Engine Block Research Articles

Improving heat transfer in an air-cooled engine by redesigning the fins

Heat transfer modelling and simulation were carried out in a single-cylinder, four-stroke, air-cooled engine to evaluate the heat transfer rate of the engine block. The modelling studies of cylinders with different numbers of fins and different geometry were performed using the SolidWorks computer platform. The tested components were made of 6063-T6 aluminium alloy castings. The simulation concerned different numbers of fins as well as changing the geometry of fins with circular and rectangular perforations. The results of the studies showed the possibility of improving the power to mass ratio for cylinder efficiency and heat transfer rate. It was shown that a large number of fins leads to an increased heat transfer rate, but it affects the overall engine efficiency due to the increase in the total engine mass. Circular perforation is a better design solution than rectangular perforated fins with the same cross-section. Circular perforation provides a lower engine cylinder mass and gives more than 4 % better heat transfer rate. The perforation size was tested using circular perforations with a diameter of 7.14 mm, 8.5 mm and 10 mm. With a 7.14 mm diameter perforation the heat transfer rate increases slightly compared to the other tested ones, while a 10 mm diameter perforation provides the best mass reduction.

Effect of thermomechanical processing on mechanical properties and the microstructure of binary Al-Ce alloy

A new generation of Al alloys based on the Al-Ce system, with microstructure and properties weakly sensitive to elevated temperatures, is being developed for applications such as thermal engine blocks and supersonic aircraft fuselage components. Cast Al-Ce binary alloys with 4, 10 and 16 wt% Ce were produced by slab casting and further processed by extrusion. Extrusion produces a highly refined microstructure resulting in a twofold increase in strength (tensile strength of 228 MPa at room temperature). We show that the Al-10Ce is highly stable (more than 99 % strength retention after 300 °C 100 h exposure), far superior to high temperature grade Al alloy AA2618. The extruded Al-10Ce also shows a room temperature ductility of more than 20 %, with a fracture toughness of 55.42 kJ/m2 that does not degrade after high temperature exposure (97.6 % toughness retention after 300 °C 100h exposure). Results from testing at different temperatures are also reported.

Overview on aluminium alloys as sinks for end-of-life vehicle scrap

Abstract A fundamental principle in metallurgy is that the higher the purity of metals and alloys, the more favourable their properties will be. However, as the recycling of materials in production becomes increasingly significant, the levels of impurities are also on the rise. In the case of aluminium, the consequences can be detrimental due to the low solubility of most elements in this metal, which leads to the formation of brittle intermetallic phases (IMPs). Moreover, once impurities have entered aluminium, it is difficult to remove them. In 2017, almost 100 million cars were produced worldwide. Historically, vehicle design prioritised performance, resulting in a multi-material mix to utilise the best materials for each application. This included over 40 different wrought and cast aluminium alloys, Cu-based materials for electrics, and steels for high-strength applications. In the recycling of end-of-life vehicles (ELVs), high purity wrought Al alloys are today down-cycled to low purity cast engine blocks. However, recent advancements show that the drawback of increase IMP-fractions can be turned into benefits through the strategic design of heterostructured alloys. A first successful alloy example from this approach enables interesting forming properties, previously only found in 5xxx series wrought aluminium alloys, in combination with a matrix composition and age-hardening potential known from 6xxx series wrought aluminium alloys. A second examples reviews compositions directly resulting from ELV scrap. By manipulating IMPs it is feasible to create heterostructures with an interesting balance of strength and ductility. These approaches challenge traditional views, allowing for a greater volume fraction of intermetallic phases. Understanding the formation and role of intermetallic particles is crucial. This work gives an overview to the current problem and the state of the art and addressed the potential of upcycled aluminium alloys that tolerate high impurity levels by using intermetallic phases as impurity sinks.

Microstructure Characteristics and Mechanical Properties of Grey Cast Iron at Varied Ferrosilicon Addition

Inoculation is an essential metallurgical route for controlling solidification conditions of cast iron, consequently, in this study, the influence of varied percent ferrosilicon (FeSi) addition on microstructure and mechanical properties of grey cast iron (GCI) was investigated. A 50Kg capacity rotary furnace was used to melt the charge (Auto engine block scrap, graphite, ferrosilicon (FeSi) and limestone). The casting was produced in a greensand mold with wooden rectangular pattern of length 50 mm and breadth 30 mm. Chemical compositions and carbon equivalent values (CEVs) of the samples were determined, using Optical emission spectrometry (AR 4 metal analyzer) and the expression respectively. Microstructures of the samples were obtained, using metallurgical microscope (model number NJF-120A). The tensile and hardness properties were measured, using Universal tensile tester and Rockwell hardness tester respectively. From the results, C and Si were the major elements. Other trace elements were Mn, P, S and Al. CEVs of both the control and inoculated samples were less than 4.3%. Microstructure of the control sample was comprised of primary dendrites and graphite flakes, while those of the inoculated samples were characterized by varied amount of more developed primary dendrites, longer graphite flakes and austenite dendrites. Also, a number of small MnS particles were observed in relative amount within the microstructures. Tensile and hardness properties of the FeSi inoculated samples were superior to the control sample. Highest tensile strength and hardness values of 76.62 MPa and 99.89 HRB respectively were obtained at the optimum inoculation of 1.5 wt. % FeSi.

Control of combustion phasing using accelerometer-based non-intrusive sensing

Abstract Measuring the combustion phasing of an engine using in-cylinder pressure sensors is well established. However, pressure sensors need to be directly exposed to the in-cylinder environment, requiring changes to the cylinder head. Several methods have been proposed for sensing combustion phasing non-intrusively by mounting an accelerometer on the engine block. This paper presents real-time control of combustion phasing in a compression-ignition engine using non-intrusive accelerometer-based sensing during a dynamic fuel switch. A systematic data-driven control framework capable of handling fuel switching in real time is used. The control is designed based on the pressure data, and the real-time implementation is performed using the accelerometer signal. Results from combustion phasing tracking experiments performed on a compression-ignition engine are presented.

Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction

Reliable and accurate traffic flow prediction is crucial for the construction and operation of smart highways, supporting scientific traffic management and planning. However, accurately predicting spatiotemporal traffic flow in non-stationary and unprecedented traffic patterns scenarios, such as holidays and adverse weather conditions, remains a challenging task. Considering that (1) Koopman theory effectively captures the underlying time-variant dynamics of the non-stationary temporal sequence (2) Graph convolutional network (GCN) effectively extracts complex spatial dependencies, combining the strengths of both is a promising solution. Therefore, this paper proposes a spatiotemporal prediction network that integrates Koopman theory and GCN, named KoopGCN, for predicting non-stationary and inexperienced highway traffic flow. KoopGCN decomposes the input into time-invariant and time-variant components based on Fast Fourier Transform. The dual engine block consisting of KoopGCN InvarEngine and KoopGCN VarEngine is designed to predict two types of components separately. And the dual engine block also passes the residual to the next block for modeling. The experiment is conducted on real monitored highway data in Ningde City, Fujian Province, China. The results indicate that even if there is a significant distribution difference between the training and testing sets, KoopGCN can achieve accurate prediction, significantly outperforms state-of-the-art baselines.

Detection of shallow underground targets using electrical resistivity tomography and the implications in civil/environmental engineering

Applying the electrical resistivity tomography (ERT) technique in detecting very near-surface targets is quite challenging in geophysical investigation, especially in civil and environmental engineering for adequate planning and designing of structural foundations, contributing to the overall safety and efficiency of construction projects. However, locating the exact position and depth of underground targets such as faults, underground utilities, and contaminants is more challenging. Therefore, this study is aimed at examining the geophysical response of various buried targets and evaluating the ability of ERT to detect buried targets in terms of locations and depths of occurrence in the context of engineering investigation and environmental studies. A laboratory test was conducted on the targets to determine their electrical conductivity and resistivity before burial. The two-dimensional (2D) ERT survey was performed on thirteen targets buried at the site using both Wenner and dipole–dipole (DD) arrays. Both arrays captured the metallic targets with a low electrical resistivity contrast (< 0.1 Ωm) corresponding to the laboratory results. In comparison, the positions of the non-metallic buried targets were found to have a high resistivity contrast greater than 3000 Ωm, matching the laboratory results. The modelled pipes and the car engine block captured by both DD and Wenner arrays on 1.0 m electrode spacing were relatively smeared and poorly resolved in shapes, sizes and geometries, while some were not captured. The electrode spacing of 0.25 m and 0.50 m was explored on undetected targets, which provide a better resolution with sizes and depths compared to 1.0 m spacing but did not produce satisfactory results in some cases. The success and failure of ERT to detect a few targets were discussed alongside the environmental and engineering implications. The effectiveness of both arrays was assessed by their sensitivity in mapping the change in subsurface resistivity values. The DD array shows sensitivity to horizontal variations in resistivity values with low signal. In contrast, the Wenner array shows a good signal strength with a good change in the horizontal and vertical resistivity values. In addition, both arrays show capacity in mapping the geophysical signature of the buried targets and subsurface structures, which has significant application in engineering and environmental investigations.

Multifunctional nanocomposite coating for aluminium alloy: Corrosion resistance, flame retardancy, and mechanical enhancement for automotive components

Aluminium alloys are utilized in the automotive industry for components such as engine blocks, wheels, chassis, and body panels. Their lightweight nature helps reduce vehicle weight, leading to improved fuel efficiency and lower emissions. The development of nanocomposites incorporating impermeable two-dimensional materials holds significant promise for safeguarding metals against corrosion. By introducing N-aminoethyl-3-aminopropyltrimethoxysilane (AAMS) functionalized molybdenum nitride (MoN) into a coating matrix, the barrier effect can be significantly enhanced owing to its remarkable chemical and thermal stability. This enhancement is further amplified through the incorporation of functionalized MoN into graphitic carbon nitride (GCN) within a polyurethane (PU) matrix, resulting in improved corrosion protection and fire-retardant properties. Through electrochemical techniques conducted in marine environments, the protective efficacy of aluminium coated with polyurethane, alongside varying concentrations of functionalized MoN/GCN, was assessed. The resulting PU composite exhibited superior flame-retardant capabilities, manifesting in substantial reductions in peak heat release rate (PHRR), total heat release (THR), and total smoke production (TSP) compared to pure PU formulations. Electrochemical impedance spectroscopy (EIS) measurements revealed a notable enhancement in coating resistance (5.65 × 1011 Ω. cm2), even following 500 h of exposure to the electrolyte. Furthermore, the newly formulated PU composite, featuring functionalized MoN/GCN, demonstrated exceptional water repellency with a water contact angle (WCA) of 157°. This remarkable hydrophobicity underscores its potential for applications requiring protection against moisture ingress. Additionally, the PU composite exhibited commendable mechanical properties, boasting an adhesive strength of 19.1 MPa within the PU substrate. This enhanced adhesive strength ensures the coating's integrity even after prolonged immersion periods. Given its multifaceted benefits, the PU composite incorporating functionalized MoN/GCN emerges as a promising candidate for integration into automotive industries as a viable coating component. Its ability to concurrently provide corrosion protection, flame retardancy, water repellency, and mechanical robustness underscores its potential to enhance the durability and longevity of automotive components, thereby contributing to improved performance and longevity in harsh operating environments.

ИССЛЕДОВАНИЕ ТЕПЛООБМЕНА АГРЕГАТОВ ТРАНСМИССИИ И ДВИГАТЕЛЯ ГРУЗОВОГО АВТОМОБИЛЯ

A significant share of goods in agriculture are transported by road at low ambient temperatures. Changing the properties of oils in these conditions significantly reduces the efficiency of the machines and requires additional measures for the thermal preparation of the units. (Research purpose) The research purpose is identifying heat flows between the engine and the transmission units of a truck and their effect on the thermal mode of the middle and rear axles. (Materials and methods) We performed experimental studies on a KamAZ 65115 car. Convective heat flows and heat transfer through thermal conductivity in the "engine-transmission units" system, as well as their effect on the thermal mode of transmission gearboxes, were studied. (Results and discussion) The effect of the engine on the thermal mode of the gearbox was established: the oil temperature rise was five degrees; the oil temperature stabilization time was 70-74 minutes. The effect of the engine on the thermal mode of the middle and rear axles was not revealed. It was shown that the main heat flows causing the gearbox to heat up are the air flows washing the block and crankcase of the engine. An increase in the air flow velocity to 25 meters per second leads to a decrease in temperature near the engine block by 2.5 percent to 278 kelvin, and near the crankcase by 3 percent to 253 kelvin. (Conclusions) Engine temperature has a significant effect only on the temperature of the gearbox. At air speeds above 10 meters per second, stabilization of transmission temperature is observed. Heat flows that cause additional heating of the gearbox are formed on the side surfaces of the cylinder block and the engine oil sump.

Comparison of the Carbon Footprint in the Manufacturing of Cast Iron and Aluminum Engine Blocks

Comparison of the Carbon Footprint in the Manufacturing of Cast Iron and Aluminum Engine Blocks

Modeling and Thermal Analysis of Engine Block

In the realm of internal combustion engines, the engine block plays a pivotal role, where the combustion of the air-fuel mixture occurs. Due to this combustion process, high heat is released and transferred to the walls of the engine block. If this heat is not dissipated properly to the outside, it will affect the engine's performance. Hence the heat should be removed from the engine block. The heat removal rate from the engine block mainly depends on the material's thermal conductivity. Hence, this study endeavors to conduct thermal analysis on an engine block composed of various materials using ANSYS Workbench software, and modeling was done in Creo Parametric. The findings are meticulously analyzed to identify the material offering superior heat transfer performance. Index Terms: Engine block, modeling, thermal analysis, Creo Parametric, and Ansys Workbench.

Zr as an Alternative Grain Refiner in the Novel AlSi5Cu2Mg Alloy

Al-Si-Cu-Mg alloys are among the most significant types of aluminum alloys, accounting for 85–90% of all castings used in the automotive sector. These alloys are used, for example, in the manufacturing of engine blocks and cylinder heads due to their excellent specific strength (ratio of strength to specific weight) and superior castability and thermal conductivity. This study investigated the effect of using Zr as an alternative grain refiner in the novel AlSi5Cu2Mg cylinder head alloy. The microstructure of this alloy could not be refined via common Al-Ti-B grain refiners due to its specifically designed chemical composition, which limits the maximum Ti content to 0.03 wt.%. The results showed that the addition of Zr via the AlZr20 master alloy led to a gradual increase in the solidus temperature and to the grain refinement of the microstructure with the addition of as little as 0.05 wt.% Zr. The addition of more Zr (0.10, 0.15, and 0.20 wt.%) led to a gradual grain refinement effect for the alloy. The presence of Zr in the AlSi5Cu2Mg alloy was reflected in the formation of Zr-rich intermetallic phases with acicular morphology. Such phases acted as potent nucleants for the α-Al grain.

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

Coupling analysis of cylinder block for two-stroke aviation piston engine

The aluminum alloy block is a component of aircraft piston engine, and it is prone to fatigue cracks when working in a thermal mechanical coupling state for a long time. Establish a GT-POWER simulation model for a certain type of engine, verify the accuracy of the model, obtain boundary parameters such as temperature and pressure of the engine block under harsh operating conditions through the model, and divide the cylinder wall into gradients based on the engine operating conditions to obtain the surface heat transfer coefficient of the block, and then obtain the temperature field distribution of the engine body. The coupling analysis of the cylinder burst pressure and temperature field of the engine block under harsh working conditions showed that the maximum stress of the engine block was 292.55 MPa and the maximum deformation was 0.39 mm, with thermal load being the main factor causing deformation. Conduct a complete engine bench test, and under the 1000 h bench durability test, there are no cracks on the engine block, indicating that the design and analysis meet the requirements.

E-Nose for Piston Ring and Cylinder Block Condition Detection of Motorcycle Engine Based on MyRIO LabVIEW Programming

This study has created a system capable of identifying the condition of the piston ring and cylinder block of a 4-stroke motorcycle engine using petrol or similar through exhaust emissions. Multisensory gas, sensitive to changes in CO, CO2, NOx, and HC gas elements and compounds, is installed as an input to the exhaust channel and integrated using LabVIEW programming on the NI myRIO module. Multisensory data is processed using the FFT and the backpropagation method to classify whether the piston rings and engine cylinder block are in good or damaged condition. Tests have been carried out on motorbikes with piston rings and engine cylinder blocks that are in good, damaged, or unknown condition. During the test, the target error value for motorcycles with piston rings and engine cylinder blocks in good or damaged condition is less than 1%. The system can distinguish the condition of the piston ring and cylinder block of a motorcycle engine that is 100% optimal and 100% damaged with an error of 0% compared to the compression test method, and the maximum error is 20% Compared to the technician's manual method. Ten motorcycles were randomly tested in unknown conditions; 50% were in good condition, and 50% were damaged. For further development, an electronic nose system can detect engine combustion conditions and damage to cylinder rings and 4-stroke motorbike engine blocks based on exhaust emissions.

A Review of Recent Advancements in Knock Detection in Spark Ignition Engines

In gasoline engines, the combustion process involves a flame’s propagation from the spark plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder content ahead of the flame, which can lead to the autoignition of the gasoline and air. The energy release from the autoignition event causes the engine cylinder to resonate, causing an unpleasant noise and eventual engine damage. This process is termed as knock. Avoiding knock has resulted in limiting the maximum engine pressures, and hence limiting the maximum efficiencies of the engine. Modern engines employ knock sensors to detect resonances, adjusting the spark plug timing to reduce pressures and temperatures, albeit at the expense of engine performance. This paper sets out to review the different signals that can be measured from an engine to detect the start of knock. These signals traditionally consist of the in-cylinder pressure, the vibrations of the engine block, and acoustic noise. This paper reviews each of these techniques, with a focus on recent advances. A number of novel methods are also presented, including identifying perturbations in the engine speed or exhaust temperature; measuring the ion charge across the spark plug leads; and using artificial intelligence to build models based on engine conditions. Each of these approaches is also reviewed and compared to the more traditional approaches. This review finds that in-cylinder pressure measurements remain as the most accurate for detecting knock in modern engines; however, their usage is limited to research settings. Meanwhile, new sensors and processing techniques for vibration measurements will more accurately detect knock in modern vehicles in the short term. Acoustic measurements and other novel approaches are showing promise in the long term.

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability.

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators' well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators' cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators' mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators' level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

Performance test of a diesel engine using transesterified soybean oil mixed with diesel fuel

The study focused on the feasibility of soybean oil to use as a biofuel. This study presented the viability of soybean oil in terms of density, flashpoint, sulfur, heating value, specific fuel consumption, and ash content. It also showed the performance result when tested on a diesel engine in terms of fuel consumption, revolution per minute, the temperature at the exhaust pipe, cylinder head, engine block, brake power, and brake thermal efficiency. Soybean oil undergoes the process of transesterification to be used as a biofuel. It was mixed with commercially available diesel fuel upon testing in the diesel engine. The mixture contained 90% diesel fuel and 10% transesterified soybean oil. After testing, the performance of the diesel engine was measured. Transesterified soybean oil was feasible to use as a fuel according to the testing conducted. However, some improvements must have been made to increase the brake thermal efficiency. Evaluating different mixtures for diesel fuel and transesterified soybean oil must also be considered to get more efficient data. It is recommended to include emission testing for future studies.

Health monitoring of in-cylinder sensors and fuel injectors using an external accelerometer

This paper focuses on the development of a methodology to monitor the health of an engine by detecting any failures in the fuel injectors or in-cylinder pressure sensors using an accelerometer that is non-intrusively mounted on the engine block. A multi-cylinder engine with each cylinder having its own pressure sensor and injector is considered. First, a model relating the combustion component of the measured acceleration signal to the combustion component of in-cylinder pressure is proposed. Then, gains of the model are tuned to reduce the cycle-to-cycle estimation error by analyzing cycle-to-cycle variations with respect to the combustion pressure peak and engine vibration peak. Using the developed model, cylinder combustion pressures are estimated from engine vibration signals with small cycle-to-cycle estimation errors. Subsequently, a health monitoring system that can detect faults in pressure sensors, fuel injectors, and the accelerometers is proposed based on residues obtained from the difference between estimated combustion pressure and measured pressure signals. The source of the failed component can be identified uniquely by analyzing the pattern of residues. The proposed combustion pressure estimation algorithms are validated by extensive evaluation with experimental data obtained by operating a four-cylinder compression-ignition direct-injection engine with a range of experimental data. Finally, the developed health monitoring system is evaluated with various failure scenarios involving faults in the in-cylinder pressure sensor, fuel injector, and accelerometer.

Promoting sustainability in 3D printed sand casting through adaptive sand mold structures

The use of 3D printed sand molds offers a high degree of flexibility in structural design and satisfying customer requirements. The optimization and adjustment of the structure of sand mold module with the required functionality helps to maximize the application potential of binder jet 3D printing technology. In this study, an adaptive sand mold structure design method was proposed to meet the sustainable requirements of 3D printed sand casting. Firstly, the functional modular and module clustering of the sand mold is carried out and then each module is adaptive structure design, to reduce the carbon emission and cost of the 3D printed casting process. Therefore, taking the small casting furnace cover and medium-sized cast engine block as examples, the life cycle framework of the 3D printed sand casting process is established, and the sustainable advantages of the adaptive structure method are explored through the calculation and analysis of carbon emission and cost models. The results show that the adaptive structure method reduces the carbon emissions of the two castings by 9.2% and 11.9%, respectively, and the cost of two castings by 6.7% and 11.1%, respectively. This approach provides more sustainable design ideas for 3D printed sand casting and 3D printed sand mass production.