Burn Marks Research Articles

Analisa Cacat Produk Dan Kerusakan Mold Pada Proses Injection Molding Dan Tindakan Perbaikan Di PT. Patco Elektronik Teknologi

This study examines product defects and mold damage in the injection molding process and corrective actions at PT. Patco Elektronik Teknologi. This type of research is field research with a qualitative approach. Researchers use primary and secondary data sources. Data collection techniques through observation, interviews, and documents related to product defects and mold damage in the injection molding process and corrective actions. The data were analyzed using data reduction, data presentation, and conclusion drawing methods. The results of the study show that injection molding has many advantages when compared to other plastic manufacturing methods, but in the process there are various defects in the plastic products produced. Based on the FMEA analysis, a number of product defects occur due to several factors, such as Bending, Gas Mark, Silver, Flash, Under Cut, Crack, Flow Mark, Scratches, Burn Mark, and Short Mold. Gas Mark has the highest risk priority number (RPN) value of 75. The problem of defects due to gas marks occurs in the housing panel components produced by PT. Patco Elektronik Teknologi. The company took corrective action NG Gas Mark on the housing panel product by means of mold maintenance and gas vent making by the mold shop for the cavity and ejector pin parts. After the gas vent making process was carried out, defects due to gas marks were greatly reduced in the housing panel products produced by PT. Patco.

Boiling of Coolant Near the Cutting Edge in High Speed Machining of Difficult-to-Cut Materials

This study investigates film boiling of coolant as a cooling inhibitor in a narrow wedge-shaped space between the tool flank face and the machined surface of a workpiece, observed during high-speed turning of stainless steel SUS304 and nickel-based superalloy Inconel 718. The boiling, likely triggered by high surface temperatures at both the face and surface close to the cutting edge, impedes coolant access to the tool tip area and efficient cooling. Therefore, the impact of coolant pressure on the boiling zone size was initially explored across pressures ranging from 0.1 to 20 MPa. A burn mark band due to coolant boiling, distinctly visible on the flank face of an insert with a yellow hard coating, expanded over cutting time. The film boiling area width, or the distance from the flank wear area to the band, decreased with increasing coolant pressure, reflecting the enhanced cooling ability and tool life with high-pressure coolant. Applying Boyle–Charles’ law to film boiling indicated that vapor pressure was directly related to coolant velocity rather than pressure. In contrast, the boiling area width increased with increasing cutting speed.

Cutout as augmentation in contrastive learning for detecting burn marks in plastic granules

Abstract. Plastic granules are a common delivery form for creating products in industries such as the plastic manufacturing, construction and automotive ones. In the corresponding sorting process of plastic granules, diverse defect types could appear. Burn marks, which potentially lead to weakened structural integrity of the plastic, are one of the most common types. Thus, plastic granules with burn marks should be filtered out during the sorting process. Artificial intelligence (AI)-based anomaly detection approaches are widely used in the field of visual-based sorting due to the higher accuracy and lower requirement of expert knowledge compared with classic rule-based algorithms (Chandola et al., 2009). In this contribution, a simple data augmentation strategy, cutout, is implemented as a way of simulating defects when combined with a contrastive learning-based methodology and is proven to improve the accuracy of the anomaly detection of burn marks. Different variants of cutout are also evaluated. Specifically, synthetic image data are used due to the lack of real data.

Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features

This study proposes a novel approach utilizing a voting-based ensemble technique to diagnose visible faults in photovoltaic (PV) modules from aerial images captured by unmanned aerial vehicles (UAVs), leveraging AlexNet features. The proposed method focuses on classifying commonly occurring visual faults such as glass breakage, snail trails, burn marks, delamination and discoloration. Two voting-based ensemble models, a two-class ensemble (combining support vector machines and k-nearest neighbor) and a three-class ensemble (integrating support vector machines, J48, and k-nearest neighbor) were developed and evaluated against individual machine learning classifiers. Results indicate that the two-class ensemble outperforms the three-class ensemble and other individual classifiers, achieving an accuracy of 98.30%. This approach not only enhances fault diagnosis accuracy but also reduces inspection costs and instrument monitoring efforts contributing to the sustainable and efficient operation of PV systems.

Investigation of the Combined Effects of Ultrasonic Vibration-Assisted Machining and Minimum Quantity Lubrication on Al7075-T6

The aluminum alloy Al7075-T6 finds extensive application in the aviation and automotive industries, where machining plays a pivotal role. Emerging techniques such as Ultrasonic Vibration-Assisted Machining (UVAM) and Minimum Quantity Lubrication (MQL) hold promise for enhancing machining efficiency. In this study, the combined use of UVAM and MQL for slot milling of Al7075-T6 was investigated. The results demonstrate that UVAM reduced cutting forces by an average of 10.87% in MQL and 8.31% in Conventional Cutting Fluid (CCF) conditions when compared to Conventional Machining (CM). In addition, UVAM yielded significantly improved surface finishes, characterized by an average reduction in surface roughness of 41.86% in MQL and 32.11% in CCF conditions relative to CM. Furthermore, surfaces subjected to UVAM exhibited fewer instances of burn marks and tool-induced markings, reduced chip splashing, and more uniform surface integrity compared to those manufactured with CM. Lastly, chips generated through UVAM exhibited distinct characteristics, notably shorter length, curvier shape, and a distinctive half-turn morphology when compared with the irregular chips produced through CM. In conclusion, our findings underscore the potential of UVAM in synergy with MQL to augment the machining of Al7075-T6 alloy, thereby yielding superior-quality machined components with enhanced operational efficiency.

Enhancing photovoltaic module fault diagnosis: Leveraging unmanned aerial vehicles and autoencoders in machine learning

Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future.

Long-term performance and degradation analysis of a 5 MW solar PV plant in the Andaman & Nicobar Islands

Renewable energy is being advocated as a new strategy in response to rising energy demand and global climate change. Solar photovoltaic technology is of great significance to climatic conditions due to the good solar intensity and availability to generate energy. In this paper, the 10-year operating performance of a 5 MW solar PV plant installed in tropical climate of the Andaman and Nicobar Islands was analysed. In 2015, the PV plant achieved its highest electricity production of 7216 MWh, while its lowest, 4757 MWh, was observed in 2022.The maximum performance ratio was recorded as 84.28 % in 2016 due to lower operating cell temperature whereas the maximum CUF as 16.17 % was recorded in 2015. The highest module, inverter and system efficiency for the ten year was observed 12.03 %, 97.64 % and 11.75 % respectively in year 2016. In 2014, the highest values for reference yield, array yield, and final yield were recorded as 4.92, 4.08, and 3.98 kWh/kW/day, respectively. Higher capture losses were observed in the plant over the year which varies upto 1.65 h/d. The plant exhibited a degradation rate of 1.99 % per annum, determined by employing an initial PR of 80.03 %, which declined to 64.11 % by 2022. Degradation analysis of the present system was also performed using visual inspection technique. Mostly found defects in plant was corrosion, delamination of encapsulant material, browning of cell material, burn mark on the back of module, milky patterns, cracks in top facing glass and solar cells. Since, the plant is located in the coastal area, it was seen that the plant is facing a severe issue of algae growth. Some PV modules were covered by more than 90 % algae which significantly contribute in capture losses.

Embedding a surface acoustic wave sensor and venting into a metal additively manufactured injection mould tool for targeted temperature monitoring

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) “stop-and-go” approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

Burn Marks on a Roman Timber-Framed Building

Several burn marks on timbers from a Roman building in London were recorded after excavation in the 1980s. Detailed investigation and experimental archaeology lead to the conclusion that the burn marks were made accidentally with an oil lamp, not deliberately. Such accidents would, however, be rare, and the burn marks found widely in post-medieval and medieval buildings of southern Europe must be the result of deliberate ritual activity.

Influence of JFET Width on Short-Circuit Robustness of 1200 V SiC Power MOSFETs

This paper investigates and compares the static performance and short-circuit (SC) robustness of 1200 V SiC MOSFETs with varying JFET widths (WJFET = 2.0–5.0 μm). Short-circuit measurements as well as electrical-thermal simulations are used to identify thermal distribution and maximum electrical field, providing valuable insights into the design limits. The devices under test (DUTs) with narrow and wide WJFET exhibit different failure mechanisms under SC stress. After the short-circuit failure, interlayer dielectric (ILD) cracks are observed in DUTs with narrow JFET width (WJFET < 3 μm). In contrast, it is discovered that the burn mark is located in the channel region of the device with a wide JFET width. Moreover, the short-circuit withstand time (SCWT) of DUTs with narrow and wide WJFET exhibits varying trends under high temperature conditions (100 °C). These results can help verify the different failure mechanisms and determine an optimal JFET design to improve the trade-off between the static performance and SC ruggedness of the SiC MOSFETs.

An Exploration of the Effects of Gene-Editing Technology on Human Identity

An Exploration of the Effects of Gene-Editing Technology on Human Identity

Enhancement of magnetization roasting of hematite ore by high voltage pulse discharge

High voltage pulse discharge (HVPD) is a new type of crushing technology with good application. Existing studies have focused on the crushing characteristics, with less reports on follow-up work. This study magnetization roasts hematite ore after mechanical crushing and HVPD, analyzing the difference in the roasting effect between the two crushing methods by establishing a new system of HVPD-magnetization roasting. For a roasting temperature of 500 °C, roasting time of 30 min and number of pulses of 100 times, the grade and recovery of magnetic separation concentrate obtained from HVPD products after secondary separation were 64.69% and 82.05% respectively, which were 0.28 and 1.96 percentage higher than those of mechanical crushing. X-ray diffraction (XRD), vibrating sample magnetometry (VSM), Brunauer-Emmett-Teller (BET), scanning electron microscopy and energy dispersive spectroscopy (SEM-EDS) were used to analyze the process and showed that the specific surface area, pore volume and average pore size of the HVPD products were improved. Grain boundary fracture and burn marks were present in the HVPD products.

Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning-Based Image Analysis

Artificial intelligence (AI) has evolved into a powerful tool that has wide-spread application in computer vision such as computer-aided inspection, industrial control systems, and navigation of robots. Monitoring the condition of machineries and mechanical components for the presence of faults with the aid of image-based automated analysis is one major application of computer vision. Diagnosing machinery faults from images can be made feasible with the adoption of deep learning and machine learning techniques. The primary objective of this study is to detect malfunctions in photovoltaic (PV) modules by utilizing a combination of deep learning and machine learning methodologies, with the assistance of RGB images captured via unmanned aerial vehicles. Six test conditions of PV modules such as good panel, snail trail, delamination, glass breakage, discoloration, and burn marks were considered in the study. The overall experimentation was carried out in two phases: (i) deep learning phase and (ii) machine learning phase. In the initial deep learning phase, the final fully connected layer of six pretrained networks, namely, DenseNet-201, VGG19, ResNet-50, GoogLeNet, VGG16, and AlexNet, was utilized to extract PVM image features. During the machine learning phase, feature selection from the extracted features was carried out using the J48 decision tree algorithm. Post selection of features, three families of classifiers such as tree, Bayes, and lazy were applied to determine the best feature extractor-classifier pair. The combination of DenseNet-201 features with k-nearest neighbour (IBK) classifier produced the overall classification accuracy of 100.00% among all other pretrained network features and classifiers considered.

Flammability of Ocular Surface Lubricants.

Surgical fires pose a substantial risk to patients and can cause significant injury, especially in oculofacial surgery. Ocular surface lubricants can potentially act as fuel for an operating room fire. We present an experimental analysis of the flammability of 9 commonly used ophthalmic lubricants under 4 ignition sources used in oculofacial surgery with and without supplemental oxygen. The flammability of 9 ophthalmic lubricants were tested under various operating room conditions. Each lubricant was exposed to 4 different ignition sources: an open flame lighter, monopolar cautery, bipolar cautery, and hand-held high temperature cautery, and the response of the lubricant was recorded. The testing was conducted both in room air and with 6 L/minute of 100% oxygen directed at the lubricant through a nasal cannula. Any reaction in which there was ignition, sparking, smoking, or a transient or permanent change in appearance of the lubricant was deemed notable. Of the 9 lubricants tested, 4 displayed a reaction to the ignition source. Without supplemental oxygen, 100% petrolatum and neomycin-polysporin-bacitracin-hydrocortisone ointment produced some smoke when applied with the high temperature cautery. Notably, under both the conditions of no supplemental oxygen and with the addition of 6 L/minute of 100% oxygen, the carboxymethylcellulose drops and lidocaine jelly both conducted and sparked with the monopolar cautery leaving visible burn marks on the paper. The overall fire hazard posed by ocular surface lubricants is low. Some topical lubricants can conduct electricity from monopolar cautery, which could increase the risk of inadvertent electrical burns. Certain lubricants could potentially become a fuel source when used in combination with hand-held high temperature battery cautery. Bipolar cautery was not associated with either increased conductivity or flammability with any of the lubricants tested.

New evidence on prehistoric settlement in Song Toyapakeh, an underwater cave in Nusa Penida, Bali

Song Toyapakeh is a submerged cave located on Nusa Penida Island, Bali, at a depth of 16m below sea level. The cave is believed to have been inhabited by human during the late Pleistocene period, as evidenced by the large numbers of bones from Cervidae and Elephantoid families, as well as turtles, that are scattered throughout the cave. The presence of these bones raises questions about the past use of Song Toyapakeh by humans and the transformation of the island's landscape into a submerged cave. The objective of this study is to address the function of Song Toyapakeh during the late Pleistocene period, based on the animal bones found within. To accomplish this, data were collected using scuba method to record the morphology of the cave and to take samples of the animal bones that were likely used by human in the past. Macroscopic and microscopic observations were then conducted on the cut marks and burn marks present on the bones to identify evidence of human use. In addition, paleogeographical and paleoclimatological data were examined to understand the landscape transformation of the cave. The analysis of the bones revealed traces of trimming and burn marks, indicating that the bones were used as tools and as food source by humans. Changes in the landscape caused by fluctuations in the sea level due to climate change during the Pleistocene period are believed to be responsible for the transformation of the cave from an area located above sea level to one that is now submerged. The evidence of human activity on the faunal remains found in Song Toyapakeh provides strong evidence of the use of the cave as human occupation during the late Pleistocene period.

Destructive breakdown studies of irradiated LGADs at beam tests for the ATLAS HGTD

In the past years, it has been observed at several beam test campaigns thatirradiated LGAD sensors break with a typical star shaped burn mark whenoperated at voltages much lower than those at which they were safely operatedduring laboratory tests. The study presented in this paper was designed todetermine the safe operating voltage that these sensors can withstand. Manyirradiated sensors from various producers were tested in two test beamfacilities, DESY (Hamburg) and CERN-SPS (Geneva), as part of ATLAS HighGranularity Timing Detector (HGTD) beam tests. The samples were placed in thebeam and kept under bias over a long period of time in order to reach a highnumber of particles crossing each sensor. Both beam tests lead to a similarconclusion, that these destructive events begin to occur when the averageelectric field in the sensor becomes larger than12 V/μm.

Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing

The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. This paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained.

Multi‐pronged degradation analysis of a photovoltaic power plant after 9.5 years of operation under hot desert climatic conditions

AbstractLong‐term reliability assessment of photovoltaic (PV) modules is key to ensuring the economic viability of PV systems. This paper presents a multi‐pronged performance degradation analysis of a 62.1 kWp solar PV power plant after 9.5 years of operation. For this purpose, various diagnosis techniques, namely, visual inspection, infrared thermography (IR), ultraviolet fluorescence (UVFL) and current‐voltage (I‐V) characterization, have been performed to evaluate the performance and degradation of the solar PV power plant. The visual characterization results show that the PV strings are affected by different mechanisms with different degrees of occurrences. The degradation mechanisms observed and the level of occurrence among the modules were found to be, encapsulant discolouration (100%), degraded frame adhesive (57%), degraded junction box adhesive (39%), snail trails (30%), burn marks (3%), cell cracks (2%) and delamination (0.4%). Although discolouration of the encapsulant was the most common possible degradation mechanism observed, the main causes of the power loss were snail trails and cracks in the PV cells. Furthermore, the IR thermography and UVFL analysis provided better understanding on snail trails phenomenon and crack mechanism on the affected PV modules. Besides imaging techniques, to assess the electrical performance of the system, we performed current‐voltage (I‐V) and power‐voltage (P‐V) characterization of the entire PV plant from which the degradation rates of the electrical parameters were determined. Our results show that the degradation rates for the maximum power and the fill factor are 0.84%/year and 0.66%/year, respectively, over the operating period of the installation. The estimated maximum power degradation rate for this installation could be considered as a realistic degradation rate for PV modules installed in harsh and hot desert climatic conditions. Additionally, our work has raised doubts about the validity of the warranties proposed by the manufacturers.

A Letter for Thomas Hoccleve?

When Henry V’s war machine trundled through France it was accompanied by a highly sophisticated mobile bureaucracy. Whereas earlier wars came with their own clerks, the extent and organizational depth of Henry’s overseas administrative structure was unprecedented. At its zenith the English government’s continental arm included a chancery branch in Rouen that supported a travelling royal council. The government branches most active in France were the Signet and the Privy Seal: in the years prior to Henry’s death in 1422, both writing offices had half of their staff in France, supporting the king and his lieutenants. There also exist numerous private letters sent to England by these clerks. One such letter was sent by John Offord on 6 June 1420 from the siege of Sens to an anonymous recipient in England. The original no longer survives; it was once probably contained in British Library, MS Cotton Caligula D. v. The manuscript was among those badly damaged by the fire of 1731 in Ashburnham House, where the Cotton Library was then located. Many folios in MS Cotton Caligula D. v. have survived, but all of them display burn marks around all four edges. However, Thomas Rymer transcribed and printed the letter in his Foedera.1 Occasionally, this letter is referenced in treatments of Henry’s marriage, Anglo-French relations in Paris, and the minutiae of the ensuing military campaign against the Dauphinist faction.2 The letter has been printed a number of times before, although it has never been edited.3

Burning Still

Using an eighth-century copy of the 60-fascicle Flower Ornament Sutra as a case study, this article examines the transformation of a Buddhist scripture into an aesthetic object in early modern Japan. On the 14th day of the second month, 1667, a fire decimated Nigatsudo at Todaiji (Nara prefecture) along with most of the sacred objects within. Clerics salvaged partially burnt scrolls of an eighth-century Flower Ornament Sutra done in silver ink on indigo-dyed paper. The scrolls were restored ten years later, but by the first half of the eighteenth century, part of the set left the temple and began circulating in the art market as collectable calligraphy fragments, later known as the “Nigatsudo burnt sutra” (Nigatsudo yakegyo). This study traces the curious shift in the attribution of Nigatsudo yakegyo’s calligrapher that took place in the eighteenth century to consider how the burn marks impacted the transformation of this scripture from a devotional text into a collectable artefact. I argue that the curious switch in attribution occurred adhering to the popular imagination of the calligraphers, as well as the distinct role certain fragments played within the early-modern calligraphy collecting culture.