Artificial Intelligence in Positioning Between Tooth and Nerve on Panoramic Radiography

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Fractal prediction of frictional force against the interior surface of forming channel coupled with temperature in a ring die pellet machine

For the biomass ring die pellet machine, the frictional force against the interior surface of the forming channel is the main cause for its frictional wear and also is key to the research of wear mechanism as well as its prediction. In this study, four ring die samples were used to measure and obtain data on their surface morphology. The fractal dimension D and fractal feature G were calculated using the Yardstick method, and lastly a fractal prediction model of sliding frictional force against the interior surface of forming channel was built, which was coupled with a fractal model of temperature distribution over friction surface. Numerical simulation, as well as friction-wear test were conducted to verify the accuracy of the model. The result showed that: when Ar < Arc, the slope of F was larger, which means the frictional force increased more rapidly, and the larger slope of FD represented a rapidly decreasing unit of frictional force. When true contact area Ar = 3.93%, Aa, FT, and FTD increased with the increase in temperature; FT increased rapidly at first and then gradually slowed down. When Ar was small, FTD increased sharply with the increase in temperature.

A fractal model of contact between rough surfaces for a complete loading–unloading process

The mechanical properties of contact between rough surfaces play an important role in the reliability of the electromechanical system. In order to improve the design accuracy of precision instruments, an elastic-plastic contact model for three-dimensional rough surfaces based on the fractal theory is developed for a complete loading–unloading process based on the Majumdar and Bhushan model. The truncation size distribution functions of asperities for different values of asperity level in the loading process are given. Relationships between true contact area and total contact load in the complete loading–unloading process are obtained according to the truncation size distribution functions of asperities. The results show the range of asperity levels has significant effects on contact mechanical behaviors of fractal rough surfaces. When the first six levels of asperities do not exceed the critical elastic level, the fractal rough surfaces exhibit elastic behavior in a complete contact process, and the load–area relationships in the loading and unloading processes are coincident approximately. When the critical elastic level is less than the minimum level of asperity, the inelastic deformation begins to appear in fractal rough surfaces and the true contact area during the unloading process is always greater than the true area during the loading process for a given total contact load. In comparison with the K-K-E model, the present model is proved to be reasonable.

Linking energy loss in soft adhesion to surface roughness

A mechanistic understanding of adhesion in soft materials is critical in the fields of transportation (tires, gaskets, and seals), biomaterials, microcontact printing, and soft robotics. Measurements have long demonstrated that the apparent work of adhesion coming into contact is consistently lower than the intrinsic work of adhesion for the materials, and that there is adhesion hysteresis during separation, commonly explained by viscoelastic dissipation. Still lacking is a quantitative experimentally validated link between adhesion and measured topography. Here, we used in situ measurements of contact size to investigate the adhesion behavior of soft elastic polydimethylsiloxane hemispheres (modulus ranging from 0.7 to 10 MPa) on 4 different polycrystalline diamond substrates with topography characterized across 8 orders of magnitude, including down to the angstrom scale. The results show that the reduction in apparent work of adhesion is equal to the energy required to achieve conformal contact. Further, the energy loss during contact and removal is equal to the product of the intrinsic work of adhesion and the true contact area. These findings provide a simple mechanism to quantitatively link the widely observed adhesion hysteresis to roughness rather than viscoelastic dissipation.

Contact Properties Research for Linear Sliding Guide Rail With the Fractal Theory

High quality products are primarily dependent on the accurate translation motion of each linear sliding guide rail (LSGR) of CNC machine tools in the advanced manufacturing industry. The existing researches about LSGR precision pay much more attentions on the components dimensional variation and deformations, while micro-geometric form errors (such as surface roughness) have been ignored in most previous studies. Therefore, the true contact interface property is so crucial that it will affect macro-mechanical performance, such as friction, wear, assembly relation, tightness, fatigue strength, etc. In order to obtain the contact behaviors of LSGR accurately, a novel finite element model contact analysis is proposed in this paper. Instead of adopting the conventional statistic characterization method, such as Greenwood-Williamson (GW) model, the rough slider surface generated by the fractal function get closer to the real surface topography. In this study, the true contact area, contact pressure, contact stress and deformation are all investigated. Furthermore, the contact properties results of the present model are in a good agreement with other analytical solutions. In conclusion, the proposed finite element model combining with the fractal theory may provide an accurate contact analysis for LSGR. It is also great of guiding significance for the prediction of assembly quality and operation performance in high-precision measurement region as well as other precise engineering applications.

Contact-Patch-Size Distribution and Limits of Self-Affinity in Contacts between Randomly Rough Surfaces

True contact between solids with randomly rough surfaces tends to occur at a large number of microscopic contact patches. Thus far, two scaling regimes have been identified for the number density n ( A ) of contact-patch sizes A in elastic, non-adhesive, self-affine contacts. At small A, n ( A ) is approximately constant, while n ( A ) decreases as a power law at large A. Using Green’s function molecular dynamics, we identify a characteristic (maximum) contact area A c above which a superexponential decay of n ( A ) becomes apparent if the contact pressure is below the pressure p cp at which contact percolates. We also find that A c increases with load relatively slowly far away from contact percolation. Results for A c can be estimated from the stress autocorrelation function G σ σ ( r ) with the following argument: the radius of characteristic contact patches, r c , cannot be so large that G σ σ ( r c ) is much less than p cp 2 . Our findings provide a possible mechanism for the breakdown of the proportionality between friction and wear with load at large contact pressures and/or for surfaces with a large roll-off wavelength.

Contact-Patch-Size Distribution and Limits of Self-Affinity in Contacts between Randomly Rough Surfaces

True contact between solids with randomly rough surfaces tends to occur at a large number of microscopic contact patches. &nbsp;So far, two scaling regimes have been identified for the number density n(A) of contact-patch sizes A in elastic, non-adhesive, self-affine contacts. &nbsp;At small A, n(A) is approximately constant,&nbsp; while&nbsp; n(A) &nbsp;decreases as a power law at large&nbsp; A.&nbsp; Using Green&rsquo;s function molecular dynamics, we identify a characteristic (maximum) contact area Ac above which a superexponential decay of n(A) becomes apparent if the contact pressure is below the pressure pcp at which contact percolates.&nbsp; We&nbsp; also find that&nbsp; Ac increases with load relatively slowly far away from contact percolation. Results for Ac&nbsp; can be estimated from the stress autocorrelation function G&sigma;&sigma; (r) with the following argument: the radius of characteristic contact patches, rc, cannot be so large that G&sigma;&sigma; (rc) is much less than pc2. Our findings provide a possible mechanism for the breakdown of the proportionality between friction and wear with load at large contact pressures and/or for&nbsp; surfaces with a large roll-off wavelength.

Comment to paper “Penetration depth and tip radius dependence on the correction factor in nanoindentation measurements” by J.M. Meza et al. [J. Mater. Res. 23(3), 725 (2008)]

[Meza et al. J. Mater. Res.23(3), 725 (2008)] recently claimed that the correction factor beta for the Sneddon equation, used for the evaluation of nanoindentation load-displacement data, is strongly depth- and tip-shape-dependent. Meza et al. used finite element (FE) analysis to simulate the contact between conical or spheroconical indenters, and an elastic material. They calculated the beta factor by comparing the simulated contact stiffness with Sneddon’s prediction for conical indenters. Their analysis is misleading, and it is shown here that by applying the general Sneddon equation, taking into account the true contact area, an almost constant and depth-independent beta factor is obtained for conical, spherical and spheroconical indenter geometries.