Increasing flexibility of self-piercing riveting by reducing tool–geometry combinations using cluster analysis in the application of multi-material design

A frequently used mechanical joining process that enables the joining of dissimilar materials is self-piercing riveting. Nevertheless, the increasing number of materials as well as material–thickness combinations leads to the need for a large number of rivet–die combinations as the rigid tool systems are not able to react to changing boundary conditions. Therefore, tool changes or system conversions are needed, resulting in longer process times and inflexibility of the joining processes. In this investigation, the flexibility of the self-piercing riveting process by reducing the required tool–geometry combinations is examined. For this purpose, various joints consisting of similar as well as dissimilar materials with different material thickness are sampled and analysed. Subsequently, a cluster algorithm is used to reduce the number of rivet–die combinations required. Finally, the effect of the changed tool geometries on both the joint formation and the joint load-bearing capacity is investigated. The investigation showed that a reduction by 55% of the required rivet–die combinations was possible. In particular, the rivet length influences the joint formation and the joint load-bearing capacity. An exclusive change of the die (e.g. die depth or die diameter) did not show a significant influence on these parameters.

Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction

A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancy defects. Thinner filament cause lowering of operation current from 500 μA to 100 μA and also improves the reset current (from >400 μA to <100 μA). Switching material thickness reduction also cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour data retention and >1000 P/E endurance with 100 ns pulses.

Arithmetic Relationship between Fracture Load and Material Thickness of Resin-Based CAD-CAM Restorative Materials

Data on the long-term behavior of computer-aided designed/computer-aided manufactured (CAD-CAM) resin-based composites are sparse. To achieve higher predictability on the mechanical behavior of these materials, the aim of the study was to establish a mathematical relationship between the material thickness of resin-based materials and their fracture load. The tested materials were Lava Ultimate (LU), Cerasmart (GC), Enamic (EN), and Telio CAD (TC). For this purpose, 60 specimens were prepared, each with five different material thicknesses between 0.4 mm and 1.6 mm (N = 60, n = 12). The fracture load of all specimens was determined using the biaxial flexural strength test (DIN EN ISO 6872). Regression curves were fitted to the results and their coefficient of determination (R2) was computed. Cubic regression curves showed the best R2 approximation (LU R2 = 0.947, GC R2 = 0.971, VE R2 = 0.981, TC R2 = 0.971) to the fracture load values. These findings imply that the fracture load of all tested resin-based materials has a cubic relationship to material thickness. By means of a cubic equation and material-specific fracture load coefficients, the fracture load can be calculated when material thickness is given. The approach enables a better predictability for resin-based restorations for the individual patient. Hence, the methodology might be reasonably applied to other restorative materials.

Design and Optimization of Microwave Sensor for the Non-Contact Measurement of Pure Dielectric Materials

This article presents an optimized microwave sensor for the non-contact measurement of complex permittivity and material thickness. The layout of the proposed sensor comprises the parallel combination of an interdigital capacitor (IDC) loaded at the center of the symmetrical differential bridge-type inductor fabricated on an RF-35 substrate (εr = 3.5 and tanδ = 0.0018). The bridge-type differential inductor is introduced to obtain a maximum inductance value with high quality (Q) factor and low tunable resonant frequency. The central IDC structure is configured as a spur-line structure to create a high-intensity coupled electric field (e-field) zone, which significantly interacts with the materials under test (MUTs), resulting in an increased sensitivity. The proposed sensor prototype with optimized parameters generates a resonant frequency at 1.38 GHz for measuring the complex permittivity and material thickness. The experimental results indicated that the resonant frequency of the designed sensor revealed high sensitivities of 41 MHz/mm for thickness with a linear response (r2 = 0.91567), and 53 MHz/Δεr for permittivity with a linear response (r2 = 0.98903). The maximum error ratio for measuring MUTs with a high gap of 0.3 mm between the testing sample and resonator is 6.52%. The presented performance of the proposed sensor authenticates its application in the non-contact measurement of samples based on complex permittivity and thickness.

Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.

EVALUATION OF THE EFFECTS OF DRILLING PARAMETERS, TOOL GEOMETRY AND CORE MATERIAL THICKNESS ON THRUST FORCE AND DELAMINATION IN THE DRILLING OF SANDWICH COMPOSITES

This study examined the effects of drilling parameters, tool geometry, and core material thickness (CMT) on thrust force and the delamination factor in the drilling of sandwich composites. Aluminum honeycomb (10 and 15[Formula: see text]mm in thickness) was used as the core material, with carbon fiber-reinforced polymer (CFRP) as the top and bottom surfaces. In the drilling experiments, three different cutting speeds (60, 78 and 100[Formula: see text]m/min) and two different feed rates (0.05 and 0.075[Formula: see text]mm/rev) were used. Drills having a diameter of 6.35[Formula: see text]mm and three different geometries (candlestick drills, twist drills and dagger drills) were used in the experiments. At the end of the experiments, thrust force was seen to increase with increased feed rate and CMT. Increased cutting speed generally decreased the thrust forces and the minimum thrust force was achieved with the 10 mm thick core material, 0.05[Formula: see text]mm/rev feed rate and 100[Formula: see text]m/min cutting speed using the dagger drill. The delamination factor at the entrance area was very low when drilling the sandwich composites and there was no significant difference based on drilling parameters, tool geometry, or CMT. Tool geometry was the main effective factor on exit delamination, and the highest delamination occurred with the use of the candlestick drill. Although increased feed rate increased delamination with all tools, with the dagger drill, increased cutting speed led to a severe increase. Delamination, tearing, and uncut fiber formation were observed when images of the exit areas of the drilled holes were examined.