Automobile Crankshaft Research Articles

Finite element simulation of automobile crankshaft processing based on ductile iron

Crankshaft plays a very important role as the key parts in the automobile engine. The processing of shaft parts mainly adopts the machining method of turning. The service life of the machining tool directly affects the production cost. In the machining process of shaft parts, due to its poor rigidity, it is easy to cause machining deformation, so it is of great significance to control the machining deformation of parts. In this paper, the Advantedge simulation software is used to establish the finite element model of tool wear and cutting process with nodular cast iron crankshaft parts as the research object, and the tool wear and machining deformation of shaft parts are studied respectively.

Crack initiation detection in crankshaft ductile cast iron based on information entropy of acoustic emission signals under tensile loading

This study investigated the use of acoustic emission (AE) signals for the crack initiation detection in a ductile cast iron, EN-GJS700-2. Uniform uniaxial tensile tests were performed on specimens, extracted from the web of an automobile crankshaft. Experiments were done under displacement-controlled conditions, as instructed in ISO-6892. The crack initiation detection was performed by two methods: the analysis of AE features and the analysis of the AE information entropy. The fracture surface of specimens was also examined and analyzed by the scanning electron microscopy (SEM). The results showed that while the crack initiation could be detected by analyzing AE features, this approach involved some uncertainties. However, the logarithmic cumulative Shannon entropy and the logarithmic cumulative Kullback-Leibler relative entropy curves could be used for the sufficiently accurate crack initiation detection without this problem. Therefore, the analysis of the AE information entropy could serve as a highly practical approach for real-time condition monitoring.

An Optimal Model for Quality Inspection Station Setting

The quality of products depends on the process of product processing, so controlling the quality of products during processing is an important measure to ensure the final quality of products. The article aims at the problem of setting up inspection stations in the manufacturing process correctly. Through the analysis of the total cost and total processing time, taking the minimum total production cost and total processing time as the objective function, the optimization setting model of quality inspection station is established, and the particle swarm optimization algorithm is used to solve the model to determine the location of the inspection station in the product processing process and quantity. The effectiveness of the model is verified through the production process of automobile crankshaft as an example.

Finite Element Analyses of Automobile Crankshaft Using ANSYS

Finite Element Method (FEM) of failure analysis was developed on automobile crankshaft, to determine the stress distribution and the fatigue life, by using ANSYS software. Further, an analytical analysis is applied, Measure the crankshaft stress life. A study was performed on some of the Honda CR-V engine components, specifically are crankshaft, the connecting rod, and the piston. Upon the finite element analysis, it was found that the fillet areas of the crankshaft are the most critical locations where high stresses were generated in these areas. Moreover, with/without considering torsional force acting on the crankshaft does not appear to have any major effects on the stress experience by the crankshaft. In addition, the location where the crack initiated, and fatigue failure starts is located at one of the crankpin journal fillet areas. Indeed, the crankshaft critical areas are mostly affected by uniaxial stress. Moreover, the prediction of the crankshaft fatigue life by using the strain-life theory gives the overall most conservative fatigue life results.

Durability analysis using Markov chain modeling under random loading for automobile crankshaft

PurposeThe purpose of this paper is to present the durability analysis in predicting the reliability life cycle for an automobile crankshaft under random stress load using the stochastic process. Due to the limitations associated with the actual loading history obtained from the experimental analysis or due to the sensitivity of the strain gauge, the fatigue reliability life cycle assessment has lower accuracy and efficiency for fatigue life prediction.Design/methodology/approachThe proposed Markov process embeds the actual maximum and minimum stresses by a continuous updating process for stress load history data. This is to reduce the large credible intervals and missing loading points used for fatigue life prediction. With the reduction and missing loading intervals, the accuracy of fatigue life prediction for the crankshaft was validated using the statistical correlation properties.FindingsIt was observed that fatigue reliability corresponded well by reporting the accuracy of 95–98 per cent with a mean squared error of 1.5–3 per cent for durability and mean cycle to failure. Hence, the proposed fatigue reliability assessment provides an accurate, efficient, fast and cost-effective durability analysis in contrast to costly and lengthy experimental techniques.Research limitations/implicationsAn important implication of this study is durability-based life cycle assessment by developing the reliability and hazard rate index under random stress loading using the stochastic technique in modeling for improving the sensitivity of the strain gauge.Practical implicationsThe durability analysis is one of the fundamental attributes for the safe operation of any component, especially in the automotive industry. Focusing on safety, structural health monitoring aims at the quantification of the probability of failure under mixed mode loading. In practice, diverse types of protective barriers are placed as safeguards from the hazard posed by the system operation.Social implicationsDurability analysis has the ability to deal with the longevity and dependability of parts, products and systems in any industry. More poignantly, it is about controlling risk whereby engineering incorporates a wide variety of analytical techniques designed to help engineers understand the failure modes and patterns of these parts, products and systems. This would enable the automotive industry to improve design and increase the life cycle with the durability assessment field focussing on product reliability and sustainability assurance.Originality/valueThe accuracy of the simulated fatigue life was statistically correlated with a 95 per cent boundary condition towards the actual fatigue through the validation process using finite element analysis. Furthermore, the embedded Markov process has high accuracy in generating synthetic load history for the fatigue life cycle assessment. More importantly, the fatigue reliability life cycle assessment can be performed with high accuracy and efficiency in assessing the integrity of the component regarding structural integrity.

Computer Aided System for Crankshafts Failure Rate of Automobile Based on Distance Travel and Age

The crankshaft is the part of an engine which translates reciprocating linear piston motion into rotation. Automobile crankshaft failure, apart from poor maintenance culture, is caused by age and distance covered. These attributes which caused crankshaft failure were investigated using questionnaire administration and oral interviews of the maintenance expert and experienced personnel in the three selected crankshaft maintenance shops in Akure, Nigeria based on six popular crankshaft brands. Information/data on failure rate and failure consequence thresholds were established and served as a decision rule on the basis at which optimal reconditioning process, replacement and interchangeability system of the crankshaft components were identified on the occurrence of failure rate (R1) and failure consequence (β1). Computer algorithm was developed for the software package, using Microsoft Visual C# computer language. The software was tested to determine their level of performance. The results generated from the application of these systems were categorised into six scenarios whereby the MC230 Class gave the best result. The software will be veritable tools in predicting crankshaft failure in automobile, manufacturing, and machine tools industries, for prompt solution.

The needs of understanding stochastic fatigue failure for the automobile crankshaft: A review

This paper reviews the fatigue failure mechanisms for the automobile crankshaft under service loading through the stochastic point of view. Fatigue failure of crankshafts are reviewed in general, as it is a major concern due to the uncertainties that arise i.e. randomness in structural materials, the geometric shape of the component and randomness of service loads. There has been very little research carried out in assessing the fatigue failure using the stochastic process in predicting the fatigue life of crankshafts. This review paper discusses the durability aspects of the component and is followed by a review of the characteristics of loading and the stochastic fatigue failure effect on the components. In addition, the stochastic approach from empirical model aspect using a safe-life approach from the more recent advances in computational methods to assess stochastic fatigue failure was discussed and reviewed in the context of this paper. The integration between the empirical and probabilistic methods can be quantified using statistical models, which evaluate the damage that leads to fatigue and eventually fatigue failure. Hence, this review provides a platform for understanding the stochastic fatigue failure for an accurate predictive prediction on the structural integrity of components, especially in the automobile industry.

Design and Construction of an Improved Balancing Machine

The balancing machine is equipment used for the following- determining the proper location and amount of balance work in a rotor (applicable in a motor amerture) – determining the position and magnitude of a given mass to balance the unbalance centrifugal effect of a system (this is applicable in determining automobile crankshaft mass distribution. This equipment was designed and constructed for the use of the Faculty Engineering, Lagos State University the Mechanics of Machine Laboratory since the possibility of acquiring one is remote due to the non-availability of the hard currency needed. Moreover the need to take students outside the university to use the equipment has totally been eliminated thereby saving valuable time and cost. The objective of looking inwards towards technology acquisition is also manifested.

Microstructure and mechanical properties of hot forging die manufactured by bimetal-layer surfacing technology

The temperature field and thermal cycling characteristics of a cast-steel matrix die of automobile crankshaft were predicted. The hot forging die was divided into three temperature regions, i.e., surfacing temperature fluctuation region (0–3mm in thickness), near surfacing temperature gradient region (3–20mm in thickness) and matrix temperature balanced region (above 20mm in thickness), and their temperatures were distributed in high, medium and low-tempered temperature zones respectively. The influences of temperature distribution on the microstructure and mechanical properties of the forging die before and after service were studied. The tempered martensite of strengthened layer decomposed and the coarse grain appeared after service. The protruding part of the ribbed slab was easy to propagate fatigue crack, leading to significant decreasing of tensile strength and impact properties. The tempered martensite and lower banite increased in transition layer, the mechanical properties under high temperatures decreased obviously. The strengthening of hardened structure in weld zone was reduced and the coarse grain structure disappeared, this enabled the performance of the weld zone was more stable than the cast-steel matrix layer and the transition layer.

Discretized Markov chain in damage assessment using Rainflow cycle with effects of mean stress on an automobile crankshaft

We studied the effect of mean stress correction factor using the Rainflow counting technique to assess the fatigue damage of an automobile crankshaft under service loading by considering the stochastic process of the Markov chain. The failure of the crankshaft will cause serious damage to the engine and also to other connecting subcomponents. The service loading is computationally generated from the Discrete Markov chain model and the fatigue cycle is counted using the Rainflow counting technique with the consideration of the local minima and maxima load. To quantify the fatigue damage, the strain-life curve using the fatigue mean stresses was used to model the fatigue failure of the material used in for the crankshaft at Nf = 106. The fatigue mean stresses were used to estimate the effects of the mean stress on the fatigue strength of the component under service loading condition. Statistical verification with the boundary condition of the 90% confidence level was performed to observe the difference between the stochastic algorithms when compared towards the fatigue life behavior of the ductile cast iron material. We concluded that for the practical application, the proposed stochastic model provides a highly accurate assessment of fatigue damage prediction for improving the safety and controlling the risk factors in terms of structural health monitoring.

RELIABILITY ASSESSMENT FOR AN AUTOMOBILE CRANKSHAFT UNDER RANDOM LOADING

This paper presents the stochastic process for reliability assessment based on the fatigue life data under random loading for structural health monitoring of an automobile crankshaft due tofatigue failure. This is based on reported failure of the component due to the effect of the random loads that acts on the component during its operating condition over a given period of time. Since there are significant limitations of the experimental analysis in terms of actual loading history, therefore, the reliability assessment is considered to be less accurate. Hence, the reliability assessment based on fatigue life data using the Markov process by incorporating loading data to synthetically generate loading history has been proposed in this study. The Markov process has the capability of continuously updating the loading history data to reduce the intervals between each data point for reliability assessment based on the fatigue life data. The accuracy of the proposed monitoring system for reliability assessment was validated through its statistical method. The reliability assessment from the Markov process corresponded well by providing an accuracy of more than 95% when compared towards the actual sampling data. The reliability of the crankshaft based on the fatigue life assessment provides a highly accurate for the improvement and control of risk factors in terms of structural health monitoring by overcoming the extensive time and cost required for fatigue testing

Research on Automobile Crankshaft Roundness and Cylindricity Errors Evaluation Mathematical Model Based on the Error Conversion

Research on Automobile Crankshaft Roundness and Cylindricity Errors Evaluation Mathematical Model Based on the Error Conversion

English

This paper emphasizes on analysing and predicting the reliability of an automobile crankshaft by analysing the time to failure (TTF) through the parametric distribution function. In this paper, the TTF was modelled to predict the likelihood of failure for crankshaft during its operational condition over a given time interval through the development of the stochastic algorithm. The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. T, the algorithm has the capability to statistically validate the algorithm to obtain the optimal parametric model to represent the failure of the component against the actual time to failure data from the local automobile industry. Hence, the validated results showed that the three parameter Weibull distribution provided an accurate and efficient foundation in modelling the reliability rate when compared with the actual sampling data. The suggested parametric distribution function can be used to improve the design and the life cycle due to its capability in accelerating and decelerating the mechanism of failure based on time without adjusting the level of stress. Therefore, an understanding of the parametric distribution posed by the reliability and hazard rate onto the component can be used to improve the design and increase the life cycle based on the dependability of the component over a given period of time. The proposed reliability assessment through the developed stochastic algorithm provides an accurate, efficient, fast and cost effective reliability analysis in contrast to costly and lengthy experimental techniques.

Investigation of Sensitivity Effect Based on Mass and Stiffness Modification in Automobile Crankshaft

A modeling through sensitivity analysis is one of the promising methods to investigate the dynamic characteristics of complex mechanical parts. This study aimed to investigate the effect of sensitivity based on mass and stiffness modification in automobile crankshaft as a function of natural frequency. Verification for the crankshaft model that is used in the experiment and simulation was done and both results showed good agreement and small errors percentage. The modification was also done by reducing the different percentage of crankshaft’s mass and stiffness. Partial differential analysis was used in the sensitivity analysis in order to figure out the natural frequency after every set of modification. According to the results, we also found that there were changes of sensitivity value by changes in mass value but the stiffness value remains unchanged. However, there is no significant effect of stiffness reduction on vibration was found in this research.

Markov chain modelling of reliability analysis and prediction under mixed mode loading

The reliability assessment for an automobile crankshaft provides an important understanding in dealing with the design life of the component in order to eliminate or reduce the likelihood of failure and safety risks. The failures of the crankshafts are considered as a catastrophic failure that leads towards a severe failure of the engine block and its other connecting subcomponents. The reliability of an automotive crankshaft under mixed mode loading using the Markov Chain Model is studied. The Markov Chain is modelled by using a two-state condition to represent the bending and torsion loads that would occur on the crankshaft. The automotive crankshaft represents a good case study of a component under mixed mode loading due to the rotating bending and torsion stresses. An estimation of the Weibull shape parameter is used to obtain the probability density function, cumulative distribution function, hazard and reliability rate functions, the bathtub curve and the mean time to failure. The various properties of the shape parameter is used to model the failure characteristic through the bathtub curve is shown. Likewise, an understanding of the patterns posed by the hazard rate onto the component can be used to improve the design and increase the life cycle based on the reliability and dependability of the component. The proposed reliability assessment provides an accurate, efficient, fast and cost effective reliability analysis in contrast to costly and lengthy experimental techniques.

FOA Based Diagnosis Model for Multivariate Production Process

Fault diagnosis for quality control during the multivariate production process is widely used to detect abnormal fluctuations, find out failure reasons and take measures to maintain the stability of the production system accordingly. The neural network methodology has become a main method in the field of intelligent diagnosis recently. However, it has certain deficiencies such as longer training time, slower convergence rate and easier falling into a local optimal solution easily. As a result, the effect of fault diagnose is influenced. Thus, this article proposes the idea of using the Fruit Fly Optimization Algorithm in the multivariable process fault diagnosis model, at the same time, to analyze the out of control sample data in the automobile crankshaft production. Compared with the neural network model in dealing with the fault diagnosis in multivariate process, Fruit Fly Optimization Algorithm’s effectiveness s verified.

RESIDUAL STRESS IN THE WHEEL OF 42CrMo STEEL DURING QUENCHING

42CrMo steel,a typical low alloy medium carbon structural steel,is widely used in important structural components that require high strength,plasticity and toughness,such as crane weight-on-wheel,automobile crank shaft,locomotive gear hub,oil drill pipe joints of deep well,fishing tools and so on,for its good harden ability,high temperature strength,good creep resistance and little quenching deformation.The wheels of the polar crane that used in the Chinese third generation nuclear power plant are made of steel 42CrMo.However,cracks and surface peeling normally occur after heat treatment at quenching process of wheel forgings.There is important application background and practical significance to research the effect of the heat treatment process on the microstructures and mechanical properties and the influence of porosity defects inside the forging wheel on the heat treatment process.Large numbers of research work had been focused on segregation and heat treatment process for solving this matter in passing days.This work aims to study the effects of thermal residual stress on porosity defect in a wheel,and explain the reason for cracking and surface feeling in the way of mechanical behavior during quenching.A wheel with surface peeling was analyzed by measuring chemical compositions,macro- and micro-crack observation.Random testing position for mechanical compositions showed that the effects of segregation was small in the wheel.A set of tests and measurements for thermal mechanical properties of 42CrMo steel were conducted from room temperature to 850 ℃.FEM models containing porosity defects in different sizes and without defects were constructed by Abaqus for studying the residual stresses during quenching in fully coupled temperature-displacement analysis.Simulation results indicate that the porosity defects in wheel cause stress concentration within themselves.The maximum residual stress is not affected by the length of porosity region.The hoop residual stress in porosity region in the wheel due to quenching process is in the highest level and believed to be the driving force of cracks.According to the stress distribution in the wheel,the cracks caused by the hoop residual stress can not propagate out of the defects region too far.While the assumptions of surface peeling of wheel are concluded due to combined influence of the residual stress and external loads when the defects region emerges near the wheel surface border in view of the current simulation.

Precipitation behavior of BN type inclusions in 42CrMo steel

Automobile crankshaft steel 42CrMo, which requires excellent machinability and mechanical properties, cannot be manufactured by traditional methods. To achieve these qualities, the formation behavior of boron nitride (BN) inclusions in 42CrMo steel was studied in this article. First, the precipitation temperature and the amount of BN type inclusions with different contents of boron and nitrogen in molten steel were calculated thermodynamically by FactSage software. Then the morphology and the size of BN type inclusions as well as the influence of cooling methods on them were investigated by scanning electron microscopy. Furthermore, the effects of cooling rate and the contents of B and N in molten steel on the morphology, size, and distribution of BN type inclusions were studied quantitatively and detailedly by directional solidification experiments. It is found that different BN inclusions in molten steel can form by controlling the cooling rate and the contents of B and N, which is important for obtaining the excellent machinability of 42CrMo steel.

Investigation on Surface Crack of C38N2 Automobile Engine Crankshaft

Defect in surface induction hardening layer at the eccentric axle neck of C38N2 automobile engine crankshaft was investigated. Fracture morphology and microstructure of the defect were analyzed by optical microscope and scanning electron microscope, and compared with the microstructure characteristics in defect-free locations. And the reasons and the technical procedures of the defects were discussed. The results indicated that the surface defect is a quenching crack caused by induction hardening process. The crack propagation is along prior-austenite grain boundaries and is affected by MnS inclusions. Martensite lath in surface hardening layer at position having 45° or 90° with mouldjoint is shorter than that at the defect position. Distribution characteristics of inclusions at different positions have significant difference. Inclusions in the defect position are slender spindle and are distributed nearly vertical the crankshaft surface. Distribution of the inclusions in the 45° position is approximately parallel to the crankshaft surface. And the inclusions in the 90° position are mostly graininess. Distributional difference of the inclusions at the different positions is related to uneven flowing of metal during stamp forging process.

Estimation of influence of medium stresses on fatigue resistance using scale effect : Domozhirov, L.I. Industrial Laboratory (Russia) (Dec. 1993) 53 (6), 613–619

This paper reviews the fatigue failure mechanisms for the automobile crankshaft under service loading through the stochastic point of view. Fatigue failure of crankshafts are reviewed in general, as it is a major concern due to the uncertainties that arise i.e. randomness in structural materials, the geometric shape of the component and randomness of service loads. There has been very little research carried out in assessing the fatigue failure using the stochastic process in predicting the fatigue life of crankshafts. This review paper discusses the durability aspects of the component and is followed by a review of the characteristics of loading and the stochastic fatigue failure effect on the components. In addition, the stochastic approach from empirical model aspect using a safe-life approach from the more recent advances in computational methods to assess stochastic fatigue failure was discussed and reviewed in the context of this paper. The integration between the empirical and probabilistic methods can be quantified using statistical models, which evaluate the damage that leads to fatigue and eventually fatigue failure. Hence, this review provides a platform for understanding the stochastic fatigue failure for an accurate predictive prediction on the structural integrity of components, especially in the automobile industry.