Influence of ultrasonic treatment on the change of monocrystalline silicon defective region

The article presents the results of an experimental study of ultrasonic action on monocrystalline silicon samples. The influence of the processing modes on the surface strength of the material under study was found.

Developing the Smart Sorting Screw System Based on Deep Learning Approaches

The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in real-time. As a result, the AI technology could not be widely implemented in actual factory inspections. In this study, the proposed smart sorting screw system combines the internet of things technique and an anomaly network for detecting the defective region of the screw product. The proposed system has three prominent characteristics. First, the spiral screw images are stitched into a panoramic image to comprehensively detect the defective region that appears on the screw surface. Second, the anomaly network comprising of convolutional autoencoder (CAE) and adversarial autoencoder (AAE) networks is utilized to automatically recognize the defective areas in the absence of a defective-free image for model training. Third, the IoT technique is employed to upload the screw image to the cloud platform for model training and inference, in order to determine if the defective screw product is a pass or fail on the production line. The experimental results show that the image stitching method can precisely merge the spiral screw image to the panoramic image. Among these two anomaly models, the AAE network obtained the best maximum IOU of 0.41 and a maximum dice coefficient score of 0.59. The proposed system has the ability to automatically detect a defective screw image, which is helpful in reducing the flow of the defective products in order to enhance product quality.

An experimental approach to reproduce in-plane fiber waviness in thermoplastic composites test coupons using a reverse forming method

In-plane fiber waviness is one of the defects that can occur from the stamp-forming process of thermoplastic composite (TPC) parts. The influence of this defect on the mechanical performance of multidirectional composites is not yet fully understood. The main challenge in determining the influence on mechanical properties lies in reproducing the waviness in test coupons that can subsequently be subjected to testing. This paper describes an experimental approach to reproduce representative in-plane waviness defects, specific for TPC, by reverse-forming of V-shape parts of various bend angles and inner radii. Characterization results show that this method enables the manufacturing of localized in-plane waviness in flat 24-ply quasi-isotropic C/PEEK composites with no voids. Furthermore, laminates having varying levels of maximum waviness angle ([Formula: see text]), between 14° to 64°, were successfully produced in this work. By comparing the [Formula: see text] value with the examples of industrial stamp-formed parts, it can be concluded that the developed coupon manufacturing method can reproduce waviness from TPC part production reasonably well. Finally, all of the produced laminates have defective region lengths smaller than 20 mm, localized within a predefined location which makes them well suited for standard compression test coupons.

Mechanical Degradation Analysis of an Amorphous Silicon Solar Module

This work examines the degradation of photovoltaic modules. It assesses the structural defects of amorphous silicon solar cells, which result from mechanical stress at nanoscale level. Firstly, it analyses the interface morphology, deformation, and internal delamination of a single junction amorphous silicon solar module. Secondly, it explores the interface deformation of the layers of the defective region of the module with some statistical tools including root mean root (RSM) and arithmetic mean (Rq). It used the aforementioned tools to demonstrate the effect of microstructural defects on the mechanical behaviour of the entire layers of the module. The study established that the defect observed in the module, emanated from long-term degradation of the a-Si solar cells after years of exposure to various light and temperature conditions. It tested the mechanism of mechanical degradation and its effect on the reliability and stability of the defective and non-defective regions of the module with adhesion force characterisation.

Highly non-linear solitary wave imaging method for detecting cylindrical defects in the metal plate of an adhesive composite metal structure

In this paper, a non-destructive testing (NDT) method based on highly non-linear solitary waves (HNSWs) was employed to detect defects in the metal plate of an adhesive composite metal structure. Firstly, HNSWs were generated by applying impulses to a one-dimensional granular chain of steel spheres. Proof-of-concept finite element (FE) simulations were then performed using Abaqus software to investigate the reflection behaviour of HNSWs at the interface of the chain and the two-layer material with defects. The time-of-flight (TOF) of primary reflected solitary waves (PSWs) was confirmed as a good indicator for defect localisation and sizing. A HNSW generator and detector device was used in the experiment and HNSW B-scan and C-scan inspections of adhesive composite steel specimens were achieved. The TOF imaging results accurately indicated the locations of cylindrical defects in the bottom steel block. The linear correlation between the peak value of the pulsed TOF profile and the defect diameter was determined. The contours of TOF with different values were introduced to reconstruct the shape of the circular defective region. Through selecting the correct threshold of TOF, the relative error of the area surrounded by the contour compared to the actual area of the five investigated defects could be less than 6.5%.

Conductance Tunable Suspended Graphene Nanomesh by Helium Ion Beam Milling

This paper demonstrates that the electrical properties of suspended graphene nanomesh (GNM) can be tuned by systematically changing the porosity with helium ion beam milling (HIBM). The porosity of the GNM is well-controlled by defining the pitch of the periodic nanopores. The defective region surrounding the individual nanopores after HIBM, which limits the minimum pitch achievable between nanopores for a certain dose, is investigated and reported. The exponential relationship between the thermal activation energy (EA) and the porosity is found in the GNM devices. Good EA tuneability observed from the GNMs provides a new approach to the transport gap engineering beyond the conventional nanoribbon method.

Computational Analysis of Structural Defects in Silica Aerogels

Technological advances in synthesis and preparation of aerogels have resulted in formulations that have the mechanical integrity (while retaining flexibility) to be utilized in a broad range of applications and have overcome the initial brittleness that this class of materials was once known for. Both structural and functional aerogels show a drop in performance when subjected to certain cyclic thermal or impact loading due to the wear and formation of cracks, which reduces their lifespan. Here we present the proof-of-concept of a computational toolset that connects the change in thermal profile to structural failure and degradation. In combination with an appropriate finite element (FEM) solver, we have developed a genetic algorithm that can reconstruct the size and shape of the defective region in silica aerogels given the temperatures from a sensor grid. Results show that a heatmap can be used as the foundation for reconstructing faults and defects in thermally insulating materials. Furthermore, the model developed in this study can be expanded to accommodate other material types. Experimental setup can used to benchmark and refine the computational toolset.