Proceedings of the 8th International Conference on Mechanical, Automotive and Materials Engineering
CMAME 2022, 16–18 December, Hanoi, Vietnam
Lecture Notes in Mechanical Engineering
Series Editors
Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia
Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy
Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine
Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia
Editorial Board
Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain
Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany
Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA
Justyna Trojanowska, Poznan University of Technology, Poznan, Poland
Jinyang Xu, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Conference Committees
Conference Committee Chairs
John P. T. Mo, Royal Melbourne Institute of Technology, Australia
Nguyen Quang Liem, Vietnam Academy of Science and Technology, Vietnam
Program Committee Chairs
Nguyen Chi Ngon, Can Tho University, Vietnam
Xingjian Jing, City University of Hong Kong, Hong Kong
Steering Committees
Nguyen Truong Thinh, HCMC University of Technology and Education, Vietnam
Huu Loc Nguyen, Ho Chi Minh City University of Technology, Vietnam
Publicity Committee
Yu-Liang Chen, Chung Cheng Institute of Technology, National Defense University, Taiwan
International Technical Committees
Trong-Phuoc Huynh, Can Tho University, Vietnam
Phu Do Xuan, Vietnamese-German University, Vietnam
Anh Vu Nguyen, Viettel Aerospace Institute, Vietnam
Linh Tung Vo, Cao Thang Technical College, Vietnam
Muhamad Arfauz A Rahman, Queen’s University of Belfast, UK
Mohd Rizal Salleh, Universiti Teknikal Malaysia Melaka, Malaysia
Kheng-Lim Goh, Newcastle University, UK
Prodip Das, Newcastle University, UK
Eram Asghar, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan
Thanakom Soontornchainacksaeng, King Mongkut’s University of Technology
North Bangkok, Thailand
Jiang Hua Zhou, Qinhuangdao City, Hebei Province, China
Meng Shaohua, Beijing Institute of Spacecraft Environment Engineering, China
Zhangming Wu, Cardiff University, UK
Ngoc-Tam Bui, Shibaura Institute of Technology, Japan
N. Jeyaprakash, National Taipei University of Technology, Taiwan
Alexander Kuzmin, St. Petersburg State University, Russia
Preface
These proceedings contain the 8th International Conference on Mechanical, Automotive and Materials Engineering (CMAME 2022), which was held as virtual conference during December 16–18, 2022, and hosted by the Vietnam Academy of Science and Technology. The aim as well as objective of CMAME is to present the latest research and results of scientists working in the field related to Mechanical, Automotive and Materials Engineering topics.
Despite travel constraints and virtual proceedings, CMAME2022 received submissions from Vietnam, China, Russia, Korea, Turkey, Perú, Canada, and Portugal illustrating that the conference has substantial support in the international engineering community. All full papers presented at CMAME 2022 have gone through thorough blind peer review by conference committee members and international experts. The paper quality, novelty and research significance are recognised. The manuscripts collected in these proceedings have been revised according to the review comments. The conference proceedings can be divided into three sections: (1) Dynamic Machinery and System Model, (2) General Mechanical Structure Design and Performance Test and (3) Mechanical Properties and Mechanical Analysis of Materials.
Part one has four papers mainly on the design of aerospace components, such as blading design and turbine blades. One paper presents the design performance of three-axle vehicles. The turbine blade papers are all related to aviation applications, for example, gas turbine for civil applications. This section has a particular focus.
Part two has four papers related to control and motion analysis of flying objects and vehicles of different power sources. All papers have demonstrated strong analytical background with interesting simulations. A couple of system designs have been verified partially by some prototype or experimental setting. This section represents typical state-of-the-art mechanical and materials engineering developments.
Part three has six papers focusing on materials properties of a variety of materials including plastics, aluminium, composites and titanium alloy. Interestingly, two papers present materials research related to 3D printing applications. Some of them develop sophisticated experimental settings for testing materials properties in special
environment. This section strikes a balance between experimental and theoretical approaches in materials engineering.
We would like to sincerely thank various contributors, reviewers and organising committee members for providing a great conference platform to engage discussions among the participants. We believe that the conference series will continue in the future and will again provide an effective platform for further exchange of advanced know-how and knowledge while also fostering potential international research collaborations in the topics of Mechanical, Automotive and Materials Engineering.
Melbourne, Australia
John P.T. Mo Conference Committee Chair CMAME 2022
Prof.
Dynamic Machinery and System Model
Effect of Suspension System Stiffness on Dynamic Load Three Axle Vehicle .......................................................
Luong Van Van, Cao Hung Phi, and Nguyen Thanh Tung
Trajectory Inference Optimization Based on Improved DR Algorithm .....................................................
Li Yao-yu, Hou Fei, Ren Wei, and Ma Man-hao
3
15
Research on AGV Chassis Structural Renovation Based on Uphill and Downhill Scenarios 25
Nan Xia, Xinxin Liang, and Wenliang Li
Finding Recommendations for Selecting Numerical Model Settings for Efficient Simulation of the Working Process of an Axial Turbine Blade with Convective Cooling 37
Andrei Volkov, Valery Matveev, Oleg Baturin, Ivan Kudryashov, and Sergei Melnikov
General Mechanical Structure Design and Performance Test
A Novel Design of Automatic Longan Seed Removing Machine ........ 59
Ngoc-Kien Nguyen, Thanh-Tung Pham, and Van-Tinh Nguyen
A Semi-analytical Approach for Dynamic Characteristics of Beams with the Effect of Static Load .......................................
Xuan Yang, Yanbin Li, Qiang Chen, and Qingguo Fei
Milling Tool Wear Prediction Based on 1DCNN-LSTM ...............
Wanliang Xia, Jin Zhou, Wenju Jia, and Miaoxian Guo
67
77
The Influence of Road Quality on Oscillating of Multi-purpose Forest Fire Fighting Vehicle 93
Luong Van Van, Chau Cong Hau, and To Ngoc Luat
Mechanical Properties and Mechanical Analysis of Materials
Filaments for 3D Printers from Surgical Masks, Cornstarch and Plastic Bottles Generated by COVID-19 .........................
Kevin Aliaga, Enori Zevallos, Corina Arroyo, Deysi Aliaga, Ariana Casimiro, and Nelida Tantavilca
AFM Analysis of 3D Printing PEI for Automotive Applications
Khanh Q. Nguyen, Pascal Y. Vuillaume, Mathieu Robert, and Saïd Elkoun
107
123
Life Cycle Assessment of Wire Arc Additive Manufacturing Process 135
Samruddha Kokare, Florinda Matos, J. P. Oliveira, and Radu Godina
Development of Cost-Effective Sustainable Hybrid Composites Based on Recycled PP and Chopped Carbon Fibers ................... 145
Alaeddin Burak Irez and Sukru Yirik
Mechanism of Droplet Coalescence in Cylindrical Hydrocyclone .......
Jing Zhang, Yongyao Sun, Xinqiang Xiong, Mingjun Du, and Shiying Shi
Influence of Geometric Imperfections on Global Buckling Strengths of Cold-Rolled Aluminium Alloy Channel Columns .........
157
171 Ngoc Hieu Pham
Dynamic Machinery and System Model
Effect of Suspension System Stiffness on Dynamic Load Three Axle Vehicle
Luong Van Van, Cao Hung Phi, and Nguyen Thanh Tung
Abstract 3-axle trucks play an important role in the freight transport network in Vietnam due to their high transport capacity. In the working process, Dynamic loads greatly affect the axle house, road, and vehicle dynamic safety. This paper presents the survey results on the influence of suspension system stiffness on the dynamic load of a 3-axle truck when the vehicle goes through a random bumpy road according to ISO 8608:2016 standard. The purpose of the study is to build and simulate the vehicle’s dynamics model to determine the dynamic load acting on the road. A dynamic model of a 3-axle truck is simulated using Matlab/Simulink. Dynamic loads generated by the excitation from the road surface bump to the wheels through the suspension. The model is applied to study the vibration and noise and analyze the structural durability of vehicles.
Keywords Suspension system · Dynamic load · 3-axle truck · ISO 8608:2016
1 Introduction
Heavy-duty trucks today play an important role in the freight network in Vietnam, as they meet the demands for durability and productivity in transporting goods with high frequency. Typical for the heavy truck segment is the 3-axle truck developed by Hyundai with the number HD270. This is a popular vehicle used for transporting materials and goods. Contributing a significant part to connecting economic regions and promoting markets, connecting goods for synchronous economic development
L. Van Van (B) · C. H. Phi · N. T. Tung
Vinh Long University of Technology Education, 73, Nguyen Hue Street, Vinh Long City, Vietnam
J. P. T. Mo (ed.), Proceedings of the 8th International Conference on Mechanical, Automotive and Materials Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-3672-4_1
across the country. Along with those benefits, the high frequency of transportation also entails a lot of heavy consequences and the issue that needs attention is the impact of dynamic loads on the road surface and dynamic loads from the road surface move to the car. Today’s roads are classified, when designing roads, it is necessary to rely on international and Vietnamese standards. The design specifications are the static loads of the shafts and the dynamic loads. The static load is according to the standard that countries have to choose first, dynamic load is the factor affecting the road characterized by the tire-road interaction, the characteristic of that interaction is dynamic load, which depends on the road profile, vehicle structure, suspension stiffness, wheel formula [1–4]. Thus, the dynamic load depends on both the vehicle and the road. Currently, ISO 8608:2016 [5–7] defines the corresponding types of roads as a standard for vehicle–road correlation surveys (Figs. 1 and 2).
Fig. 1 Road surface in Vietnam under dynamic load [2]
Fig. 2 Random road profile [6, 7]
Based on ISO 8608:2016 [2, 3, 7], there are 4 standard roughness forms corresponding to hmax = [0.015 0.025 0.05 0.1] according to B; C; D and E with random road parameters, we can calculate C i from the frequencies f min = 0.05 Hz, f max = 50 Hz built and run at a speed v = 40 km/h. There are many criteria to evaluate road pressure, but in this study, the author chooses dynamic load coefficient (K d-)[2, 8]to evaluate. The dynamic load factor K d- is calculated as follows:
According to Russian standard 218.046–01, the maximum dynamic load (K d-) selected when designing is 1.3 [1, 9–11].
2 Model Building
The 3-wheel truck is a deformed mechanical system, characterized by this deformation is deformation of the elastic elements through the tires, springs, joints, and chassis. In this study, the author uses a mechanical model based on the system of Newton–Euler equations to build. The dynamic model is built including a description of vehicle vibration with vertical (Z s ), pitch angle (θ sy ), roll angle (θ sx )motionofthe suspended mass; vertical and horizontal motion of the mass is not suspended at each axle (Z i , θ i ). To study stochastic oscillation and have an experimental basis on the road, the oscillation model was established in the form of a dynamic model (Fig. 3).
3 Model of 3-axle truck oscillation in space [2, 5]
Fig.
Applying Newton–Euler equations to the oscillating mass system, we can establish a system of oscillating differential equations of the system consisting of 9 equations as follows:
sy l 3 − Z s ) + 2C 56 l 3 ( Z 3 + θsy l 3 − Z s ) = 0(3)
Jsx θsx + 2 K 12 d 2 (θsx − θu 1 ) + 2C 12 d 2 (θsx − θu 1 ) + 2 K 34 d 2 (θsx − θu 2 ) + 2C 34 d 2 (θsx − θu 2 ) + 2 K 56 d 2 ( ˙ θsx − ˙ θu 3 ) + 2C 56 d 2 (θsx − θu 3 ) = 0(4)
m 1 ¨ Z 1 + 2 K 12 ( ˙ Z 1 − ˙ Z s − ˙ θsy l 1 ) + 2C 12 ( Z 1 − Z s − θsy l 1 ) + K L 12 (2 ˙ Z 1 − ˙ h 11 − ˙ h 21 ) + C L 12 (2 Z 1 − h 11 − h 21 ) = 0(5) J1 ¨ θ1 + 2 K 12 d 2 ( ˙ θ1 − ˙ θsx ) + 2C 12 d 2 (θ1 − θsx ) + K L 12 B (2 ˙ θ1 B − h 11 + h 21 ) + C L 12 B (2θ1 B − h 11 + h 12 ) = 0(6)
m 2 ¨ Z 2 + 2 K 34 ( ˙ Z 2 − ˙ Z s + θsy l 2 ) + 2C 34 ( Z 2 − Z s + θsy l 2 ) + K L 34 (2 ˙ Z 2 − ˙ h 32 − ˙ h 42 ) + C L 34 (2 Z 2 − h 32 − h 42 ) = 0(7)
J2 ¨ θ2 + 2 K 34 d 2 ( ˙ θ2
The spatial dynamics model of the 3-axle construction truck is simulated by numerical Matlab simulation software with the “ode 5, fixed-step” solving algorithm, the simulation results are presented in the next section.
3 Survey and Assessment of Dynamic Loads Acting on Vehicles
When the vehicle is on the road, the excitation function from the road surface is random. To classify more conveniently in the design, construction, and survey of the international standard ISO 8608:2016 have given the types of roads as analyzed above. The survey parameters correspond to each type of road, and speed. Vehicle load when surveying is a full load. The survey results for evaluation are the dynamic load factor and the maximum dynamic load factor, the lowest survey speed is 20 km/ h, and the jump is 10 km/h. For roads B, C, D and E, the maximum allowed speed is 90 km/h, 80 km/h, 60 km/h and 50 km/h [1, 2], respectively.
3.1 Survey to Determine Dynamic Load Factor
As analyzed above, the survey method determines the maximum value of the characteristic parameters when surveying with the excitation function which is a random function with the type of D and E roads (because this is the type of road with high volatility, the largest scale), the survey speed is 60 km/h for D and 50 km/h for E. The graphs from Figs. 4, 5, 6 and 7 depict the dynamic load coefficients along the length of the D and E road at the left front wheel (K d-11 ) and the left rear wheel (K d-31 ). The graphs also determine the maximum value.
The survey results show that when the vehicle is moving on bad roads (D) with a speed of 60 km/h, the maximum dynamic load coefficients at the front and rear wheels are 1.18 and 1.23 respectively, compared to standard 218.046–01, the maximum dynamic load factor (K d-) is less than the allowable limit value; When the vehicle move on a very bad road (E) with a speed of 50 km/h, the maximum dynamic load coefficients at the front and rear wheels are 1.38 and 1.45 respectively, compared
Fig. 4 The left front wheel dynamic load factor on road D
Fig. 5 The left front wheel of dynamic load factor on road E
Fig. 6 max(K d-11 ), max(K d-31 ) depends on the speed with road type B
Fig. 7 max(K d-11 ), max(K d-31 ) depends on the speed with road type C
to standard 218.046–01, the maximum dynamic load factor (K d-) is larger than the allowable limit value.
3.2 Investigate the Maximum Dynamic Load Factor
In this study, the author investigates the maximum value of the dynamic load factor at the left rear wheel max(K d-31 ). Figures 8, 9, 10 and 11 show the max(K d-31 ) value for each type of road and by speed. For an overview, the graphs of Figs. 12 and 13 are the summaries of the values of the dynamic load on the left front wheel max(K d-11 ) and the dynamic load on the left rear wheel max(K d-31 ) on different types different paths and speeds. Table 1 is a summary of the values of the maximum dynamic load factor of the left rear wheel.
Fig. 8 max(K d-11 ), max(K d-31 ) depends on the speed with road type D
Fig. 9 max(K d-11 ), max(K d-31the ) depends on the speed with road type E
Fig. 10 max(K d-11 ) depends on the speed and road types
Fig. 11 max(K d-31 ) depends on the speed and road types
Fig. 12 max(K d-31 ) depends on the speed with road type B
Fig. 13 max(K d-31 ) depends on the speed with road type C
Table 1 Summary of the maximum values of the dynamic load factor at the left front wheel max(K d-11 ) and the left rear wheel max(K d-31 ) depending on the speed and road types
Type of road Dynamic load factor Vehicle speed (km/h)
The maximum dynamic load factor of the left front wheel and the left rear wheel, corresponding to B, C, D and E roads, the maximum dynamic load coefficient is 1.525 at the left rear wheel when the vehicle is moving at a speed of 90 km/h on the E road. The general synthesis of these values is shown in Figs. 12 and 13. With the same type of road, when the vehicle speed increases, the maximum dynamic load factor tends to increase (road D: v = 40 km/ h, max(K d-31 ) = 1.1575; v = 50 km/h, max(K d-31 ) = 1.1897; v = 60 km/h, max(K d-31 ) = 1.4349; v = 70 km/h, max(K d-31 ) = 1.2436; v = 80 km/h, max(K d-31 ) = 1.2488; v = 90 km/h, max(K d-31 ) = 1.26811). At the same speed, the worse the road, the higher this coefficient increases. With a speed of 60 km/h; road B, max(K d-11 ) = 1.0477; C road, max(K d-11 ) = 1.1011; D, max(K d-11 ) = 1.1866; E–F road, max(K d-11 ) = 1.4349.
Fig. 14 max(K d-31 ) depends on the speed with road type
3.3 Investigate the Influence of Suspension Stiffness on the Maximum Dynamic Load Factor
To be able to realize the influence of suspension stiffness on dynamic loads, we investigate to determine the dynamic load coefficient when increasing the suspension stiffness to double (increase stiffness; C1 = 532332 N, C2 = C3 = 1090754 N) and reduce the suspension stiffness by half (decrease stiffness; C1 = 133083 N, C2 = C3 = 272689 N) from the original stiffness. Investigate the influence of suspension stiffness on dynamic loads with random roads according to ISO 8608:2016 on types of roads B, C, D, and E. The author determines the dynamic load factor (K d-), and the maximum dynamic load factor max(K d-) and compares it with the original suspension stiffness value to evaluate and propose to match the road design in Viet Nam. Because the above survey values show that the dynamic load factor at the rear wheel is larger than the dynamic load factor at the front wheel, the author only shows the dynamic load factor at the rear wheel in the graphs (Figs. 14 and 15, Tables 2 and 3).
The survey results show that when the suspension stiffness is increased, the dynamic load coefficient increases, and vice versa when the suspension stiffness is decreased, the dynamic load coefficient decreases. When the stiffness is increased, when the vehicle moves on the good road (B) and the average road (C) with a speed of less than 90 km/h, the maximum dynamic load coefficient at the front and rear wheels are both less than 1.3; When the vehicle moves on a bad road (D), the maximum dynamic load factor at the front wheel is less than 1.3, on the D road with a speed of 60 km/h or more, the dynamic load factor at the rear wheel is the largest. greater than 1.3; When the vehicle is traveling on very bad roads (E) with a speed of 40 km/h or more, the maximum dynamic load coefficients at the front and rear wheels are both greater than 1.3. When reducing the stiffness, the maximum dynamic load coefficient when the vehicle moves on roads B, C, and D are all less than 1.3.
Fig. 15 max(K d-31 ) depends on the speed with road type E
Table 2 Summary of the maximum values of the dynamic load factor at the left front wheel max(K d-11 ) and the left rear wheel max(K d-31 ) depending on the speed and road types when increasing the suspension stiffness
Type of road Dynamic load factor Vehicle speed (km/h)
Table 3 Summary of the maximum values of the dynamic load coefficient at the left front wheel max(K d-11 ) and the left rear wheel max(K d-31 ) depending on the speed and road types when reducing the suspension stiffness
Type
)
)
)
4 Conclusion
The article investigated random agitation for 4 types of roads according to ISO 8608:2016 on B (good road), C (average), D (bad), and E (very bad) roads. The dynamic load coefficients K d- and the maximum dynamic load coefficients (maxK d-) have been determined for moving speeds on different types of roads. The results also investigated when changing the suspension stiffness to evaluate the dynamic load, when increasing the suspension stiffness, the dynamic load increases, and vice versa when the suspension stiffness is decreased, the dynamic load decreases. When the suspension stiffness is reduced, the dynamic load coefficient when the vehicle is moving on roads B, C, and D are all less than 1.3, on the high road (E, maximum speed 50 km/h) when the vehicle is in motion at speeds below 60 km/h, the dynamic load factor is less than 1.3.
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Trajectory Inference Optimization Based on Improved DR Algorithm
Li Yao-yu, Hou Fei, Ren Wei, and Ma Man-hao
Abstract Trajectory planning is often encountered in distribute tactical virtual simulation. To improve the non-smooth problem caused by the nonunified step of components in the simulation of naval battlefield, we proposed an improved DR algorithm, which is verified by simulation experiments, to provide a smooth trajectory inference method, and to achieve finer grain of trajectory inference in distributed interactive simulation.
Keywords DR algorithm · Trajectory planning · Distributed simulation
1 Introduction
The position of each intermediate time cannot be determined by interpolation for a model with continuous location information constraints provided by the simulation engine because at the beginning of each simulation moment [1], it does not know the precise location information, or refinement end position, which should be provided in the following simulation moment.
Only the current location or entity information of the prior steps can be used to predict the position information of the subsequent refining points [2]. The model is then modified to reflect the comparison results in order to minimize prediction error at the subsequent simulation moment, which compares the entity position information provided by the simulation engine with the predicted information.
L. Yao-yu (B) M. Man-hao
Science and Technology On Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China e-mail: garett_1984@hotmail.com
J. P. T. Mo (ed.), Proceedings of the 8th International Conference on Mechanical, Automotive and Materials Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-3672-4_2
A common state estimation and error correction approach in distributed interactive systems is the DR algorithm. This paper enhances the current DR algorithm in accordance with the ship’s movement legislation starting from building of an improved model with limits on the location information [3]. To forecast and smooth out the movement of ships, an improved DR algorithm based on a movement model is suggested.
2 DR Algorithm
2.1 Principle of Dead Reckoning
Dead Reckoning, known as DR, is a navigational technique that involves utilizing a previously established location to calculate the present position of a moving object, as well as adding estimations of its speed, heading, and course over the length of time [4].
As a result of the growing distance between the simulation nodes, and the number of nodes in the entire simulation system has expanded along with the size of distributed interaction simulation, the amount of interactive data exchanged between each node has raised system network load and decreased the effectiveness of system simulation operation, the system’s synchronization has been completely disrupted by the information transmission latency.
The DR algorithm is a key component of DIS (Distributed Interactive Simulation), and it focuses on reducing the delivery of interaction information between each simulation node reasonably while compensating for transmission delays [5]. This lowers the network’s transmission load and boosts the effectiveness of the entire distributed interactive system.
Local simulation nodes must forecast the state of the simulation node that changes in the interaction connection in order to limit interactive information transmission between distributed nodes and maintain the regular functioning of the full distributed simulation system at the same time. Each simulation node in the DR method has a low-level DR model for the state recursive in addition to a high-level exact model defining its own imitation of the state.
The node with the DR model used to register the entity to the node locally is also preserved by other nodes that interact with this simulation node [6]. As illustrated in Fig. 1, when the accurate state surpasses the threshold, the node communicates the precise state information of the local entity to the simulation node participating in the interaction and modifies every attribute of this node’s DR model.
