Search results for: precision machinery industry

Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

Procedia PDF Downloads 280

Authors: Arsalan Salahi

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Today, the number of changes in the construction industry and urban mass house building is increasing, which makes you need to pay more attention to targeted planning for human resource management and training. The human resources working in the construction industry have various problems and deficiencies, and in order to solve these problems, there is a need for basic management and training of these people in order to lower the construction costs and increase the quality of the projects, especially in mass house building projects. The success of any project in reaching short and long-term professional goals depends on the efficient combination of work tools, financial resources, raw materials, and most importantly, human resources. Today, due to the complexity and diversity of each project, specialized management fields have emerged to maximize the potential benefits of each component of that project. Human power is known as the most important resource in construction projects for its successful implementation, but unfortunately, due to the low cost of human power compared to other resources, such as materials and machinery, little attention is paid to it. With the correct management and training of human resources, which depends on its correct planning and development, it is possible to improve the performance of construction projects. In this article, the training and motivation of construction industry workers and their effects on the effectiveness of projects in this industry have been researched. In this regard, some barriers to the training and motivation of construction workers and personnel have been identified and solutions have been provided for construction companies. Also, the impact of workers and unskilled people on the efficiency of construction projects is investigated. The results of the above research show that by increasing the use of correct and basic training for human resources, we will see positive results and effects on the performance of construction projects.

Keywords: human resources, construction industry, principled training, skilled and unskilled workers

Procedia PDF Downloads 55

Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

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Authors: Chandra Gupt Porwal, Preeti C. Porwal

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Transient data reveals much about the machine's condition that steady-state data cannot. New technologies make this information much more available for evaluating the mechanical integrity of a machine train. Recent surveys at various stations indicate that simplicity is preferred over completeness in machine audits throughout the power generation industry. This is most clearly shown by the number of rotating machinery predictive maintenance programs in which only steady-state vibration amplitude is trended while important transient vibration data is not even acquired. Efforts have been made to explain what transient data is, its importance, the types of plots used for its display, and its effective utilization for analysis. In order to demonstrate the value of measuring transient data and its practical application in rotating machinery for resolving complex and persistent issues with turbine generators, the author presents a few case studies that highlight the presence of rotor instabilities due to the shaft moving towards the bearing centre in a 100 MM LMZ unit located in the Northern Capital Region (NCR), heavy misalignment noticed—especially after 2993 rpm—caused by loose coupling bolts, which prevented the machine from being synchronized for more than four months in a 250 MW KWU unit in the Western Region (WR), and heavy preload noticed at Intermediate pressure turbine (IPT) bearing near HP- IP coupling, caused by high points on coupling faces at a 500 MW KWU unit in the Northern region (NR), experienced at Indian power plants.

Keywords: transient data, steady-state-data, intermediate -pressure-turbine, high-points

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Zichao Zhang, Yifan Zhao, Samuel Court

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The carbon emission problem of the traditional construction industry has long been a pressing issue. With the growing emphasis on environmental protection and advancement of science and technology, the organic integration of digital technology and emission reduction has gradually become a mainstream solution. Among various sophisticated digital technologies, digital twins, which involve creating virtual replicas of physical systems or objects, have gained enormous attention in recent years as tools to improve productivity, optimize management and reduce carbon emissions. However, the relatively high implementation costs including finances, time, and manpower associated with digital twins have limited their widespread adoption. As a result, most of the current applications are primarily concentrated within a few industries. In addition, the creation of digital twins relies on a large amount of data and requires designers to possess exceptional skills in information collection, organization, and analysis. Unfortunately, these capabilities are often lacking in the traditional construction industry. Furthermore, as a relatively new concept, digital twins have different expressions and usage methods across different industries. This lack of standardized practices poses a challenge in creating a high-quality digital twin framework for construction. This paper firstly reviews the current academic studies and industrial practices focused on reducing greenhouse gas emissions in the construction industry using digital twins. Additionally, it identifies the challenges that may be encountered during the design and implementation of a digital twin framework specific to this industry and proposes potential directions for future research. This study shows that digital twins possess substantial potential and significance in enhancing the working environment within the traditional construction industry, particularly in their ability to support decision-making processes. It proves that digital twins can improve the work efficiency and energy utilization of related machinery while helping this industry save energy and reduce emissions. This work will help scholars in this field to better understand the relationship between digital twins and energy conservation and emission reduction, and it also serves as a conceptual reference for practitioners to implement related technologies.

Keywords: digital twins, emission reduction, construction industry, energy saving, life cycle, sustainability

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Authors: Fumihiko Isada, Yuriko Isada

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The aim of this research is to clarify the difference by industry segment or product characteristics as regards organisation management for an open innovation to raise R&D performance. In particular, the trait of the pharmaceutical industry is defined in comparison with IT component industry segment. In considering open innovation, both inter-organisational relation and the management in an organisation are important issues. As methodology, a questionnaire was conducted. In conclusion, suitable organisation management according to the difference in industry segment or product characteristics became clear.

Keywords: empirical study, industry segment, open innovation, product-development organisation pattern

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Authors: Mehrdad Nouri Khajavi, Gollamhassan Payganeh, Mohsen Fallah Tafti

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Fault diagnosis is an important aspect of maintaining rotating machinery health and increasing productivity. Many researches has been done in this regards. Many faults such as unbalance, misalignment, looseness, bearing faults, etc. have been considered and diagnosed with different techniques. Most of the researches in fault diagnosis of rotating machinery deal with single fault. Where as in reality faults usually occur simultaneously and it is, therefore, necessary to recognize them at the same time. In this research, two of the most common faults namely unbalance and misalignment have been considered simultaneously with different intensity and then identified and classified with the use of Multi-Layer Perception Neural Network (MLPNN). Processed Vibration signals are used as the input to the MLPNN, and the class of mixed unbalancy, and misalignment is the output of the NN.

Keywords: unbalance, parallel misalignment, combined faults, vibration signals

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Authors: Eureeka Haishang Wu

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Creative Industry has been recognized as top priority in many nations for decades, as through globalization processes, culture can be economized by creative industry to develop economies. From non-economic perspectives; creative industry supports nation-identity, enhances global exposure, and improve international relation. In order to enable the globalization processes of creative industry, a three-step approach was proposed to align education, policies, and technologies into a transformation platform, and eventually to achieve a common model of global collaboration.

Keywords: creative industry, education, policies, technologies, collaboration, globalization

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Authors: Faisal AlShammari, Abdulmajid Addali, Mosab Alrashed

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It is widely acknowledged that bearing failures are the primary reason for breakdowns in rotating machinery. These failures are extremely costly, particularly in terms of lost production. Roller bearings are widely used in industrial machinery and need to be maintained in good condition to ensure the continuing efficiency, effectiveness, and profitability of the production process. The research presented here is an investigation of the use of acoustic emission (AE) to monitor bearing conditions at low speeds. Many machines, particularly large, expensive machines operate at speeds below 100 rpm, and such machines are important to the industry. However, the overwhelming proportion of studies have investigated the use of AE techniques for condition monitoring of higher-speed machines (typically several hundred rpm, or even higher). Few researchers have investigated the application of these techniques to low-speed machines ( < 100 rpm). This paper addressed this omission and has established which, of the available, AE techniques are suitable for the detection of incipient faults and measurement of fault growth in low-speed bearings. The first objective of this paper program was to assess the applicability of AE techniques to monitor low-speed bearings. It was found that the measured statistical parameters successfully monitored bearing conditions at low speeds (10-100 rpm). The second objective was to identify which commonly used statistical parameters derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify the onset of a fault in the out race. It was found that these parameters effectually identify the presence of a small fault seeded into the outer races. Also, it is concluded that rotational speed has a strong influence on the measured AE parameters but that they are entirely independent of the load under such load and speed conditions.

Keywords: acoustic emission, condition monitoring, NDT, statistical analysis

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Authors: Muhammad Saad Aslam

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In 2021, our organization has launched our proprietary mobile App i.e. Farm Intelligence platform, an industrial-first precision agriculture solution, to Pakistan. It was piloted at 47 locations (spanning around 1,200 hectares of land), addressing growers’ pain points by bringing the benefits of precision agriculture to their doorsteps. This year, we have extended its reach by more than 10 times (nearly 130,000 hectares of land) in almost 600 locations across the country. The project team selected highly infested areas to set up traps, which then enabled the sales team to initiate evidence-based conversations with the grower community about preventive crop protection products that includes pesticides and insecticides. Mega farmer meeting field visits and demonstrations plots coupled with extensive marketing activities, were setup to include farmer community. With the help of App real-time pest monitoring (using heat maps and infestation prediction through predictive analytics) we have equipped our growers with on spot insights that will help them optimize pesticide applications. Heat maps allow growers to identify infestation hot spots to fine-tune pesticide delivery, while predictive analytics enable preventive application of pesticides before the situation escalates. Ultimately, they empower growers to keep their crops safe for a healthy harvest.

Keywords: precision pest management, precision agriculture, real time pest tracking, pest forecasting

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Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: autonomous vehicles, deformable part model, dpm, pedestrian detection, real time

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Authors: Hoon Yi, Minho Sung, Hangil Ko, Moon Kyu Kwak, Hoon Eui Jeong

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Inspired by the remarkable adhesion properties of gecko lizards, bio-inspired dry adhesives with smart adhesion properties have been developed in the last decade. Compared to earlier dry adhesives, the recently developed ones exhibit excellent adhesion strength, smart directional adhesion, and structural robustness. With these unique adhesion properties, bio-inspired dry adhesive pads have strong potential for use in precision industries such as semiconductor or display manufacturing. In this communication, we present a new manufacturing technology based on advanced dry adhesive systems that enable precise manipulation of large-area substrates over repeating cycles without any requirement for external force application. This new manufacturing technique is also highly accurate and environment-friendly, and thus has strong potential as a next-generation clean manufacturing technology.

Keywords: gecko, manufacturing robot, precision manufacturing

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Authors: Morteza Raei Dehaghi, Mojtaba Molaahmadi, Seyed Mohammad Mirhashemi

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This study aimed to explore the relationship between energy consumption and value-added in Iran’s industry sector during the time period 1973-2011. Annual data related to energy consumption and value added in the industry sector were used. The results of the study revealed a positive relationship between energy consumption and value-added of the industry sector. Similarly, the results showed that there is one-way causality between energy consumption and value-added in the industry sector.

Keywords: economic growth, energy consumption, granger causality test, industry sector

Procedia PDF Downloads 447

Authors: H. Zou, B. Wang

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Thermal distortion of the machine tool plays a critical role in its machining accuracy. This study investigates the thermal performance of a high-precision machine frame with future-oriented mineral casting components. A thermo-mechanical finite element model (FEM) was established to evaluate the thermal behavior of the frame under environmental thermal fluctuations. The validity of the presented FEM model was confirmed experimentally by a series of laser interferometer tests. Good agreement between numerical and experimental results demonstrates that the proposed model can accurately predict the thermal deformation of the frame with thermo-mechanical coupling effect. The results also show that keeping the workshop in thermally stable conditions is crucial for improving the machine accuracy of the system with large scale components. The goal of this paper is to investigate the feasibility of innovative mineral casting material applied in high-precision drilling machine and to provide a strategy for machine tool industry seeking a perfect substitute for classic frame materials such as cast iron and granite.

Keywords: thermo-mechanical model, finite element method, laser interferometer, mineral casting frame

Procedia PDF Downloads 273

Authors: Jana Petrů, Tomáš Zlámal, Robert Čep, Lenka Čepová

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This article is focused on the thermal spray coating machining issue. Those are irreplaceable in many areas of nowadays industrial branches such as aerospace industry, mostly thanks to their excellent qualities in production and also in renovation of machinery parts. The principals of thermal spraying and elementary diversification are described in introduction. Plasma coating method of composite materials -cermets- is described more thoroughly. The second part describes thermal spray coating machining and grinding in detail. This part contains suggestion of appropriate grinding tool and assessment of cutting conditions used for grinding a given part. Conclusion describes a problem which occurred while grinding a cermet thermal spray coating with a specially designed grindstone and a way to solve this problem.

Keywords: coating, aerospace, plasma, grinding

Procedia PDF Downloads 519

Authors: Chatrabhuti Pipop, Visessri Supattra, Charinpanitkul Tawatchai

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Durian is one of the top agricultural products exported by Thailand. There is the massive market potential for the durian industry. While the global demand for Thai durians, especially the demand from China, is very high, Thailand's durian supply is far from satisfying strong demand. Poor agricultural practices result in low yields and poor quality of fruit. Most irrigation systems currently used by the farmers are fixed schedule or fixed rates that ignore actual weather conditions and crop water requirements. In addition, the technologies emerging are too difficult and complex and prices are too high for the farmers to adopt and afford. Many farmers leave the durian trees to grow naturally. With improper irrigation and nutrient management system, durians are vulnerable to a variety of issues, including stunted growth, not flowering, diseases, and death. Technical development or research for durian is much needed to support the wellbeing of the farmers and the economic development of the country. However, there are a limited number of studies or development projects for durian because durian is a perennial crop requiring a long time to obtain the results to report. This study, therefore, aims to address the problem of durian production by developing an autonomous and precision irrigation system. The system is designed and equipped with an industrial programmable controller, a weather station, and a digital flow meter. Daily water requirements are computed based on weather data such as rainfall and evapotranspiration for daily irrigation with variable flow rates. A prediction model is also designed as a part of the system to enhance the irrigation schedule. Before the system was installed in the field, a simulation model was built and tested in a laboratory setting to ensure its accuracy. Water consumption was measured daily before and after the experiment for further analysis. With this system, the crop water requirement is precisely estimated and optimized based on the data from the weather station. Durian will be irrigated at the right amount and at the right time, offering the opportunity for higher yield and higher income to the farmers.

Keywords: Durian, precision irrigation, precision agriculture, smart farm

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Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos

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As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.

Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming

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Authors: S. Homrossukon, S. Sansureerungsigun

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This work aims to introduce an efficient and to standardize the measuring system analyses for automotive industrial. The study started by literature reviewing about the management and analyses measurement system. The approach of measuring system management, then, was constructed. Such approach was validated by collecting the current measuring system data using the equipments of interest including vernier caliper and micrometer. Their accuracy and precision of measurements were analyzed. Finally, the measuring system was improved and evaluated. The study showed that vernier did not meet its measuring characteristics based on the linearity whereas all equipment were lacking of the measuring precision characteristics. Consequently, the causes of measuring variation via the equipment of interest were declared. After the improvement, it was found that their measuring performance could be accepted as the standard required. Finally, the standardized approach for analyzing the measuring system of automotive was concluded.

Keywords: automotive part manufacturing measurement, measuring accuracy, measuring precision, measurement system analyses

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, S. Harapanahalli, J. Walsh

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The objective of this study was to determine the effects of environmental stressors on the performance of lactating dairy cows and discuss some future trends. There exists a relationship between the meteorological data and milk yield prediction accuracy in pasture-based dairy systems. New precision technologies are available and are being developed to improve the sustainability of the dairy industry. Some of these technologies focus on welfare of individual animals on dairy farms. These technologies allow the automatic identification of animal behaviour and health events, greatly increasing overall herd health and yield while reducing animal health inspection demands and long-term animal healthcare costs. The data set consisted of records from 489 dairy cows at two dairy farms and temperature measured from the nearest meteorological weather station in 2018. The effects of temperature on milk production and behaviour of animals were analyzed. The statistical results indicate different effects of temperature on milk yield and behaviour. The “comfort zone” for animals is in the range 10 °C to 20 °C. Dairy cows out of this zone had to decrease or increase their metabolic heat production, and it affected their milk production and behaviour.

Keywords: behavior, milk yield, temperature, precision technologies

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Authors: Ying Bo Xie, Hisa Yuki Kurokawa

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Due to the fast development, China's express delivery industry has involved in a dilemma that the service quality are keeping decreasing while the construction rate of delivery network cannot meet the customers’ demand. In order to get out of this dilemma and enjoy a succession development rate, it is necessary to understand the current situation of China's express delivery industry. Firstly, the evolution of China's express delivery industry was systematical presented. Secondly, according to the number of companies and the amount of parcels they has dealt each year, the merits and faults of tow kind of operating pattern was analyzed. Finally, based on the characteristics of these express companies, the problems of China's express delivery industry was divided into several types and the countermeasures were given out respectively.

Keywords: China, express delivery industry, status, problem

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Authors: K. Suheiti, P. Soni, Yardha

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Challenges ahead of Indonesian agricultural development include increasing rural welfare, food needs, and the provision of employment through resource optimization that are laid out in agribusiness system. The agricultural system also responsive to the changing strategic environment. However, mounting pressure of population increase and changes in land-uses, require intensive use of agricultural land with modern agricultural machinery. Similarly, environmentally friendly technologies should continue to be developed in an effort to build and develop a good farming practice model. This paper explains the development of agricultural mechanization in Indonesia, particularly on rice production. The method of the research was analyze secondary data, tabulation and interpretation. The result showed, there was a variety of tools and agricultural machinery that have been produced and used by farmers to support national food security. The role of mechanization was needed to support national rice production and food security achievement.

Keywords: farming, Indonesia, mechanization, rice

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Authors: Lin Dong-Yih, Yang S. M., Huang B. W., Lian J. A.

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Super duplex stainless steel has excellent mechanical properties and corrosion resistance. It becomes important structural material as its application has been extended to the fields such as renewable energy and the chemical industry because of its excellent properties. As examples are offshore wind power, solar cell machinery, and pipes in the chemical industry. The mechanical properties and corrosion resistance of super duplex stainless steel can be eliminated by welding due to the precipitation of the hard and brittle σ phase, which is rich of chromium, and molybdenum elements. This paper studies the influence of carbon element on the phase transformation of -ferrite and σ phase in 2507 super duplex stainless steel. The 2507 will be under argon gas protection welded with 316 and 316L extra low carbon stainless steel separately. The microstructural phases of stainless steels before and after welding, in fusion, heat affected zones, and base material will be studied via X-ray, OM, SEM, EPMA i.e. their quantity, size, distribution, and morphology. The influences of diffusion by carbon element will be compared according to the microstructures, hardness, and corrosion tests.

Keywords: complex stainless steel, welding, phase formation, carbon element, sigma phase, delta ferrite

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Authors: M. Tawfik, X. Tonnellier, C. Sansom

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The Lens-Lens Beam Generator (LLBG) is a Fresnel-based optical concentrating technique which provides flexibility in selecting the solar receiver location compared to conventional techniques through generating a powerful concentrated collimated solar beam. In order to achieve that, two successive lenses are used and followed by a flat mirror. Hence the generated beam emerging from the LLBG has a high power flux which impinges on the target receiver, it is important to determine the precision of the system output. In this present work, mathematical investigation of different parameters affecting the precision of the output beam is carried out. These parameters include: Deflection in sun-facing lens and its holding arm, delay in updating the solar tracking system, and the flat mirror surface flatness. Moreover, relationships that describe the power lost due to the effect of each parameter are derived in this study.

Keywords: Fresnel lens, LLBG, solar concentrator, solar tracking

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Authors: Emily Fortune Super, Ameerah Khadaroo, Aurore Bardey

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Fashion models are often assumed to have a glamorous job with limited consideration for their well-being. This study aims to assess the well-being of models through semi-structured interviews with six professional fashion models and six industry professionals. Thematic analysis revealed that although models experienced improved self-confidence, they also reported heightened anxiety levels, body image issues, and the negative influence of modelling on their self-esteem. By contrast, industry professionals reported no or minimum concerns about anxious behaviours or the general well-being of fashion models. Being resilient as a model was perceived as an essential attribute to have by both models and industry professionals as they face recurrent rejection in this industry. These results demonstrate a significant gap in the current understanding of the well-being of fashion models between industry professionals and the models themselves. Findings imply that there is an inherent need for change in the modelling industry to promote and enhance their well-being.

Keywords: body image, fashion industry, modelling, well-being

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Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

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Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

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Authors: Athirat Intajorn

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According to the ASEAN free trade areas (AFTA), Thailand has applied the AFTA agreement for reducing tariffs and reflecting changes in business processes. The reflection of changes in agribusiness processes, in particular, has accumulated as production costs for producers. Palm Oil industry has become an important industry to Thailand economic. Thailand currently ranks the 3rd in the world for Crude Palm Oil CPO. Therefore, the scope of this paper presents a research framework to investigate the supply chain management practices in Thailand palm oil industry. This research is limit to literature review. And the proposed framework identifies the criteria of supply chain management for Thailand palm oil industry in order for linkage among entities within logistics management involving plantation, mill, collection port, refinery and cookie from the data utilization. The Supply Chain Management Practices in Thailand Palm Oil Industry framework has a somewhat different view due to the high complexity of agribusiness logistics management.

Keywords: supply chain management, practice, palm oil industry, Thailand palm oil industry

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Authors: Saleem Z. Ramadan

Abstract:

This paper discusses the effects of using progressive Type-I right censoring on the design of the Simple Step Accelerated Life testing using Bayesian approach for Weibull life products under the assumption of cumulative exposure model. The optimization criterion used in this paper is to minimize the expected pre-posterior variance of the PTH percentile time of failures. The model variables are the stress changing time and the stress value for the first step. A comparison between the conventional and the progressive Type-I right censoring is provided. The results have shown that the progressive Type-I right censoring reduces the cost of testing on the expense of the test precision when the sample size is small. Moreover, the results have shown that using strong priors or large sample size reduces the sensitivity of the test precision to the censoring proportion. Hence, the progressive Type-I right censoring is recommended in these cases as progressive Type-I right censoring reduces the cost of the test and doesn't affect the precision of the test a lot. Moreover, the results have shown that using direct or indirect priors affects the precision of the test.

Keywords: reliability, accelerated life testing, cumulative exposure model, Bayesian estimation, progressive type-I censoring, Weibull distribution

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Authors: T. H. Bitebe, T. M. Lima, F. Charrua-Santos, C. J. Matias Oliveira

Abstract:

This study aims to present a proposal for an economic integration model for the development of the manufacturing industry in Cabinda, Angola. It seeks to analyze the degree of economic integration of Cabinda and the dynamics of the manufacturing industry. Therefore, in the same way, to gather information to support the decision-making for public financing programs that will aim at the disengagement of the manufacturing industry in Angola and Cabinda in particular. The Cabinda Province is the 18^th of Angola, the enclave is located in a privileged area of the African and arable land.

Keywords: economic integration, industrial development, Cabinda industry, Angola

Procedia PDF Downloads 196

Authors: Misbah Shaheen, Anam Qureshi

Abstract:

In this work we will discuss the challenges faced by tourism industry in Pakistan. Tourism plays vital role in the socio-economic growth of a country. The countries of world, with less tourism opportunities are lagging behind from other nations of the world. Pakistan is one of those countries which rich in historical places, natural beauty, and uniqueness in handmade items and also of green forests. Present study will discuss the challenges being faced by tourism industry with special focus on hotel industry and law and order situation.

Keywords: Pakistan, tourism, handmade items, hotel industry

Procedia PDF Downloads 362