Observation on void formed in oxide scale of Fe-Cr-Ni alloy at 1073K in dry and humid environments

Void formation in oxide scale during high temperature oxidation is a common phenomenon. Over a long period of time voids will affect the mechanical property of scales by influencing the cracking and spalling. Voids formed in dry environment are different than that of formed in humid environment. With the presence of water vapor in humid environment the formation of void will increase, thus greater number of void compared to that in dry environment. Fe-Cr-Ni alloy samples were exposed isothermally at 1073 K in air (P_(O_2)= 0.21atm = 2.1 x? 10 5 Pa) and humid (air + steam) environments. XRD analysis done to all samples confirms that Fe2O3, Fe3O4, NiCr2O4, FeCr2O4, Cr2O3 and NiO phases exist in the scale. EDX analysis done shows varying compositions of Fe,Cr,Ni and O in outer and inner oxide scale, oxide scale/metal interface and metal. Field emission scanning electron microscope (FE-SEM) was used to investigate voids formed in the cross sections of the oxidized samples. Volume fraction of voids in the oxide scale was calculated in accordance to the cross sectional area fraction of voids in the scale. It shows that Fe-Cr-Ni alloy samples exposed in humid environment has as high as 71% more voids than that exposed in dry environment. It is concluded that the humid environment increased the number of void formed in the oxide scale, thus facilitates the exfoliation of protective scale during the high temperature oxidation

Observation On Void Formed In Oxide Scale Of Fe-Cr-Ni Alloy At 1073k In Dry And Humid Environments

Void formation in oxide scale during high temperature oxidation is a common phenomenon. Over a long period of time voids will affect the mechanical property of scales by influencing the cracking and spalling. Voids formed in dry environment are different than that of formed in humid environment. With the presence of water vapor in humid environment the formation of void will increase, thus greater number of void compared to that in dry environment. Fe-Cr-Ni alloy samples were exposed isothermally at 1073 K in air (P_(O_2 )= 0.21atm = 2.1×?10?^(5 )Pa) and  humid (air + steam) environments. XRD analysis done to all samples confirms that Fe2O3, Fe3O4, NiCr2O4, FeCr2O4, Cr2O3 and NiO phases exist in the scale. EDX analysis done shows varying compositions of Fe,Cr,Ni and O in outer and inner oxide scale, oxide scale/metal interface and metal. Field emission scanning electron microscope (FE-SEM) was used to investigate voids formed in the cross sections of the oxidized samples. Volume fraction of voids in the oxide scale was calculated in accordance to the cross sectional area fraction of voids in the scale. It shows that Fe-Cr-Ni alloy samples exposed in humid environment has as high as 71% more voids than that exposed in dry environment. It is concluded that the humid environment increased the number of void formed in the oxide scale, thus facilitates the exfoliation of protective scale during the high temperature oxidation. ABSTRAK: Pembentukan gelembung udara di dalam lapisan oksida ketika proses pengoksidaan di suhu tinggi merupakan satu fenomena biasa. Pada satu jangka masa yang panjang gelembung-gelembung ini akan memberi kesan kepada sifat mekanikal oksida dengan mempengaruhi pembentukan keretakan dan pengelupasan oksida. Gelembung udara yang terbentuk di dalam persekitaran kering berbeza daripada yang terbentuk di dalam persekitaran lembap. Dengan adanya wap air, pembentukan gelembung akan bertambah berbanding yang terbentuk di dalam persekitaran kering. Sampel aloi Fe-Cr-Ni telah dioksidakan secara isoterma pada suhu 1073 K di dalam udara (P_(O_2 )= 0.21atm = 2.1×?10?^(5 )Pa) dan lembap (udara + wap air). Analisis Pembelauan Sinar – X (XRD) kepada semua sampel menunjukkan oksida yang terbentuk ialah Fe2O3, Fe3O4, NiCr2O4, FeCr2O4, Cr2O3 dan NiO. Analisis Penyebaran Tenaga Sinar – X (EDX) menunjukkan komposisi Fe, Cr, Ni dan O yang berubah - ubah di lapisan oksida luar dan dalam, oksida/ antara muka logam dan logam. Mikroskop Imbasan Elektron-Pancaran Medan (FE-SEM) digunakan untuk meneliti gelembung di dalam oksida pada keratan rentas sampel. Pecahan isi padu gelembung yang terbentuk pada oksida dikira dengan merujuk kepada pecahan luas keratan rentas pada oksida tersebut. Sampel aloi Fe-Cr-Ni yang dioksidakan di dalam persekitaran lembap mempunyai kandungan gelembung udara 71% lebih banyak berbanding dengan yang dioksidakan di dalam persekitaran kering. Kesimpulannya persekitaran lembap meningkatkan bilangan gelembung yang terbentuk di dalam lapisan oksida, sekaligus memudahkan pengelupasan oksida semasa pengoksidaan suhu tinggi. KEYWORDS: high temperature oxidatio;, Fe-Cr-Ni alloy; void formation; quantitative analysis of voi;, dry environment; humid environmen

The effects of conventional and microwave heating techniques on extraction yield of orthosiphon stamineus leaves

The heating technique in a solid-liquid extraction system plays a significant role in the design and economic potential for the extraction of active components from herbs. This paper focused on the effects of extraction parameters such as ratio of sample to solvent, temperature and time of processing on the extraction yield of Orthosiphon stamineus leaves in conventional and microwave heating extraction techniques. The extracts were concentrated and dried using a rotary evaporator and freeze dryer in order to relate the yield to the processing parameters quantitatively in both heating techniques. The analysis results revealed that the processing parameters; ratio of sample to solvent, temperature and time of extraction had essential effects on the extraction yield of Orthosiphon stamineus leaves. Microwave heating extraction produced a comparable yield to conventional heating extraction with a relatively small deviation of approximately 2.8 % in average. Furthermore, microwave heating extraction reduced processing time, where this technique required about 25 % of the conventional heating time in heating up the extraction mixture to set-point temperature (60 ºC). This study concludes that microwave heating extraction, which is a green technology, has great potential in reducing the carbon foot print due to a shorter processing time and reduced energy consumption (~77 % less) compared to conventional heating extraction

Enhanced Community Detection Based On Cross Time For Higher Visibility In Supply Chain: A Six-Steps Model Framework

Increasing the visibility in supply chain network had decrease the risk in industries. However, the current Cross-Time approach for temporal community detection algorithm in the visibility has fix number of communities and lack of operation such as split or merge. Therefore, improving temporal community detection algorithm to represent the relationship in supply chain network for higher visibility is significant. This paper proposed six steps model framework that aim: (1) To construct the nodes and vertices for temporal graph representing the relationship in supply chain network; (2) To propose an enhanced temporal community detection algorithm in graph analytics based on Cross-time approach and (3) To evaluate the enhanced temporal community detection algorithm in graph analytics for representing relationship in supply chain network based on external and internal quality analysis. The proposed framework utilizes the Cross-Time approach for enhancing temporal community detection algorithm. The expected result shows that the Enhanced Temporal Community Detection Algorithm based on Cross Time approach for higher visibility in supply chain network is the major finding when implementing this proposed framework. The impact advances industrialization through efficient supply chain in industry leading to urbanization

Modeling physical interaction and understanding peer group learning dynamics: Graph analytics approach perspective

Physical interaction in peer learning has been proven to improve students’ learning processes, which is pertinent in facilitating a fulfilling learning experience in learning theory. However,observation and interviews are often used to investigate peer group learning dynamics from a qualitative perspective. Hence, more data-driven analysis needs to be performed to investigate the physicalinteraction in peer learning. This paper complements existing works by proposing a frameworkfor exploring students’ physical interaction in peer learning based on the graph analytics modeling approach focusing on both centrality and community detection, as well as visualization of the grap model for more than 50 students taking part in group discussions. The experiment was conducted during a mathematics tutorial class. The physical interactions among students were captured through an online Google form and represented in a graph model. Once the model and graph visualization were developed, findings from centrality analysis and community detection were conducted to identify peer leaders who can facilitate and teach their peers. Based on the results, it was found that five groups were formed during the physical interaction throughout the peer learning process, with at least one student showing the potential to become a peer leader in each group. This paper also highlights the potential of the graph analytics approach to explore peer learning group dynamics and interaction patterns among students to maximize their teaching and learning experience

Identifying influential nodes with centrality indices combinations using symbolic regressions

Numerous strategies for determining the most influential nodes in a connected network have been developed. The use of centrality indices in a network allows the identification of the most important nodes in the network. Specific indices, on the other hand, cannot search for a network's entire meaning because they are only interested in a single attribute. Researchers frequently overlook an index's characteristics in favour of focusing on its application. The purpose of this research is to integrate selected centrality indices classified by their various properties. A symbolic regression approach was used to find meaningful mathematical expressions for this combination of indices. When the efficacy of the combined indices is compared to other methods, the combined indices react similarly and outperform the previous method. Using this adaptive technique, network researchers can now identify the most influential network nodes

Software optimization of vision-based around view monitoring system on embedded platform

Image processing algorithm requires high computational power. Optimizing the algorithm to be run on an embedded platform is very critical as the platform provides limited computational resources. This research focused on optimizing and implementing a vision-based Around View Monitoring (AVM) system running on two embedded boards of Cortex-A7 quad and Cortex-A15 quad-core, and desktop platform of Intel i7 core. This paper presented a study on several techniques of software optimization that is removing code redundancy and multi-threading. The two methods improve the total processing time of the AVM system by 45% on ARM Cortex-A15 and 47% on ARM Cortex-A7

Observation on void formed in oxide scale of Fe-Cr-Ni alloy at 1073K in dry and humid environments

Void formation in oxide scale during high temperature oxidation is a common phenomenon.Over a long period of time voids will affect the mechanical property of scales by influencing the cracking and spalling. Voids formed in dry environment are different than that of formed in humid environment. With the presence of water vapor in humid environment the formation of void will increase, thus greater number of void compared to that in dry environment. Fe-Cr-Ni alloy samples were exposed isothermally at 1073 K in air ((POO2=0.21atm = 2.1 × 10 5 Pa) and humid (air + steam) environments. XRD analysis done to all samples confirms that Fe2O3, Fe3O4, NiCr2O4, FeCr2O4, Cr2O3 and NiO phases exist in the scale. EDX analysis done shows varying compositions of Fe,Cr,Ni and O in outer and inner oxide scale, oxide scale/metal interface and metal. Field emission scanning electron microscope (FE-SEM) was used to investigate voids formed in the cross sections of the oxidized samples. Volume fraction of voids in the oxide scale was calculated in accordance to the cross sectional area fraction of voids in the scale. It shows that Fe-Cr-Ni alloy samples exposed in humid environment has as high as 71% more voids than that exposed in dry environment. It is concluded that the humid environment increased the number of void formed in the oxide scale, thus facilitates the exfoliation of protective scale during the oxidation

Deep learning-based water segmentation for autonomous surface vessel

Visual-based obstacle detection from an autonomous surface vessel (ASV) is a complex task due to high variance of scene properties such as different illumination and presence of reflections. One approach in implementing the task is through extracting waterlines to enable inferring of vessel orientation and obstacles presence. Classical computer vision algorithms for detection holds limitation in robustness and scalability. With recent breakthroughs in deep neural network architectures, vision-based object detection is seen to obtain high performance. In this work, the deep learning models based on Convolutional Neural Network (CNN) to implement binary semantic segmentation is studied. This architecture identifies each pixel to water and non-water classes. In purpose of benchmarking models, Fully Convolutional Network (FCN), SegNet and U-Net are trained on a publicly available dataset, IntCatch Vision Data Set (ICVDS), to evaluate the performance. From the experiments carried out, quantitative results show effectiveness of the models with accuracy all above 95.55% and lowest average speed of 11 frames per second. To improve, pre-trained networks (VGG 16, Resnet-50 and MobileNet) are used as a backbone, obtaining an improved accuracy above 98.14% with lowest inferring speed of 10 frame per second. Using the developed ASV, new dataset of 143 images called Malaysia ASV Dataset (MASVD) is collected, labelled and made publicly available. The trained models are tested with the newly collected dataset obtaining accuracy of 75%. The high accuracy performance shows potential for the models to be employed for collision avoidance algorithm in ASV navigation

Comparison of Various Spectral Models for the Prediction of the 100-Year Design Wave Height

Offshore structures are exposed to random wave loading in the ocean environment, and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. In most cases, the dominant load on offshore structures is due to wind-generated random waves where the ocean surface elevation is defined using appropriate ocean wave energy spectra. Several spectral models have been proposed to describe a particular sea state that is used in the design of offshore structures. These models are derived from analysis of observed ocean waves and are thus empirical in nature. The spectral models popular in the offshore industry include Pierson-Moskowitz spectrum and JONSWAP spectrum. While the offshore industry recognizes that different methods of simulating ocean surface elevation lead to different estimation of design wave height, no systematic investigation has been conducted. Hence, the aim of this study is to investigate the effects of predicting the 100-year responses from various wave spectrum models. In this paper, the Monte Carlo time simulation (MCTS) procedure has been used to compare the magnitude of the 100-year extreme responses derived from different spectral models. Additionally, the linear random wave theory (LRWT) was implemented to simulate the offshore structural responses due to random wave loading. The models have been tested for three different environmental conditions represented by Hs = 15m, 10m and 5m respectively. The accuracy of the predictions of the 100-year responses from Pierson-Moskowitz and JONSWAP spectrums will then be investigated