Search Results (5)

Background/Objectives: the precise application of torque during prosthetic screw tightening is essential to the long-term success and mechanical stability of implant-supported restorations. This study aimed to evaluate the influence of practitioner experience, glove material, screwdriver length, and hand moisture on the maximum torque value (MTV) generated during manual tightening. Methods: thirty participants, comprising 10 experienced professors and 20 senior dental students, performed tightening tasks under six hand conditions (nitrile gloves, latex gloves, and bare hands, each in dry and wet environments) using two screwdriver lengths (21 mm and 27 mm). The torque values were measured using a calibrated digital torque meter, and the results were analyzed using a linear mixed model. Results: professors applied significantly higher torque than students (16.92 Ncm vs. 15.03 Ncm; p = 0.008). Nitrile gloves yielded the highest torque (17.11 Ncm), surpassing bare hands significantly (p = 0.003). No statistically significant differences were found for screwdriver length (p = 0.12) or hand moisture (p = 0.11). Conclusions: these findings underscore the importance of clinical proficiency and glove material in torque delivery, providing evidence-based insights to enhance procedural reliability and training standards in implant prosthodontics. Full article

The calculation of manual tightening torque applied by clinicians plays a critical role in achieving optimal preload for dental implants. However, there is a research gap when it comes to understanding the specific calculus involved in this process. This study aims to address this gap by analyzing the bending and torsional moments during manual tightening torque application by physicians of various specialties and genders. Additionally, the rates of early complications associated with clinician-calculated preload will be evaluated. The findings of this study will contribute to enhancing the understanding of clinician-controlled preload and guide future practices for successful dental implant outcomes. Full article

The inspection of railway fasteners to assess their clamping force can be used to evaluate the looseness of the fasteners and improve railway safety. Although there are various methods for inspecting railway fasteners, there is still a need for non-contact, fast inspection without installing additional devices on fasteners. In this study, a system that uses digital fringe projection technology to measure the 3D topography of the fastener was developed. This system inspects the looseness through a series of algorithms, including point cloud denoising, coarse registration based on fast point feature histograms (FPFH) features, fine registration based on the iterative closest point (ICP) algorithm, specific region selection, kernel density estimation, and ridge regression. Unlike the previous inspection technology, which can only measure the geometric parameters of fasteners to characterize the tightness, this system can directly estimate the tightening torque and the bolt clamping force. Experiments on WJ-8 fasteners showed a root mean square error of 9.272 N·m and 1.94 kN for the tightening torque and clamping force, demonstrating that the system is sufficiently precise to replace manual measurement and can substantially improve inspection efficiency while evaluating railway fastener looseness. Full article

Background and Objectives: Using an appropriate torque to tighten set screws ensures the long-term stability of spinal posterior fixation devices. However, the recommended torque often varies between different devices and some devices do not state a recommended torque level. The purpose of this study is to evaluate the effect of set screw torque on the overall construct stability and fatigue life. Materials and Methods: Two commercial pedicle screw systems with different designs for the contact interface between the set screw and rod (Group A: plane contact, Group B: line contact) were assembled using torque wrenches provided with the devices to insert the set screws and tighten to the device specifications. The axial gipping capacity and dynamic mechanical stability of each bilateral construct were assessed in accordance with ASTM F1798 and ASTM F1717. Results: Increasing or decreasing the torque on the set screw by 1 Nm from the recommended level did not have a significant effect on the axial gripping capacity or fatigue strength of Group A (p > 0.05). For Group B, over-tightening the set screw by 1 Nm did cause a significant reduction in the fatigue strength. Conclusions: Excessive torque can damage the rod surface and cause premature failure. When insertion using a manual driver is preferred, a plane contact interface between the set screw and rod can reduce damage to the rod surface when the set screw is over-torqued. Full article

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures. Full article