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In invisible orthodontics, the role of composite attachments in facilitating complex tooth movements is crucial. This study, which evaluates the efficacy of a novel clinical attachment procedure, holds significant implications for the field. The technique used two templates (one pre-drilled and the other pre-loaded with high-viscosity composites) and was compared with the standard procedure. Fifty attachments were planned for four dental arch prototypes. Dental impressions were taken using digital scans for virtual planning and after tested techniques. The stereolithographic files (STL) obtained were aligned with those of the virtual planning, and a colorimetric map was used to evaluate the composite resin’s maximum excess and defect deviation. The enamel–resin interfaces were observed by scanning electron microscopy (SEM). The Fisher test for the distribution of detachments and morphological defects and the Mann–Whitney test for the maximum values of excess and defect were used. No significant results were found between groups for morphological defects and detachments, and the maximum values of defect and excess were reported. SEM images for the experimental technique showed integrated adhesion. This innovative procedure, which has proven reliable and operationally straightforward, holds promise, instilling confidence in its practicality and potential to advance the field of orthodontics.

Geometric deviation associated with the assembly of sheet metal is a general concern for manufacturers. The typical assembly step involves a sequence of events that exert forces on the parts to enforce them to the nominal condition and to connect the parts together. The simulation and optimization of the assembly steps often neglect the sequence of operations due to the problem and computation complexity. This paper investigates the influence of the clamping sequence in the body-in-white (BIW) manufacturing process on the geometrical quality of the assembly. An approach for modeling clamping sequences for non-rigid variation simulation is introduced in a digital twin context, taking the part deviation into consideration. An optimization method is proposed to achieve minimum geometric deviation after clamping the parts and welding them together. The method is successfully applied on two reference assemblies, and the results show that the sequence of clamping can impact the total geometric deviation up to 31%. Combining clamping and welding sequence optimization can enhance the quality improvement to 77% after releasing the assembly from the fixture and springback.

The spindle of a machine tool plays a key role in machining because the wear of a spindle might result in inaccurate production and decreased productivity. To understand the condition of a machine tool, a vector-based convolutional fuzzy neural network (vector-CFNN) was developed in this study to diagnose faults from signals. The developed vector-CFNN mainly comprises a feature extraction part and a classification part. The feature extraction phase encompasses the use of convolutional layers and pooling layers, while the classification phase is facilitated through the deployment of a fuzzy neural network. The fusion layer plays an important role by being placed between the feature extraction and classification parts. It combines the characteristics and then passes the feature information to the classification part to improve the model’s performance. The developed vector-CFNN was experimentally evaluated against existing fusion methods; vector-CFNN required fewer parameters and achieved the highest average accuracy (99.84%) in fault diagnosis relative to conventional neural networks, fuzzy neural networks, and convolutional neural networks. Moreover, vector-CFNN achieved superior fault diagnosis using spindle vibration signals and required fewer parameters relative to its counterparts, indicating its feasibility for online spindle vibration monitoring.

In additive manufacturing (AM), the surface roughness of the deposited parts remains significantly higher than the admissible range for most applications. Additionally, the surface topography of AM parts exhibits waviness profiles between tracks and layers. Therefore, post-processing is indispensable to improve surface quality. Laser-aided machining and polishing can be effective surface improvement processes that can be used due to their availability as the primary energy sources in many metal AM processes. While the initial roughness and waviness of the surface of most AM parts are very high, to achieve dimensional accuracy and minimize roughness, a high input energy density is required during machining and polishing processes although such high energy density may induce process defects and escalate the phenomenon of wavelength asperities. In this paper, we propose a systematic approach to eliminate waviness and reduce surface roughness with the combination of laser-aided machining, macro-polishing, and micro-polishing processes. While machining reduces the initial waviness, low energy density during polishing can minimize this further. The average roughness (Ra=1.11μm) achieved in this study with optimized process parameters for both machining and polishing demonstrates a greater than 97% reduction in roughness when compared to the as-built part.

Multifunctional composites offer a higher strength to weight ratio, electrical properties, etc., thereby providing possible solutions for replacing the physical electrical wirings in aircraft. The lack of research on the coupled multifunctional characterization of 3D printed composites flexural-electrical properties is the main reason for its unsuitability in aerospace applications. The proposed method evaluates multifunctional flexural-electrical properties of 3D printed multifunctional carbon fiber composites. Traditional methods for conducting structural and electrical analyses for aircraft certification do not accommodate new technologies that are not yet proven. Such technologies are additive manufacturing (AM) techniques, multifunctional composite structures, and the certification requirements for 3D printed multifunctional carbon fiber composites for use in aircraft. In this study, the multifunctional 3D printed specimens were concurrently evaluated for flexural-electrical properties using three-point bending and electrical conductivity tests. The results showed that the multifunctional properties included the maximum flexural strength of 271 MPa and the maximum electrical resistance of 55.1 G Ohms, with the failure modes and mechanisms found to be consistent with the traditional composites. Due to its infancy, the existing AM techniques, and the use of the multifunctional carbon fiber composites manufactured using those AM technologies, are not implemented on a large commercial scale.

In this paper, a TiNiCu shape memory alloy single-wall structure was fabricated by the directed energy deposition technique with a mixture of elemental Ti, Ni, and Cu powders following the atomic percentage of Ti50Ni45Cu5 to fully utilize the material flexibility of the additive manufacturing process to develop ternary shape memory alloys. The chemical composition, phase, and material properties at multiple locations along the build direction were studied, using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, Vickers hardness testing, tensile testing, and differential scanning calorimetry. The location-dependent compositions of martensitic TiNi and austenitic TiNi phases, mechanical properties, and functional properties were investigated in detail. Variations were found in atomic compositions of Ti, Ni, and Cu elements along the build direction due to the complex interaction between elemental powders and laser processing. Good correlations were present among the chemical composition, phase constituent, hardness, and feature of phase transformation temperatures at various locations. The ultimate tensile strength of the as-deposited TiNiCu alloy is comparable with the previously reported additively manufactured TiNi binary alloys. By adding Cu, a much lower thermal hysteresis was achieved, which shows good feasibility of fabricating ternary TiNiCu shape memory alloys, using elemental powders in the directed energy deposition to adjust the thermal hysteresis.

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

A novel adaptive displacement-controlled test setup was developed for fatigue testing on mini specimens. In property characterization of additive manufacturing materials, mini specimens are preferred due to the specimen preparation, and manufacturing cost but mini specimens demonstrate higher fatigue strength than standard specimens due to the lower probability of material defects resulting in fatigue. In this study, a dual gauge section Krouse type mini specimen was designed to conduct fatigue tests on additively manufactured materials. The large surface area of the specimen with a constant stress distribution and increased control volume as the gauge section may capture all different types of surface and microstructural defects of the material. A fully reversed bending (R = −1) fatigue test was performed on simply supported specimens. In the displacement-controlled mechanism, the variation in the control signal during the test due to the stiffness variation of the specimen provides a unique insight into identifying the nucleation and propagation phase. The fatigue performance of the wrought 304 and additively manufactured 304L stainless steel was compared applying a control signal monitoring (CSM) method. The test results and analyses validate the design of the specimen and the effective implementation of the test bench in fatigue testing of additively manufactured materials.