Engine Manufacturers Research Articles

Preface

It is our great pleasure to have you at the 2023 9th International Forum on Manufacturing Technology and Engineering Materials (ICMTEM 2023), which took place in Shenyang, China from December 15th to 17th, 2023. ICMTEM 2023 was successfully held and received unanimous praise from various experts and scholars all over the world.ICMTEM 2023 existed as an international forum for academic communications between experts and scholars in the fields of Manufacturing Technology and Engineering Materials. It promoted the research and developmental activities in related fields and scientific information interchange among all the participants, establishing connections for them to find global partners for potential collaboration.After months of well preparation and hard work, the Proceedings of ICMTEM 2023 covering a bunch of papers, having been checked through rigorous review processes to meet the requirements of publication, are smoothly published. These papers feature but are not limited to the following topics: Electronic Materials, Advanced Forming Manufacturing and Equipment, Industrial Robotics and Automation, Dynamic Mechanical Analysis, Optimization and Control, etc.We had about 150 participations both as speakers and participants, including researchers, industrial experts, teachers, students, and other practitioners. During the keynote speech part, each keynote speaker was given 30 – 40 minutes for keynote speech. The keynote speakers include Professor Yunqing Rao (Huazhong University of Science and Technology, China), Professor Jiang Guo (Dalian University of Technology, China), Professor Zidong Wang (University of Science and Technology Beijing, China), and Professor Shahid Hussain (Jiangsu University, China). They shared their own research experience and special insights by presenting their research achievements, triggering heated and interesting discussions and a good number of informal talks among all the participants.We would like to express our gratitude to the conference organizers, keynote speakers, authors, and all the participants of ICMTEM 2023, for their fruitful work and contribution to the success of the Conference. We are thankful to all the reviewers for providing constructive criticisms, stimulating comments and suggestions to the authors. Particularly, our special thanks go to Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume.May this event be a spark that ignites many more new applications and inventions in the fields of and related to manufacturing technology and engineering materials!The Committee of ICMTEM 2023List of Committee Member is available in this pdf.

THE DEVELOPMENT OF PRECISION MEASUREMENT SKILLS PACKAGES ON OUTSIDE MICROMETERS FOR THE FACULTY OF INDUSTRIAL EDUCATION’S STUDENTS, KING MONGKUT'S UNIVERSITY OF TECHNOLOGY NORTH BANGKOK

This research was to create Precision Measurement Skills Packages. The samples used in this research consisted of 29 undergraduate students from the Manufacturing and Industrial Engineering Program, Department of Mechanical Education, King Mongkut's University of Technology North Bangkok. The design of this research is an experiment with one experimental group (One shot case study). The researchers created a skill package of precision measurement on outside micrometers. The researchers used a cluster sampling method, instruments consisted of 1) Precision measurements on outside micrometers and 2) Quality assessment form of skills packages of precision measurements on outside micrometers. The learners achievement was compared to the standard by using a t-test. The results of the research showed that: Appropriateness of precision measurements on outside micrometers Overall, it is appropriate at a very good level ( x̄ = 4.66, SD = 0.48). The results of finding the effectiveness of the of precision measurements on outside micrometer were found that the efficiency of (E1) is 87.01 and the efficiency of (E2) is 88.16. It was found that the effectiveness of precision measurements on outside micrometers that the researchers created has higher efficiency than the criteria set, namely 80/80. Comparison of learning achievement with skills packages of precision measurements on outside micrometers against criteriaIt was found that the students had overall academic achievement with an average of 26.45 with a standard deviation of 0.41, which was higher than the predetermined standard with a significant level of .05.

A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment

Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques.

Investigation into the Impact of Groove Shape on the Tensile Strength of Commercial Steel

In the industrial landscape, welding holds a prominent position, with a significant demand for both effective and high-quality welding processes. Manufacturers, striving to maintain competitiveness, rely on their manufacturing engineers and production personnel to swiftly and efficiently establish manufacturing processes for new products. Among the various welding methods employed, Gas Metal Arc Welding (GMAW) stands out as one of the most widely utilized processes. Several input factors, including welding current, welding voltage, gas flow rate, wire feed speed (WFS), wire size, and welding speed, play a crucial role in determining the quality of welding outcomes. Taguchi's design, recognized for its effectiveness, serves as a powerful optimization tool for enhancing the quality and performance output of manufacturing processes. In this particular study, GMAW was employed to weld commercial steel under predetermined factors of welding voltage, WFS, and groove shape. The X-groove welding exhibited lower tensile strength and hardness compared to V-groove weldments. Utilizing Taguchi's design, the objective was to identify the optimal process factors that would result in higher tensile strength and hardness. The analysis revealed that welding groove shape had the most significant impact on both tensile strength and hardness, followed by voltage. In contrast, WFS exerted the least influence on tensile strength and hardness. The optimized combination of welding factors identified was a V-groove shape, 20 V welding voltage, and a 5.9 m/min WFS.

Elemental Influence on Oxidation Behaviour of Cantor‐Based and Refractory High Entropy Alloys: A Critical Review

This article reviews the oxidation behaviour of the two most significant high entropy alloy systems (HEAs) namely Cantor‐based and refractory HEAs. HEAs have been extensively researched and show potential applications in various industries, including marine engine manufacturing, chemical industry, furnaces, ducting, heat exchangers, jet engines, steam turbines, nuclear reactors and electronic devices, among others. Effect of the presence of elements such as aluminium, manganese, chromium, silicon, tantalum, vanadium etc. is studied for catalysing the oxidation of HEAs. Aluminium, chromium, and silicon are reportedly found to considerably impact the oxidation kinetics and enhance the oxidation resistance. However, silicon can positively or adversely affect the oxidation resistance depending on its concentration and alloy composition. Other elements like manganese tend to adversely impact the oxidation resistance of FeCoNi‐based HEAs. Refractory elements are typically found to be not suitable for oxidation studies due to the formation of non‐protective oxide layers. However, refractory HEAs offer interesting trends both in terms of enhancing or reducing oxidation resistance depending on the alloying elements. Similarly, findings related to other elements are also presented and elaborated.

A Comprehensive Comparison of IBM Cloud and Microsoft Azure

The ever-growing demand for scalability, agility, and cost-effectiveness has fuelled cloud computing to the forefront of modern IT infrastructure. According to Precedence Research, the global cloud computing market size was valued at USD 480 billion in 2022 and is expected to hit USD 2297.37 billion by 2032 with a registered CAGR of 17% from 2023 to 2032 [1]. The growth of cloud computing is driven by various factors such as the rapid evolution of technology and the globalization of businesses. Cloud computing is already embraced in various industries like Financial Services, Manufacturing, Healthcare, Retail, Education, and Architecture Engineering & Construction (AEC), where it streamlines operations, enhances collaboration, and revolutionizes processes. Cloud computing holds immense potential, especially in sectors like healthcare, real estate, tourism, and small and medium enterprises, where it will facilitate faster and more informed decision-making processes through data collection and analysis. Additionally, cloud technology is revolutionizing government sectors by driving cost savings, enhancing efficiency, and promoting transparency and citizen engagement. With the increasing number of cloud computing service providers such as IBM, Microsoft, Google, Amazon, and others, it becomes crucial to delve into their technical nuances. Understanding how each provider's offerings align with specific business requirements, technical specifications, and future scalability is paramount for organizations venturing into their cloud journey. The main purpose of this paper is to explore and evaluate the technical dimensions of IBM Cloud and Microsoft Azure and understand its future potential.

Artificial Intelligence Approach in Aerospace for Error Mitigation

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

Experimental Investigation of Tip Design Effects on the Unsteady Aerodynamics and Heat Transfer of a High-Speed Turbine

Abstract While modern engine manufacturers devote significant efforts to the development of reliable and efficient machines, the introduction of novel, optimized components in the hot gas path represents a risky opportunity. Accurate experimental and numerical data are critical to assess the impact of new technologies on the harsh engine environment. The present study addresses the impact of a selection of high-performance rotor blade tips on the aerodynamic and heat flux field of a high-pressure turbine (HPT) stage. A combined numerical and experimental approach is employed to characterize the interaction of the tip leakage flow with the rotor secondary flows and the casing heat transfer mechanisms for each individual tip geometry. The turbine stage is tested at engine-scaled conditions in the rotating turbine facility of the von Karman Institute. In the present study, the turbine rotor is operated in rainbow configuration to allow the simultaneous testing of multiple blade tip geometries. Reynolds-averaged Navier–Stokes (RANS) simulations are employed to predict the aerodynamic and thermal fields of the individual profiles using test-calibrated boundary conditions. Isothermal steady computations are performed at different wall temperatures to compute the adiabatic wall temperature and the heat transfer convective coefficient. Low-order models are used to represent the over-tip thermal field and the driving heat transfer mechanisms. The time-resolved outlet flow is characterized using a vortex tracking technique and high-frequency aerodynamic measurements to identify the rotor secondary flow structures.

Exergy analysis under consideration of operational parameters by numerical approach in a two-stroke marine diesel engine

This paper presents a numerical approach of the influence of operational parameters on the exergy terms in a two-stroke marine diesel engine. Operational parameters are introduced as different start of injection (-4, -2, 0 and 2 °CA) and scavenge air inlet temperature (300, 312, 330 and 340 K) values. The energy analysis is conducted within a closed cycle by using a three-dimensional computational fluid dynamics code. The simulation results are compared with the corresponding experimental pressure data in the literature and show good agreement. Also, the exergy analysis is performed by a developed in-house computational code. Various exergy rates and cumulative exergies are detected and computed separately at operational parameters. The study reveals that varying the start of injection timing from −4 to 2 °CA at full load condition results in a decrease in exergy efficiency from 43.45 % to 41.36 %. Furthermore, an increase in scavenge air inlet temperature from 300 K to 340 K leads to a reduction in exergy efficiency from 42.83 % to 41.44 %. These findings underscore the critical influence of injection timing and scavenge air temperature on the exergy performance. In light of these results, this paper gains more insight into the impact of design parameters of a two-stroke marine diesel engine on exergy efficiency and presents a preliminary data for marine diesel engine manufacturers in the design of a two-stroke marine diesel engine that uses energy efficiently.

Investigation on toxicity of ammonia releasing from storage tank onboard through CFD simulations

ABSTRACT This study investigates potential risks associated with ammonia releases from a case ship to be constructed in a Korean Shipyard and operated in Korean coastal areas. The focus is on understanding how ammonia disperses from the onboard storage tank’s Vent Master and the associated toxic zones. Numerical simulations using CFD tools were employed to model ammonia gas dispersion scenarios. The study revealed that if the safety valve opens, the high internal pressure of the ammonia tank could release a significant amount of ammonia through the vent mast. Thus, determining the position and height of the vent mast is crucial for safety design. Simulations showed that even if ammonia is released at sea through the vent mast, it poses minimal risk to nearby residential areas. The vent mast, positioned at least 4 m above the ship’s deck, ensured rapid gas dispersion, reducing the chance of human exposure. The findings aim to enhance the safety design and operation of ships, providing valuable reference material for policymakers, regulators, engine manufacturers, and ammonia suppliers, ultimately contributing to the establishment of safe ship design and construction practices in the future.

Preface

The 2nd International Conference on Mechanical Engineering and Applied Sciences, 2022 (ICMEAS – 2022) was a continuation of the previous endeavor dedicated to gather a significant number of diverse leading scholar, scientist, academician, researchers, industrial experts etc., together from different parts of the globe and exchange their experiences and researches. That conference offered a platform to junior researchers and scientist to have an exposure to a wider but uniquely intellectual community and have interaction with them. It gave an exceptional value for students, academics and industry researchers. The ICMEAS – 2022 also provided a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Mechanical, Manufacturing, Aeronautical, Naval, and Energy Engineering and other related fields. This conference was run both online and physical modes over a period of three days of time.The ICMEAS – 2022 conference was a great success with the relentless effort of each member of the organizing committee and active participation of the organizing partners, and spontaneous response of the scholars, researchers and scientist from around the world. The voluntary contribution of the scientific community in completing the rigorous peer review process was remarkable.It would not be possible without the unreserved patronization of the Commandant MIST and persistent conviction of the Chair, Co-Chair and Organizing Chair of the ICMEAS – 2022 conference. Last but not the least, contribution of all sponsors, particularly MJL Bangladesh Limited was undeniable to make that conference successful.List of Organising Committee, Advisory Committee, Scientific Committee, Publication and Technical Sub Committee, Reviewers are available in this pdf.

Artificial Intelligence: A Way to Promote Innovation

The theme of artificial intelligence is how to use it to make computers useful in solving problems concerning health. We interpret the data obtained by diagnosing various diseases, such as various types of cancer, diabetes, etc. The largest scientific goal of information construction—the theory of processing intelligence. It is the science and engineering of manufacturing. Intelligent machines, especially an intelligent computer. This work presents artificial intelligence (A.I.). Study how to make computers that have some characteristics of the human mind. A.I. systems are now routinely used in the economy, Medicine, and the military. They also have broad data that can potentially solve many problems in clinical trials. This article provides an overview of A.I. and its innovations. It is one of the cutting-edge technologies shaping the future of pharmacy. Various advanced systems, such as mathematics, machining performance, cloud computing, and algorithm design, have accelerated the development of methods that can be used to analyze, interpret, and make predictions using these data sources. We can learn how to get machines to solve problems by observing others.There has been sporadic research growth in two main areas: genomics and digital Medicine. This article examines the introduction, definition, history, applications, and innovation in pharmacy.

Investigating the Effects of Environmentally Friendly Additives on the Exhaust Gas Composition and Fuel Consumption of an Internal Combustion Engine

This article shows the effect of the addition of effective microorganisms and silver on the exhaust gas composition and fuel consumption. Exhaust emission standards are becoming increasingly stringent, which makes it difficult for engine manufacturers to meet them. For this reason, intensive work is underway to use alternative propulsion systems on ships, and for diesel engines, alternative fuels. Among other things, this applies to mixtures of petroleum-based fuels with vegetable oils and their esters. Unfortunately, their use, due to their physicochemical properties, can negatively affect the performance of the engine and the wear of its components. Therefore, the aim of this study was to see how additives of effective microorganisms in the form of ceramic liquid and tubes, and a silver solution and colloidal silver would affect some engine parameters, including the exhaust gas composition and fuel consumption. The authors are not aware of the results of previous research on this issue. The tests were carried out on a diesel engine for four types of green additives at concentrations of 2% and 5%, at different ranges of its load. The additives added to the diesel fuel were characterised, and the test stand was presented, along with the parameters of the tested fuel. The effect of additives on selected engine parameters, including fuel consumption, was presented. The characteristics of hourly fuel consumption and selected components of the exhaust gas, including nitrogen oxides, carbon monoxide and carbon dioxide as a function of the concentration of ecological additives are shown and analysed. It was found that the most beneficial additive that had a positive effect on the exhaust gas composition and fuel consumption was a silver solution in a 2% concentration. There was a decrease of up to 4% in the NOx content of the exhaust gas, a decrease in carbon monoxide of more than 28%, a decrease in carbon dioxide of 4.6% and a decrease in fuel consumption of around 3% was achieved under the tested conditions. The use of these additives is an innovative solution that has a positive impact on reducing the emissions of harmful compounds into the atmosphere. In further research, it will be necessary to study the effect of this additive on the combustion process in the engine and the wear of its components, as well as to confirm the results obtained in real operating conditions.

On fuel and deposits. Part 1

Introduction (problem statement and relevance). Generally, deposits or sediments are an inevitable consequence of processes of mixture formation and combustion in the engine cylinder, complex physical and chemical processes with the hydrocarbon “participants” of this process – fuel and oil. There is no consensus in the scientific community regarding the role of these participants. Some researchers consider the “fuel” factor to be the predominant one, the others have no less reason to think it is the “oil” factor. Meanwhile, understanding the nature of formation of deposits, both the “cold” ones (in the intake path) and the “hot” ones (in the combustion chamber) is the most important condition in order to determine the methods to combat this negative phenomenon, in particular, and due to development of scientifically grounded requirements for qualitative characteristics of both fuels and oils. The relevance of the issue increases due to the fact that the existing methods of fuel testing based on foreign procedures with application of foreign reference engines become almost inaccessible following refusal of a number of the manufacturers of reference engines for standardized testing to provide further technical support caused by phasing-out of models of these engines. Moreover, during the period of common confusion in the European engine-building and uncertainty over prospects of the automotive industry on the whole, no solution was taken either for the procedure or for the new reference engine for the direct fuel injection engines increasingly dominant in the market of gasoline engines. The vacuum created is occupied by unapproved procedures with application of engines having different designs. In such conditions, correct comparison of test results obtained in different laboratories becomes almost impossible, while the results themselves lose their practical value.The purpose of the study and its practical significance consist in study of the mechanism of deposits formation in spark ignition engines and development of methods to test fuels for tendency to form certain types of deposits. Development of domestic scientific research methodological and experimental basis for engine testing of fuels and oils for deposits formation tendencies.Scientific novelty and results. The experimental studies, and the thermal gravimetric ones in particular, convincingly prove the hypothesis of domination of the fuel factor in formation of “cold” deposits on the intake valves. It has been experimentally shown that the results of testing following the new procedures adequately reflect the efficiency of using detergent additives.

Producing advanced alloys based on titanium aluminides for modern aircraft engine manufacturing

Producing advanced alloys based on titanium aluminides for modern aircraft engine manufacturing

From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI

From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

The mining industry faces persistent challenges related to hazardous gas emissions. Diesel engine-powered wheeled vehicles are commonly used during work shifts and are a primary source of nitrogen oxides (NOx) in underground mines. Despite diesel engine manufacturers providing gas generation data, mining companies need to predict NOx emissions from numerous load-haul-dumping (LHD) vehicles operating under dynamic conditions and not always equipped with gas sensors. This study focused on two ensemble methods: bootstrap aggregation (bagging) and least-square boosting (boosting) to predict NOx emissions. These approaches combine multiple weaker statistical models to yield a robust result. The innovation of this research is in the statistical analysis and selection of LHD vehicles’ working parameters, which are most suitable for NOx emission prediction; development of the procedure of source data cleaning and processing, model building and analyzing factors, which may influence the accuracy; and the comparison of two ensemble methods and showing their advantages and limitations for this specific engineering application, which was not previously reported in the literature. For datasets obtained from the same LHD vehicle and different operators, the more efficient bagging method gave a coefficient of determination R2 > 0.79 and the RMSE (root mean square error) was under 30 ppm, which is comparable with the measurement accuracy for transient regimes of physical NOx sensors available in the market. The obtained insights can be utilized as input for mine ventilation systems, enhancing mining transport management, reducing workplace air pollution, improving work planning, and enhancing personnel safety.

Improving the manufacturing of 3D printed insoles through a combined experimental and topology optimization approach

This study integrates additive manufacturing and advanced engineering to produce lightweight, mechanically suitable insoles. The research combines experimental data and computational techniques to enhance the manufacturing process. Through mechanical testing and computational homogenization, an alternative infill pattern is identified, allowing for faster printing. Topology optimization, based on Finite Element analysis, determines an optimal material distribution for the forefoot. Validation analyses confirm the structural performance of the optimized shape. Thanks to this procedure, a time reduction up to 50% can be expected. This approach allows for an efficient and sustainable alternative for the production of insoles.

Spiking autoencoder for nonlinear industrial process fault detection

In recent years, artificial neural networks have been found successful applications in process monitoring within metallurgy, chemical engineering and mechanical manufacturing owing to their superior ability to construct flexible models with varying degrees of nonlinearity and effectively handle large-scale data. However, the model's distinctiveness diminishes due to the high cost associated with neural network training and initializations, resulting in a more fluctuating fault detection performance. To alleviate this problem and inspired by the way biological neurons emit spikes to transmit information, the spiking neurons are employed in the construction of a spiking neural network, introducing a shift in parameter optimization from conventional global parameter tuning to two-stage layer-wise process. Based on this, a brain-like discrete model, spiking auto-encoder (SNNAE) is constructed. The SNNAE's training process is firstly compared with that of artificial neural networks with the same structure through modeling a numerical example, results show the superior efficiency and accuracy of SNNAE in modeling highly nonlinear data. To gauge its effectiveness in fault detection, SNNAE is then compared with the state-of-art methods through the same numerical example and a three-phase flow process, indicating its ability to significantly improve process monitoring performance in nonlinear processes while notably reducing its fluctuation.

Student Demand by Engineering Discipline: Trends and Observations at One Institution

Engineering students most commonly select their discipline (i.e., program) before or during their first year of studies. Knowing what the student demand is for different engineering disciplines is important for program planning and the curriculum changes, and it may be helpful to students in their selection in situations where placement is done on a competitive basis. In this study, the demand for thirteen different disciplines at a large Canadian university with a common first-year program (i.e., where students start in their discipline in second year). The findings of this study are consistent with national trends in that mechanical, civil, electrical, chemical, and computer engineering are in high demand, while manufacturing, environmental, mining, materials, and geological engineering are in lower demand. This study also considers the trends in demand, and reveals recent growth in biomedical, integrated, and computer engineering. Lastly, by considering patterns in student rankings, a mapping of programs commonly grouped together by students was determined.