D printer.


2020 ◽  
Vol 49 (7) ◽  
pp. 20200154
Author(s):  
Ann Wenzel ◽  
Louise Hauge Matzen ◽  
Rubens Spin-Neto ◽  
Lars Schropp

Objectives: To assess dental students’ ability to recognize head positioning errors in panoramic (PAN) images after individual learning via computer-assisted-learning (CAL) and in a simulation clinic (SIM). Both cognitive skills and performance in patient examination were assessed. Methods and materials: 60 students (mean age 23.25 years) participated in lectures on the relation between PAN-image errors and patient’s head position. Immediately after they took a test, based on which they were randomized to three groups: control (CON) group, CAL group, and SIM group (both CAL and training in a simulation clinic with a phantom). 4–5 weeks after intervention/no intervention, all students individually examined a patient with PAN-exposure. A blinded rater, not knowing group allocation, supervised patient exposure and assessed student’s performance (correct/incorrect head position in three planes). 1–2 weeks after, the students scored positioning errors in 40 PAN-images. Differences in cognitive test scores between groups were evaluated by ANOVA and in patient examination by χ2 tests, and within-group differences by sign-tests. Results: No statistically significant difference in cognitive test scores was seen between the SIM and CAL group, while the CON group scored lower (p < 0.003). In all groups, several students positioned the patient incorrectly in the Frankfort horizontal plane. All students performed well in the sagittal plane. Students in SIM group positioned the patient more correctly in the coronal plane. Conclusions: Training with CAL increased students’ cognitive skills compared with a control group. Simulated patient exposure with a phantom increased to some extent their performance skills in examination of patients.


Author(s):  
Tianyi Zhou ◽  
Hang Gao ◽  
Xuanping Wang ◽  
Lun Li ◽  
Qing Liu
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.


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