Modeling of Micropores Drilling Force for Printed Circuit Board Micro-holes Based on Energy Method

The quality of printed circuit board(PCB)micro-hole processing directly determines the stability of the inner and outer circuit connections.Micro-hole drilling technology is a typical method for PCB micro-hole processing.The problem of optimal control of its drilling force is one of the main factors affecting the quality of micro-hole machining.To address this problem,the thrust forces and torques in PCB drilling were first modeled and analyzed,and the corresponding prediction models were established.The drilling force analysis was carried out through the micro-hole drilling experiment,the specific cutting energy under different feed rates was calculated,the influence of the size effect was clarified,and the accuracy of the prediction model was verified.The result shows that during the drilling of glass fiber cloth,changes in the material removal mechanism are induced as the feed per revolution is varied.When the feed per revolution is less than the tool edge radius,the glass fiber is not cut by the main cutting edge,but is crushed and broken.When the feed per revolution is greater than the radius of the tool edge,the glass fiber is cut by the main cutting edge.At the same time,the established analytical model can accurately reflect the influence of the size effect on the drilling torque in PCB micro-hole drilling,and the error is within 10%.This method has certain practical application value in controlling PCB micro hole processing quality.

Detection Algorithm of Surface Defect Word on Printed Circuit Board

For Printed Circuit Board(PCB)surface defect detection,traditional detection methods mostly focus on template matching-based reference method and manual detections,which have the disadvantages of low defect detection efficiency,large errors in defect identification and localization,and low versatility of detectionmethods.In order to furthermeet the requirements of high detection accuracy,real-time and interactivity required by the PCB industry in actual production life.In the current work,we improve the Youonly-look-once(YOLOv4)defect detection method to train and detect six types of PCB small target defects.Firstly,the original Cross Stage Partial Darknet53(CSPDarknet53)backbone network is preserved for PCB defect feature information extraction,and secondly,the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process.Then,the K-means++clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection,which further improves the recognition and localization of small PCB defects.Finally,the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms.The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%,which has better detection capability,lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.

Optimization of low-temperature alkaline smelting process of crushed metal enrichment originated from waste printed circuit boards

A novel low-temperature alkaline smelting process is proposed to convert and separate amphoteric metals in crushed metal enrichment originated from waste printed circuit boards. The central composite design was used to optimize the operating parameters,in which mass ratio of Na OH-to-CME, smelting temperature and smelting time were chosen as the variables, and the conversions of amphoteric metals tin, lead, aluminum and zinc were response parameters. Second-order polynomial models of high significance and3 D response surface plots were constructed to show the relationship between the responses and the variables. Optimum area of80%-85% Pb conversion and over 95% Sn conversion was obtained by the overlaid contours at mass ratio of Na OH-to-CME of4.5-5.0, smelting temperature of 653-723 K, smelting time of 90-120 min. The models were validated experimentally in the optimum area, and the results demonstrate that these models are reliable and accurate in predicting the smelting process.

Efficient recovery of valuable metals from waste printed circuit boards by microwave pyrolysis

The recycling of waste printed circuit board(WPCBs) is of great significance for saving resources and protecting the environment. In this study, the WPCBs were pyrolyzed by microwave and the contained valuable metals Cu, Sn and Pb were recovered from the pyrolyzed WPCBs. The effect of pyrolysis temperature and time on the recovery efficiency of valuable metals was investigated. Additionally, the characterization for morphology and surface elemental distribution of pyrolysis residues was carried out to investigate the pyrolysis mechanism. The plastic fiber boards turned into black carbides, and they can be easily separated from the metals by manual. The results indicate that 91.2%, 96.1% and 94.4% of Cu, Sn and Pb can be recovered after microwave pyrolysis at 700 °C for 60 minutes. After pyrolysis, about 79.8%(mass)solid products, 11.9%(mass) oil and 8.3%(mass) gas were produced. These gas and oil can be used as fuel and raw materials of organic chemicals, respectively. This process provides an efficient and energy-saving technology for recovering valuable metals from WPCBs.

A micro-chip exploding foil initiator based on printed circuit board technology

Conventional exploding foil initiator (EFI) in ignition or detonation applications hosts many performance advantages, but was hindered by the bulky, inaccurate, inefficient and expensive shortcomings. We highlight a novel micro-chip exploding foil initiator (McEFI) using printed circuit board (PCB) technology. The structural parameters were determined based on energy coupling relationship at the component interfaces. Next, the prototype McEFI has been batch-fabricated using PCB technology, with a monolithic structure of 7.0 mm (l) × 4.5 mm (w) × 4.0 mm (δ). As expected, this PCB-McEFI illustrated the successful firing validations for explosives pellets. This paper has addressed the cost problem in both military munitions and civil markets wherever reliable, insensitive and timing-dependent ignition or detonation are involved.

Pyrolysis behaviour and combustion kinetics of waste printed circuit boards

The effective recycling of waste printed circuit boards(WPCBs)can conserve resources and reduce environmental pollution.This study explores the pyrolysis and combustion characteristics of WPCBs in various atmospheres through thermogravimetric and Gaussian fitting analyses.Furthermore,this study analyses the pyrolysis products and combustion processes of WPCBs through thermogravimetric and Fourier transform infrared analyses(TG-FTIR)and thermogravimetry-mass spectrometry(TG-MS).Results show that the pyrolysis and combustion processes of WPCBs do not constitute a single reaction,but rather an overlap of multiple reactions.The pyrolysis and combustion process of WPCBs is divided into multiple reactions by Gaussian peak fitting.The kinetic parameters of each reaction are obtained by the Coats-Redfern method.In an argon atmosphere,pyrolysis consists of the overlap of the preliminary pyrolysis of epoxy resin,pyrolysis of small organic molecules,and pyrolysis of brominated flame retardants.The thermal decomposition process in the O_(2) atmosphere is mainly divided into two reactions:brominated flame retardant combustion and epoxy combustion.This study provided the theoretical basis for pollution control,process optimization,and reactor design of WPCBs pyrolysis.

Study on High-power LED Heat Dissipation Based on Printed Circuit Board

In order to study the role of printed circuit board(PCB)in high-power LED heat dissipation,a simple model of high-power LED lamp was designed.According to this lamp model,some thermal performances such as thermal resistances of four types of PCB and the changes of LED junction temperature were tested under three different working currents.The obtained results indicate that LED junction temperature can not be lowered significantly with the decreasing thermal resistance of PCB.However,PCB with low thermal resistance can be matched with smaller volume heat sink,so it is hopeful to reduce the size,weight and cost of LED lamp.

Supergravity separation of Pb and Sn from waste printed circuit boards at different temperatures

Printed circuit boards（PCBs） contain many toxic substances as well as valuable metals, e.g., lead（Pb） and tin（Sn）. In this study, a novel technology, named supergravity, was used to separate different mass ratios of Pb and Sn from Pb–Sn alloys in PCBs. In a supergravity field, the liquid metal phase can permeate from solid particles. Hence, temperatures of 200, 280, and 400°C were chosen to separate Pb and Sn from PCBs. The results depicted that gravity coefficient only affected the recovery rates of Pb and Sn, whereas it had little effect on the mass ratios of Pb and Sn in the obtained alloys. With an increase in gravity coefficient, the recovery values of Pb and Sn in each step of the separation process increased. In the single-step separation process, the mass ratios of Pb and Sn in Pb–Sn alloys were 0.55, 0.40, and 0.64 at 200, 280, and 400°C, respectively. In the two-step separation process, the mass ratios were 0.12 and 0.55 at 280 and 400°C, respectively. Further, the mass ratio was observed to be 0.76 at 400°C in the three-step separation process. This process provides an innovative approach to the recycling mechanism of Pb and Sn from PCBs.

An Improved Randomized Circle Detection Algorithm Using in Printed Circuit Board Locating Mark

This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform. The experimental results denote that this algorithm can locate the circular mark of Printed Circuit Board (PCB).

Stress Analysis of Printed Circuit Board with Different Thickness and Composite Materials Under Shock Loading

In this study,the deformation and stress distribution of printed circuit board(PCB)with different thickness and composite materials under a shock loading were analyzed by the finite element analysis.The standard 8-layer PCB subjected to a shock loading 1500 g was evaluated first.Moreover,the finite element models of the PCB with different thickness by stacking various number of layers were discussed.In addition to changing thickness,the core material of PCB was replaced from woven E-glass/epoxy to woven carbon fiber/epoxy for structural enhancement.The non-linear material property of copper foil was considered in the analysis.The results indicated that a thicker PCB has lower stress in the copper foil in PCBs under the shock loading.The stress difference between the thicker PCB(2.6 mm)and thinner PCB(0.6 mm)is around 5%.Using woven carbon fiber/epoxy as core material could lower the stress of copper foil around 6.6%under the shock loading 1500 g for the PCB with 0.6 mm thickness.On the other hand,the stress level is under the failure strength of PCBs with carbon fiber/epoxy core layers and thickness 2.6 mm when the peak acceleration changes from 1500 g to 5000 g.This study could provide a reference for the design and proper applications of the PCB with different thickness and composite materials.

A corrected method of distorted printed circuit board image

This paper proposes a corrected method of distorted image based on adaptive control. First, the adaptive control relationship of pixel point positions between distorted image and its corrected image is given by using polynomial fitting, thus control point pairs between the distorted image and its corrected image are found. Secondly, the value of both image distortion centre and polynomial coefficient is obtained with least square method, thus the relationship of each control point pairs is deduced. In the course of distortion image processing, the gray value of the corrected image is changed into integer with bilinear interpolation. Finally, the experiments are performed to correct two distorted printed circuit board images. The results are perfect and the mean square errors of residual error are tiny.

Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

Printed Circuit Boards(PCBs)are very important for proper functioning of any electronic device.PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs.If PCBs do not function properly then the whole electric machine might fail.So,keeping this in mind researchers are working in this field to develop error free PCBs.Initially these PCBs were examined by the human beings manually,but the human error did not give good results as sometime defected PCBs were categorized as non-defective.So,researchers and experts transformed this manual traditional examination to automated systems.Further to this research image processing and computer vision came into actions where the computer vision experts applied image processing techniques to extract the defects.But,this also did not yield good results.So,to further explore this area Machine Learning and Artificial Intelligence Techniques were applied.In this studywe have appliedDeep Neural Networks to detect the defects in the PCBS.PretrainedVGG16and Inception networkswere applied to extract the relevant features.DeepPCB dataset was used in this study,it has 1500 pairs of both defected and non-defected images.Image pre-processing and data augmentation techniques were applied to increase the training set.Convolution neural networks were applied to classify the test data.The results were compared with state-of-the art technique and it proved that the proposed methodology outperformed it.Performance evaluation metrics were applied to evaluate the proposed methodology.Precision 94.11%,Recall 89.23%,F-Measure 91.91%,and Accuracy 92.67%.

Non-toxic, high selectivity process for the extraction of precious metals from waste printed circuit boards

The work presented here focused on the extraction of gold (Au), silver (Ag) and palladium (Pd) from electronic waste using a solution of ammonium thiosulfate. Thiosulfate was used as a valid alternative to cyanide for precious metal extractions, due to its non-toxicity and high selectivity. The interactions between sodium thiosulfate, total ammonia/ammonium, precious metal concentrations and the particle size of the waste printed circuit boards (WPCBs) were studied by the response surface methodology (RSM) and the principal component analysis (PCA) to maximize precious metal mobilization. Au extraction reached a high efficiency with a granulometry of less than 0.25 mm, but the consumption of reagents was high. On the other hand, Ag extraction depended neither on thiosulfate/ammonia concentration nor granulometry of WPCBs and it showed efficiency of 90% also with the biggest particle size (0.50 < Ø < 1.00 mm). Pd extraction, similarly to Au, showed the best efficiency with the smallest and the medium WPCB sizes, but required less reagents compared to Au. The results showed that precious metal leaching is a complex process (mainly for Au, which requires more severe conditions in order to achieve high extraction efficiencies) correlated with reagent concentrations, precious metal concentrations and WPCB particle sizes. These results have great potentiality, suggesting the possibility of a more selective recovery of precious metals based on the different granulometry of the WPCBs. Furthermore, the high extraction efficiencies obtained for all the metals bode well in the perspective of large-scale applications.

Multiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection

The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and structure of PCB are getting complicated.However,there are few effective and accurate PCB defect detection methods.There are high requirements for the accuracy of PCB defect detection in the actual pro-duction environment,so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method(DDMV)and the defect detection by multi-model learning method(DDML).With the purpose of reducing wrong and missing detection,the DDMV and DDML integrate multiple defect detection networks with different fusion strategies.The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets.The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score,and the area under curve value of DDML is also higher than that of any other individual detection model.Furthermore,compared with DDMV,the DDML with an automatic machine learning method achieves the best performance in PCB defect detection,and the Fl-score on the two datasets can reach 99.7%and 95.6%respectively.

Products Made from Nonmetallic Materials Reclaimed from Waste Printed Circuit Boards

Printed circuit boards （PCBs） are in all electronic equipment, so with the sharp increase of electronic waste, the recovery of PCB components has become a critical research field. This paper presents a study of the reclaimation and reuse of nonmetallic materials recovered from waste PCBs. Mechanical processes, such as crushing, milling, and separation, were used to process waste PCBs. Nonmetallic materials in the PCBs were separated using density-based separation with separation rates in excess of 95%. The recovered nonmetals were used to make models, construction materials, composite boards, sewer grates, and amusement park boats. The PCB nonmetal products have better mechanical characteristics and durability than traditional materials and fillers. The flexural strength of the PCB nonmetallic material composite boards is 30% greater than that of standard products. Products derived from PCB waste processing have been brought into industrial production. The study shows that PCB nonmetals can be reused in profitable and environmentally friendly ways.

Preparation of pure SnO_2 powders from tin slag of printed circuit boards waste

The recycling method and principle of SnO2 from the tin slag of printed circuit boards（PCB） waste were investigated. In this study, pure SnO2 powders were obtained through a multi-step process including ball-milling, roasting, dissolving, precipitating, and pickling. The total recovery rate of tin can be up to 91 %. The SnO2 powders obtained is the single phase, and the content of SnO2 is up to 99.9 %. However, the SnO2 particles are easier to agglomerate during the precipitation process. The agglomerate SnO2 particles are about 7.778 lm in mean particle size（D50）. This preparation method presents a viable alternative for the tin slag recycling. The tin is not only recycled, but also reused directly to prepare pure SnO2 powders.

Self-catalytic pyrolysis thermodynamics of waste printed circuit boards with co-existing metals

Waste printed circuit boards(WPCBs)are generated increasingly recent years with the rapid replacement of electric and electronic products.Pyrolysis is considered to be a potential environmentally-friendly technology for recovering organic and metal resources from WPCBs.Thermogravimetric analysis and kinetic analysis of WPCBs were carried out in this study.It showed that the co-existing metals(Cu,Fe,Ni)in WPCBs have positive self-catalytic influence during the pyrolysis process.To illustrate their catalytic effects,the apparent activation energy was calculated by differential model.Contributions of different reactions during catalytic pyrolysis process was studied and the mechanism function was obtained byŠesták-Berggren model.The results showed that Cu,Fe,Ni can promote the reaction progress and reduce the apparent activation energy.Among the three metals,Ni plays better catalytic role than Cu,then Fe.This work provides theoretical base for understanding the three metals’catalytic influence during the pyrolysis of non-metal powders in WPCBs.

Copper recovery from waste printed circuit boards concentrated metal scraps by electrolysis

Copper recovery is the core of waste printed circuit boards （WPCBs） treatment. In this study, we proposed a feasible and efficient way to recover copper from WPCBs concentrated metal scraps by direct electrolysis and thctors that affect copper recovery rate and purity, mainly CuSO4.5H2O concentration, NaCI concentration, H2SO4 concentration and current density, were discussed in detail. The results indicated that copper recovery rate increased first with the increase ofCuSO4- 5H2O, NaCI, H2SO4 and current density and then decreased with further increasing these conditions. NaCI, H2SO4 and current density also showed a similar impact on copper purity, which also increased first and then decreased. Copper purity increased with the increase of CuSO4.5H2O. When the concentration of CuSO4-5H2O NaCI and H2oSO4 was respectively 90, 40 and 118 g/L and current density was 80 mA/cm-, copper recovery rate and purity was up to 97.32% and 99.86%, respectively. Thus, electrolysis proposes a feasible and prospective approach for waste printed circuit boards recycle, even for e-waste, though more researches are needed for industrial application.

Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

Characterization of a New DC-Glow Discharge Plasma Set-Up to Enhance the Electronic Circuits Performance

The (DC-GDPAU) is a DC glow discharge plasma experiment that was designed, established, and operated in the Physics Department at Ain Shams University (Egypt). The aim of this experiment is to study and improve some properties of a printed circuit board (PCB) by exposing it to the plasma. The device consists of cylindrical discharge chamber with movable parallel circular copper electrodes (cathode and anode) fixed inside it. The distance between them is 12 cm. This plasma experiment works in a low-pressure range (0.15 - 0.70 Torr) for Ar gas with a maximum DC power supply of 200 W. The Paschen curves and electrical plasma parameters (current, volt, power, resistance) characterized to the plasma have been measured and calculated at each cm between the two electrodes. Besides, the electron temperature and ion density are obtained at different radial distances using a double Langmuir probe. The electron temperature (KTe) was kept stable in range 6.58 to 10.44 eV;whereas the ion density (ni) was in range from 0.91 × 1010 cm−3 to 1.79 × 1010 cm−3. A digital optical microscope (800×) was employed to draw a comparison between the pre-and after effect of exposure to plasma on the shaping of the circuit layout. The experimental results show that the electrical conductivity increased after plasma exposure, also an improvement in the adhesion force in the Cu foil surface. A significant increase in the conductivity can be directly related to the position of the sample surfaces as well as to the time of exposure. This shows the importance of the obtained results in developing the PCBs manufacturing that uses in different microelectronics devices like those onboard of space vehicles.