Influence of cavity design preparation on stress values in maxillary premolar: a finite element analysis

Aim To analyze the influence of cavity design preparation on stress values in three-dimensional (3D) solid model of maxillary premolar restored with resin composite. Methods 3D solid model of maxillary second premolar was designed using computed-tomography (CT) data. Based on a factorial experiment, 9 different mesio-occlusal- distal (MOD) cavity designs were simulated, with three cavity wall thicknesses (1.5 mm, 2.25 mm, 3.0 mm), and three cusp reduction procedures (without cusp reduction, 2.0 mm palatal cusp reduction, 2.0 mm palatal and buccal cusp reduction). All MOD cavities were simulated with direct resin composite restoration (Gradia Direct Posterior, GC, Japan). Finite element analysis (FEA) was used to calculate von Mises stress values. Results The von Mises stresses in enamel, dentin, and resin composite were 79.3-233.6 MPa, 26.0-32.9 MPa, and 180.2-252.2 MPa, respectively. Considering the influence of cavity design parameters, cuspal reduction (92.97%) and cavity wall thickness (3.06%) significantly (P < 0.05) determined the magnitude of stress values in enamel. The influence of cavity design parameters on stress values in dentin and resin composite was not significant. When stresses for enamel, dentine, and resin composite were considered all together, palatal cusp coverage was revealed as an optimal option. Cavity wall thickness did not show a significant effect on stress values. Conclusion Based on numerical simulations, a palatal cusp reduction could be suggested for revealing lower stress values in dental tissues and restorative material. This type of cavity design should contribute to better biomechanical behavior of tooth-restoration complex, consequently providing the long-lasting clinical results

Investigation of the accuracy of close-range photogrammetry – a 3D printing case study

3D scanning of physical objects is one of the frequently used methods for generating input data for 3D printing process. Close-range photogrammetry represents a cost-efficient alternative to conventional 3D scanning. However, one of the basic problems in application of this method is accuracy, especially in the case of small objects with complex geometry. In this case study, a 3D-printed object of small dimensions was used to test the accuracy and precision of close-range photogrammetry. CAD Inspection was used to obtain measurements of the scanned model and compare it with the original CAD model, while the results were statistically analyzed. The results of statistical analysis showed that the scanning accuracy in this experiment did not depend on the particular cross-section of the model, while the precision of 3D scanning depended on the selection of cross-sectional profile curve

Finite element simplifications and simulation reliability in single point incremental forming

Single point incremental forming (SPIF) is one of the most promising technologies for the manufacturing of sheet metal prototypes and parts in small quantities. Similar to other forming processes, the design of the SPIF process is a demanding task. Nowadays, the design process is usually performed using numerical simulations and virtual models. The modelling of the SPIF process faces several challenges, including extremely long computational times caused by long tool paths and the complexity of the problem. Path determination is also a demanding task. This paper presents a finite element (FE) analysis of an incrementally formed truncated pyramid compared to experimental validation. Focus was placed on a possible simplification of the FE process modelling and its impact on the reliability of the results obtained, especially on the geometric accuracy of the part and bottom pillowing effect. The FE modelling of SPIF process was performed with the software ABAQUS, while the experiment was performed on a conventional milling machine. Low-carbon steel DC04 was used. The results confirm that by implementing mass scaling and/or time scaling, the required calculation time can be significantly reduced without substantially affecting the pillowing accuracy. An innovative artificial neural network (ANN) approach was selected to find the optimal values of mesh size and mass scaling in term of minimal bottom pillowing error. However, care should be taken when increasing the element size, as it has a significant impact on the pillow effect at the bottom of the formed part. In the range of selected mass scaling and element size, the smallest geometrical error regarding the experimental part was obtained by mass scaling of 19.01 and tool velocity of 16.49 m/s at the mesh size of 1 × 1 mm. The obtained results enable significant reduction of the computational time and can be applied in the future for other incrementally formed shapes as well