Burn Marks Research Articles

In-cavity pressure measurements for failure diagnosis in the injection moulding process and correlation with numerical simulation

The pressure profile analysis for monitoring and diagnosis processing failures during an injection moulding process, such as burn marks and short shots, is a useful instrument for process and part quality control and production with zero defects and greater efficiency. Therefore, this work aims to demonstrate the in-cavity pressure monitoring feasibility for failure diagnosis and injection moulding process optimization. The methodology used to analyse the obtained pressure variation is presented. The results were correlated to the typical cavity pressure profile, which enables the acquisition of information about the process and the moulding tool. This way, it was possible to determine the origin of the defects present in the injected parts, focusing not only on the velocity to pressure switchover but also on the initial part of the curve, related to the filling phase. Moreover, the obtained results and the studied processing conditions were correlated with the injection moulding process simulation.

Finishing performance of polyurethane-based flexible tools fabricated using solvent casting and particulate leaching method

Oxygen-free high thermal conductivity (OFHC) copper is a common material for mirrors in different optical applications. As it is a soft material and easy to scratch, the nanofinishing using traditional finishing processes is difficult. A flexible abrasive tool that can adapt to the shape of the component and apply uniform and gentle pressure is required to achieve a high-quality surface finish on the soft materials. Flexible abrasive tools are fabricated using the solution casting and dissolution method with open porous microstructure and uniformly dispersed silicon carbide particles in a polyurethane polymer matrix. Mechanical and finishing characteristics of the fabricated tools are obtained under dry and wet conditions. The developed tools are self-lubricating and self-replenishing, reducing tool wear and eliminating burn marks on finished surfaces. Scanning electron micrographs and energy dispersive spectroscopy are used to investigate the tool microstructure and abrasive particle distribution before and after finishing. The mechanical behaviour of tools is studied using a dynamic mechanical analyser. Porosity is measured using the interconnected void volume, and fluid absorption rate is calculated through weight measurement before and after fluid absorption. The surface finish of finished surfaces is analysed using an optical surface profilometer. Results show that the flexible abrasive tool performs the best under wet conditions. In a single pass, a wet salt-leached tool achieved a reduction of 42.2 % on a surface having a lower initial roughness, whereas a wet pure polymer tool achieved a reduction of 45.7 % on a surface having a higher initial roughness. Finally, areal surface roughness of 34 nm is achieved in 16 mins of finishing experiment using pure polymer tool followed by salt-leached tool.

Identification and characterization of the grinding burns by eddy current method

Abstract This work presents an attempt to identify local changes in materials caused by local grinding burnings by using the eddy current (EC) method. The locally heat-treated AISI 9310 steel specimen was prepared by using a laser surfacing process to imitate three different grinding burns. These burn marks were characterized in terms of changes in microstructure and hardness on the surface and cross-section of the specimen. On such a basis, the depth of the heat-affected zone caused by the grinding tool was examined. Subsequently, the specimen was subjected to the EC measurements for the quantitative description of the signal from each of the defects by using a commercial NORTEC 600D flaw detector working in specimen scanning mode and with a pencil probe. The changes in the amplitude and the phase angle of the signal from three defects indicate the possibility to identify burns along with their quantitative description and subsequent estimation of their depth. The differences in the phase angle value, related to the local changes in the stress state, serve as an effective indicator of the specimen overheating degree in the area of the EC induction.

Optimization of Vacuum Forming Parameter Settings to Minimize Burning Defect on Strawberry Packaging Products Using the Taguchi Method

Packaging products are being developed by PT. ATMI IGI – Center. The process of making these products uses the Vacuum forming method. The packaging product is made of PP (Polypropylene) and is used to pack strawberries. There are several problems during the production process, one of which is that there are still product defects burning or often called burn marks. These problems are caused by the machining parameters that have not been standardized, and based on trial eror or experience of the operator. The vacuum forming machine used in this production is the Formech 508FS Vacuum Forming machine. The optimized parameters are heat zone, heat time, and stand by temperature. Optimizing these parameters using the Taguchi method, with an orthogonal array with 9 trials, and the selected signal to noise ratio (SNR) is smaller is better. The response of this research is the reduction of burn mark product defects. The results showed that to reduce burn mark product defects, is to set the parameter heat zone 1 by 90%, heating zone 2 is 80%, heating time 45s, and stand by temperature 60%. Based on this arrangement, the product defects obtained only reached 13%. PT. ATMI IGI-Center is expected to be able to use these parameter settings for the strawberry packaging production process.

An explanatory study on defects in plastic molding parts caused by machine parameters in injection molding process

In the Injection Molding process, the output product quality is dependence on various attributes such as product geometry, the material used for manufacturing, and the machining parameters (process parameters). Here it is an exaggeration to say that the parameters associated with the molding process play a crucial role in the production of high-quality products. In the present study, the causal approach is used to recognize the problems associated with the injection molding components to cause defects such as shrinkage, flow lines, sink marks, burn marks, short shots, warpage, etc. This study aims to reveal the basic understanding of the injection molding process and the importance of machining parameters in moldings. This study will brief the type of defects and their causing machining parameters to the industrial practitioners/researchers. In addition, the study will suggest the parameters to set out on the machine so that the final product will be free from any defect.

Hotspot testing of glass/backsheet and glass/glass PV modules pre-stressed in extended thermal cycling

This paper investigates the effect of hotspot (HS) stress endurance of two of the latest designs of monocrystalline modules: a half-cell glass/backsheet (G/B) module and a full-cell glass/glass (G/G) module. These modules have already been pre-stressed in extended thermal cycling with 600 cycles per the IEC 61215 standard to represent field-stressed modules. This study differs from the other conventional studies wherein only fresh modules are subjected to hotspot endurance stress. The G/G module reached a maximum temperature of approximately 200 °C at a cell shading of 25 %, 55 °C higher than the maximum hotspot temperature of 145 °C in the G/B module. A significant burn mark, without glass shattering, was observed in the hotspot-stressed cell of the G/G module due to the current mismatch induced by partial shading. Most of the cells in the G/G module appear to be severely damaged (severe dark areas), as observed in the electroluminescence (EL) image, while the dark regions were rarely present in the G/B module. The EL image also illustrates multiple cell cracks that resist current flow and eventually contribute to the full-cell module degradation. About 8.3 % degradation in maximum power was observed for the G/G module and 1.3 % for the G/B module after the sequential stress tests. The lower degradation in the G/B module can potentially be attributed to its design, which comprises of two parallel strings, each having 72-half-cells and benefits from lower heat dissipation. This indicates that the half-cell design could potentially minimize the hotspot degradation and failures in crystalline-silicon modules.

The role of growth regulators in the reduction of xenobiotic pesticide exposures in the hybrid tea rose culture

Phytotoxic-chemical burn is a very common occurrence in modern agriculture, which leads to a noticeable decrease in the productivity of cultivated plants. The use of growth regulators with immunoprotective functions can neutralize the harmful effects of this phenomenon. Studies conducted on three varieties of hybrid tea roses (Red Naomi, Avalange and Jumilia) showed that growth regulators significantly reduced the xenobiotic effect of those used in the technology of growing cut crops in greenhouses. A higher protective effect against pesticide burn was observed when rose bushes were sprayed with a preparation from the class of brassinosteroids – Epin-extra. In this variant, there was a significant decrease in the manifestation of leaf burn, depending on the variety, by 2.4–3.8 times compared with the control. The protective effect of the drug from the group of poly-beta-hydrooxibutyric acid, Albite, turned out to be somewhat lower. Burn marks in this variant were 1.2–1.5 times less than in the control variant. The least protective effect against chemical burns was observed when rose plants were treated with a preparation of the gibberellin class, Buton II. The treatment of hybrid tea rose bushes with growth regulators in protection against pesticide burns significantly increased the yield of marketable products when cultivated in closed ground. The use of Epin-extra provided the highest yield of marketable shoots on average over four series of experiments for the Avalange variety by 43%, for the Jumilia variety by 46%, and for the Red Naomi variety by 44% compared to the control. Among the varieties studied, Avalange proved to be more resistant to chemical burn and the most productive in all climatic seasons of the year. In descending order of the productivity of a hybrid tea rose, the seasonal periods can be arranged as follows: summer-spring-autumn-winter.

Effect of the meltdown on thermoplastic joint produced by quasi-simultaneous laser transmission welding

This study aims to investigate the effect of the polymer pair meltdown process and mechanical properties using three different laser transparencies 20%, 30% and modulated, ranging from a minimum 27% to a maximum 47%. The cross-sections of joints were analysed with an optical microscope. Burst pressure was performed to evaluate the strength of welded joints produced with different laser welding process parameters. Welding parameters, scanning speed and meltdown were varied and their influence on the quality of the polymer joint was recorded and evaluated. With the increase of meltdown, a more molten volume of polymer can be seen in the cross-section area. It was shown that with the increase of meltdown, the formation of pores occurred due to the high energy concentration obtained within each laser scan passing across the weld seam. The decrease of burst pressure in all polymer pairs was observed with 0.33 mm and 0.34 mm targeted meltdown. Burn marks and burned lines were observed in joints with 20% laser transparency. Modulated laser transparency influenced non-homogeneous temperature distribution inside the joint, which influenced the formation of heat-affected zones (HAZ) and pores. A study of the influence of cooling time on joint formation showed that it is possible to variate total meltdown while changing cooling time until the joint is solidified. • Within the increase of meltdown, bigger weld flash occurred inside weld seam. • Peak weld strength observed at specific meltdown values. • The decrease of weld strength is influenced by defect formation inside seam. • Non-homogeneous melting, HAZ observed with modulated laser transparency polymers. • Total meltdown can be controlled by modulating cooling time.

Automatic detection of visual faults on photovoltaic modules using deep ensemble learning network

The present study proposes an ensemble-based deep neural network (DNN) model for autonomous detection of visual faults such as glass breakage, burn marks, snail trail, and discoloration, delamination on various photovoltaic modules (PVM). The proposed technique utilizes an image dataset captured by RGB (Red, Green, Blue) camera mounted on an unmanned aerial vehicle (UAV). In the first step, the images are preprocessed by deriving spatial and frequency domain features, such as discrete wavelet transform (DWT), texture, grey level co-occurrence matrix (GLCM), fast Fourier transform (FFT), and grey level difference method (GLDM). The processed images are inserted as input in the proposed ensemble-based deep neural network (DNN) model in order to detect any visual faults on the photovoltaic modules (PVM). The performance of the proposed model is evaluated through classification accuracy, receiver operating characteristic (ROC) curve, and confusion matrix. The results demonstrate that the proposed ensemble-based deep neural network (DNN) model, along with the random forest classifier, achieved a classification accuracy of 99.68% for detecting visual faults on the PV modules. To verify the performance and robustness of the proposed model, we compare our model’s results to those of various classification approaches described in the literature. The suggested approach is compatible with the commercial unmanned aerial vehicle (UAV) embedded flight management system for fault detection.

Manifestation of TASER drive stun burn marks on fabrics.

The TASER® is a type of conducted energy weapon (CEW) used with increasing frequency by law enforcement to subdue subjects in circumstances where compliance is necessary. When operated in the drive stun method of deployment, the electrodes at the head of this CEW are intended to make direct contact with a surface, generating heat and light which may result in burn marks as a by-product of the electrical discharge that occurs. This research aims to tackle a crucial gap in CEW research that fails to address the appearance of burn marks on fabrics. A drive stun duration (DSD) of 1, 3, and 5s was used with three TASER models (X26P, X2, & TASER 7) on three fabrics (white 100% cotton, 100% polyester, 35:65 cotton-polyester blend) with an underlying backing of pork hock. Using a Keyence VHX-6000 confocal microscope, high magnification images were taken to observe any qualitative changes to the fabric. On polyester fabric, with increasing DSD, darker brown discoloration occurred. Additionally, on polyester fabric, the spatial orientation of the burn marks corresponded with that of the electrodes at the muzzle of each TASER model. These features enabled the correct identification of the TASER model and DSD on polyester fabric in the blind tests performed. Evidence of burn marks on cotton and blend fabrics were both limited and inconsistent such that no features were sufficiently unique to link them to any TASER model or DSD. Despite this, overall features such as discoloration and the formation of holes on these fabric types may be sufficiently unique to suggest the use of a CEW.

Mid-life degradation evaluation of polycrystalline Si solar photovoltaic modules in a 100 kWp grid-tied system in east-central India

This study presents the mid-life degradation evaluation of a 100 kWp grid-tied solar photovoltaic (PV) power plant which is in the eleventh year of its operation. The field evaluation includes the visual inspections to identify the extent of degradation present in the PV modules, and the measurement of field current-voltage (I–V) data for maximum power deviation of the operational PV modules. Significant visual degradations were observed in many PV modules like encapsulant discoloration, snail tracks and cell cracks, burn marks on the surface of the PV module, shattering of front PV glass due to lightning strike, discoloration on metallization, corroded metal frame and hot spots. The PV module having heavy snail tracks, visible cracks and burn marks on the surface showed the highest power reduction of 95.9 W with respect to the reference module. The power degradation rate of the reference PV modules, which had no apparent visible degradation, over a period of ten years, was found to be varying between 0.16%/year to 1.72%/year. This study would be helpful in better understanding of the various types of degradations affecting the PV modules operating in the composite climate regions of east-central India.

Change in Central Macular Thickness on OCT after Pan Retinal Photocoagulation

Background: Proliferative diabetic retinopathy is the proliferation of retinal neo vessels due to uncontrolled diabetes mellitus and long duration of this disease that leads to advance diabetic eye disease . proliferative diabetic retinopathy is treated by pan retinal photocoagulation which reduces retinal oxygen demand by retinal photocoagulation. Retinal photocoagulation leads to the laser tissue interaction called thermal damage leading to coagulative necrosis of the retinal pigment epithelial cells as these cells contain melanin a dark pigment that has the ability to absorb light energy in argon laser spectrum. Objective: To determine the mean change in central macular thickness after pan retinal photocoagulation on optical coherence tomography of the macula in patients with proliferative diabetic retinopathy . Material & Methods: Our study was a Quasi experimental study conducted at Department of Ophthalmology, Services Hospital Lahore for duration of six months from April 2021 to September 2021. In this study 40 eyes from 40 patients with proliferative diabetic retinopathy were selected who underwent pan retinal photocoagulation in one sessions with 1500 burn marks. Patients in the age group of 15 to 70 years, male and female, with proliferative diabetic retinopathy in at least one eye with no history of ocular surgery or ocular trauma in last one year were included in this study . Optical coherence tomography of macula was performed before and four weeks after the session of pan retinal photocoagulation. CMT (central macular thickness) was measured for mean change in central macular thickness. Results: In this study , the mean age of patients was 48.68±1.52 years, and gender ratio of female to male patients was 1:0.77,The mean CMT before start of treatment that was considered as baseline was 230.55±5.0 and the mean CMT value after four weeks of pan retinal photocoagulation was 238.50±6.43. (p-value=0.001) Conclusion: No significant change in CMT was found from baseline CMT to four weeks after pan retinal photocoagulation. Keywords: Optical coherence tomography, pan retinal photocoagulation, Central Macular Thickness, proliferative diabetic retinopathy

Penggunaan Kaedah Aliran Angin untuk Mengatasi Masalah Kecacatan Produk Plastik

The product from plastic has occurred many defects and caused more difficulties to the worker in the injection moulding manufacturing sectors. There are some of few samples of defected plastic product such as shot short, burn mark, flashing, warping, bubbles and et cetera. Air vent is the marking line was designed on the mould. This air vent line has constructed in the centre of the ejector rod. Air vent also invented at the block in the mould or suitable places. This airflow line will occur during, while or after setting up a new mould installation processes. The position and size of the airflow line will determine the analysis of flow rate for raw material. Other factors also counted such as working experience and trial and error concepts. If the injection time is lesser than the from recommended timing (S<sup>2</sup> > S<sup>1</sup>), will cause the gases to emit from melting material will be trapped into the mould. This scenario will occur defects to the products. According to the journal papers, four (4) cases were practically addressed about plastic products defects. All study has presented and addresses cases, many benefits were obtained in terms of time and number of defected products. But this process only can be controlled if the mould maintenance and mould lifespan where repaired. Hence the cost of operation and the number of defected products is significantly decreased as well as increased the number of products.

Assessment of ignition risk in dry helical hole milling of AZ31 magnesium alloy considering the machining temperature and chip morphology

The helical hole milling process is gaining importance due to its capability to generate lower cutting forces and produce quality holes compared to the mechanical drilling process. However, high machining temperature coupled with the production of discontinuous particulate chips can amplify the risk of chip ignition and combustion. Therefore, the work addresses the risk of ignition during dry helical hole milling of AZ31 magnesium alloy by evaluating the machining temperature and chip morphology. The results showed that cutting speed highly influenced the machining temperature. In contrast, the rise in the machining temperature with tangential feed and the helical pitch was moderate. Lower machining temperatures can be accomplished by maintaining the cutting speed at 30 m/min, tangential feed at 0.02 mm/z, and axial pitch at 0.1 mm/rev. The investigation also revealed a high degree of dominance of helical milling variables on the chip morphology. The discontinuous cut at the peripheral edge produced short comma-shaped chips, while the chips were characterized as fragmented arc-shaped, conical, and ribbon-type chips at the front cutting edge. The free surface of the chips was marked by the presence of lamellar structures whose thickness increased with the chip size. Nevertheless, no burn marks were noted on the chip surface, even at higher machining temperatures. The results demonstrate that the risk of chip ignition resulting in fire hazards is relatively low, and the helical hole milling can be a sustainable alternative to the dry drilling process.

Machine vision based fault diagnosis of photovoltaic modules using lazy learning approach

Machine Vision is an advanced and powerful imaging based technique that has been applied in various fields like robotics, inspection and process control. Machine vision, in industrial terms, is termed as a subcategory of computer vision. The primary aim of the proposed study is to distinguish various visual fault conditions that hinder the performance and life span of photovoltaic (PV) modules using computer vision and a machine learning approach. Literatures state that thermographic and electroluminescence images were used in deep learning for identifying faults in photovoltaic modules. However, the effectiveness of normal RGB images with the fusion of deep learning and machine learning techniques is less explored. This paper deals with the classification of normal RGB images of PV modules acquired using a fusion of deep learning and machine learning techniques. Visual faults like delamination, snail trail, burn marks, glass breakage and discoloration that occur in a photovoltaic module (PVM) were considered in the study. A machine learning approach was used to handle this problem which contains three stages: (i) feature extraction, (ii) feature selection and (iii) feature classification. Initially, the features from the aerial images of PVM (acquired from unmanned aerial vehicles (UAVs) equipped with digital cameras) were extracted using convolutional neural networks (CNN). Secondly, J48 decision tree algorithm was employed to select the features of utmost significance and dominance from the extracted image features. Finally, a set of lazy classifiers like locally weighted learning (LWL), K-star algorithm (KS), nearest neighbor (NN) and k-nearest neighbor (kNN) were adopted to execute the classification task on the selected image features. The classification accuracies of all the aforementioned classifiers were compared and it was found that the k-nearest neighbor classifier achieved a maximum accuracy of 98.95% with a lesser computational time of 0.04 s.

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Revolution and Power in Puerto Rican Nonfiction

Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features

ABSTRACT Visual faults in photovoltaic (PV) modules persist as a problem that can create consequences such as reduced life span, increased output power loss and raising safety concerns during operation. Increased manpower requirement, larger time consumption, confinement to single fault prediction and high initial cost are certain drawbacks of conventional fault diagnosis techniques. Recent advancements in technology and the innovation of scientific techniques have urged the need for advanced fault diagnosis techniques that deliver instantaneous results. In the present study, unmanned aerial vehicles (UAVs) were employed to capture images of PVM with visual faults. The most common visual faults in photovoltaic modules (PVM) are delamination, burn marks, glass breakage, discoloration, and snail trails. Each fault condition contains a unique image pattern appearance attributed to the particular type of fault. Such patterns are extracted using convolutional neural networks and classified with the help of decision tree algorithms. First, the features are extracted from these aerial images by utilizing pre-trained AlexNet convolutional neural networks. Secondly, the J48 decision tree algorithm is utilized to select the most significant and valuable features from the extracted image features. Finally, the classification is carried out with several decision tree algorithms such as decision stump, hoeffiding tree, J48, linear model tree (LMT), random forest, random tree, representative (REP) tree, best first (BF) tree, extra tree, functional tree (FT), J48 consolidated, J48 graft, least absolute deviation (LAD) tree, naïve bayes (NB) tree and simple cart. The classification accuracies of the algorithms mentioned above are compared to suggest the best-in-class algorithm for real-time application. Among all the available tree-based algorithms, the random forest algorithm produced a maximum classification accuracy of 98.25% with a computational time of 0.89 seconds.

Reducing the Burn Marks on Injection-Molded Parts by External Gas-Assisted Injection Molding.

A burn mark is a sort of serious surface defect on injection-molded parts. In some cases, it can be difficult to reduce the burn marks by traditional methods. In this study, external gas-assisted injection molding (EGAIM) was introduced to reduce the burn marks, as EGAIM has been reported to reduce the holding pressure. The parts with different severities of burn marks were produced by EGAIM and conventional injection molding (CIM) with the same molding parameters but different gas parameters. The burn marks were quantified by an image processing method and the quantitative method was introduced to discuss the influence of the gas parameters on burn marks. The results show that the burn marks can be eliminated by EGAIM without changing the structure of the part or the mold, and the severity of the burn marks changed from 4.98% with CIM to 0% with EGAIM. Additionally, the gas delay time is the most important gas parameter affecting the burn marks.

The Influence of Heating Time on the Mechanical Properties of Laser Though-Transmission Welded Polyamide PPA40GF Joint

The goal of this work was to investigate effect of heating time during welding on mechanical properties of High Performance Polyamide thermoplastic composite material PPAGF40. Studies showed that welded joints with shorter heating time from 6.1 s to 6.7 s had a higher amount of pores and burn mark defects compared to the welded joints with longer heating time. Cross-section analysis of welded samples revealed tendency, that samples with higher heating time from 7.4 s to 11.5 s possessed more homogenous weld seam and higher resistance to burst pressure, respectively from 5.1 bar to 7.5 bar, while the resistance to pressure values of shortest heating time range, respectively are varying from 3.3 bar to 3.9 bar. The weld seam defects like pores and burn marks weakened the welded joint. 470 W of laser power energy input influenced fast melt-down of a cover and a housing, which caused material partial evaporation and later on formation of pores inside weld seam. By using lower 450 W of laser power and scanning speed of 1900 – 1700 mm/s, we can observe more homogenous weld seam. The cross-section of the samples were examined by using optical microscope. Mechanical resistance to pressure was performed in order to evaluate the performance of welded joints of different heating time of PPAGF40.

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration, glass breakage, and snail trails make detection of faults difficult under harsh environmental conditions. Various researchers have made several attempts to identify visual faults in a PVM. However, much of the previous studies were centered on the identification and analysis of limited number of faults. This article presents the use of a deep convolutional neural network (CNN) to extract image features and perform an effective classification of faults by machine learning (ML) algorithms. In contrast to the present-day work, five different fault conditions were considered in the study. The proposed solution consists of three phases, to effectively analyze various PVM defects. First, the module images are acquired using unmanned aerial vehicles (UAVs) and data augmentation is performed to generate a uniform dataset. Afterward, a pre-trained deep CNN is adopted for image feature extraction. Finally, the extracted image features are classified with the help of various ML classifiers. The final results show the effectiveness of pre-trained deep CNN and accurate performance of ML classifiers. The best-in-class ML classifier for multiple fault classification is suggested based on the performance comparison.