Vibration and acoustic pre-assessment study for free piston engine structure

This paper presents modelling and simulation study of vibration and acoustic for a new free piston engine. The free piston engine is a new engine concept where its piston motion is not restricted by the crankshaft component. The free movement of the piston influenced by forces developed from the fuel combustion process and air compression in the engine. The piston movement has become an issue or a problem which consequently developed vibration to the engine structure because of the unbalance forces. Vibration analysis has been developed using finite element software which is MSC.PATRAN in order to determine the natural frequency and frequency response of the engine structure. Theoretical development of the engine balance motion and frequency response has also been conducted. From the simulation and finite element analysis, the force response pattern of the engine vibration can be determine and compare with its natural frequency. The vibration analysis will then be used as the input data for acoustic analysis of the engine. The acoustic analysis used boundary element method coupled with finite element method to determine the noise level produce by the engine structure. This integration determined the noise - frequency data that affected the engine structure towards the occurrence of engine noise especially when engine is in operation mode

Integration of feedforward neural network and finite element in the draw-bend springback prediction

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elasticallydriven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data

The analysis of initial probability distribution in Markov Chain model for lifetime estimation

Fatigue crack growth is a stochastic phenomenon due to the uncertainties factors such as material properties, environmental conditions and geometry of the component. These random factors give an appropriate framework for modelling and predicting a lifetime of the structure. In this paper, an approach of calculating the initial probability distribution is introduced based on the statistical distribution of initial crack length. The fatigue crack growth is modelled and predicted by a Markov Chain associated with a classical deterministic crack Paris law. It has been used in calculating the transition probabilities matrix to represent the physical meaning of fatigue crack growth problem. The equation of Paris law provides information regarding the stress intensity factor and material properties in predicting the crack growth rate. The data from the experimental work under constant amplitude loading was analyzed using the Markov Chain model. The results provide a reliable prediction and show excellent agreement between proposed model and experimental result. The reliability of the model can be an effective tool for safety analysis of structure

Assessment on surface treatment on fatigue life of cylinder block for linear engine using frequency response approach

Objectives: This study was focused on the finite element techniques to investigate the effect of surface treatment on the fatigue life of the vibrating cylinder block for new two-stroke free piston engine using random loading conditions. Motivation: An understanding of the effects related to the random loading is necessary to improve the ability of designers to accurately predict the fatigue behavior of the components in service. An internal combustion engine cylinder block is a high volume production component subjected to random loading. Problem statement: Proper optimization of this component that is critical to the engine fuel efficiency and more robustly pursued by the automotive industry in recent years. A detailed understanding of the applied loads and resulting stresses under in-service conditions is demanded. Approach: The finite element modeling and analysis were performed utilizing the computer aided design and finite element analysis codes respectively. In addition, the fatigue life prediction was carried out using finite element based fatigue analysis code. Aluminum alloys were considered as typical materials in this study. Results: The frequency response approach was applied to predict the fatigue life of cylinder block using different load histories. Based on the finite element results, it was observed that the fatigue life was significantly influenced for the nitriding treatment. The obtained results were indicated that the nitrided treatment produces longest life for all loading conditions. Conclusion: The nitriding process is one of the promising surface treatments to increase the fatigue life for aluminum alloys linear engine cylinder block

Uncertainty Factors of a Finite Element Model using the Fuzzy Analysis Method

The recent advancement in manufacturing technology in the automotive and aerospace sectors has led to the invention of advanced structured material, which is lightweight and a complex geometry model that can be manufactured. As it is related to human safety and hazards, the need for uncertainty analysis in a structure before and after a manufacturing process is a primary concern. Thus, this paper analyzes the uncertainty parameters of a meshed finite element model in the geometry, boundary condition, load, and material properties. An uncertainty analysis numerical tool, the fuzzy analysis method, is applied in Excel-VBA as the simulation platform. Each uncertainty parameter is in a range of numbers, with a maximum and minimum value as the limit. The α-cuts determine the fuzzy analysis output on the membership function. The deterministic value of the variable is implemented for comparison purposes. The simulation result for the von-Mises stress analysis has significantly impacted the uncertainty analysis as its curve has surpassed the yield strength limit of the material. The simulation output for the displacement has a more considerable uncertainty dispersion when compared to the other results. This study helps to find a better security margin of a structure for its sustainability in the future

A Surrogate Model's Decision Tree Method Evaluation for Uncertainty Quantification on a Finite Element Structure via a Fuzzy-Random Approach

A novel additive manufacturing method (AM)) constructs a three-dimensional model from a computer-aided design by adding material layer by layer. This technique produces a lightweight end product with complex geometries and has gained recognition among industrial players. Nonetheless, the mechanical properties and geometry components are the uncertainties that prevail in its structures. An alternative approach using the Finite Element Method (FEM) to analyse these uncertainties demands extensive computational effort and time consumption. Therefore, a machine learning (ML) tool using the surrogate modelling technique offers an alternative way to provide and predict simulation outcomes. This study applies two surrogate modelling approaches, the decision tree (DT) and the Gaussian process regression (GPR) methods. Output data from a FEM simulation with uncertainty elements are obtained for the training purposes of the surrogate models. Both ML methods can predict simulation results with high precision. Both approaches obtained an excellent coefficient of determination value, R2 of 0.998, and Root Mean Square Error, RMSE of 0.012, successfully reducing time consumption and computational effort. The DT method shows better robustness when compared to the GPR method. A value change in the input parameter significantly impacts the surrogate model's prediction performance. An adequate quantity of data input for the training phase of both surrogate models exhibits the FEM results with the presence of uncertainty and robustness

Assessing the Safety Behaviour of the Bus Express Driving Condition from the Passengers' Perspective

This paper presents the passengers' perspective towards express bus safety issues based on the driver's behavioural characteristics, providing an important aspect in reducing the accidents in Malaysia.  A pilot study with a respondent of 40 people, aged between 16 to 41 years old were conducted in Universiti Kebangsaan Malaysia. The developed questionnaires based on the five-point Likert Scale were implemented to assess the safety perception on express buses, and it has a higher reliability of Cronbach's Alpha score at 0.91. The findings show that more than 45 % of the respondents agreed that dangerous behaviour of express bus drivers were mainly due to the use of mobile during driving (63.33%), tailgating (57.58%) and driving above the given speed limit (48.59%). The reason of this behaviour is insufficient time to stick to running schedules and the shift pattern rotations. In terms of safety precautions, about 77% passengers preferred safety briefing using audio due to its ease in understanding the meaningful instruction. In addition, 97% passengers agreed on the needs of a second driver to ensure a safe journey to their destination. Hence, a proposed mitigated solution such as drivers monitoring is needed by the respective agencies to reduce this careless behaviour that may influence the dangerous driving behaviour