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A pilot model using Simulink™ of three interlinked industrial sectors leading to painted automotive bodies was constructed for the purpose of observing time based effects on an Life Cycle Analysis (LCA). Current LCA neglects time under an implicit assumption that material inventory data is steady state. In this study, process models were built which included time as a parameter in addition to LCA material inventory data. The results show that time is a critical factor in the overall material inventory. If the transient behavior due to demand or regulatory control results in an industry instability, material supplies may be interrupted or overproduced depending on the timing and strength of the control. Furthermore, potentially greater inventories of undesirable materials could occur. These effects are not currently captured by LCA Inventory Analysis procedures. However, this paper shows that use of dynamic modeling can correct this situation.

The determination of the points of contact between an S cam and brake shoe rollers cannot be determined explicitly by mathematical methods. An iteration procedure is developed which allows the points of contact to be determined for any cam rotation. A mathematical proof is given which verifies the iteration procedure for one of the two cases examined. Examples for a brake are worked out which show how the iteration procedure converges on the actual contact points.

Probabilistic studies can be prohibitively expensive because they require repeated finite element analyses of large models. Re-analysis methods have been proposed with the premise to estimate accurately the dynamic response of a structure after a baseline design has been modified, without recalculating the new response. Although these methods increase computational efficiency, they are still not efficient enough for probabilistic analysis of large-scale dynamic systems with low failure probabilities (less or equal to 10-3). This paper presents a methodology that uses deterministic and probabilistic re-analysis methods to generate sample points of the response. Subsequently, tail modeling is used to estimate the right tail of the response PDF and the probability of failure a highly reliable system. The methodology is demonstrated on probabilistic vibration analysis of a realistic vehicle FE model.

The drive of Design for the Environment is to reduce the environmental impact of both design and manufacturing processes. The most frequent method recommended is to substitute better materials and processes. However, there are processes that will continue to have undesirable environmental impacts due to the lack of knowledge of better methods. These processes are critical to manufacturing of products and can not be eliminated. All possible substitutions appear to have worse impacts. This paper explores modeling these processes and imposing a control method which permits an improvement of the environmental impact.

An approach for Probabilistic Reanalysis (PRA) of a system is presented. PRA calculates very efficiently the system reliability or the average value of an attribute of a design for many probability distributions of the input variables, by performing a single Monte Carlo simulation. In addition, PRA calculates the sensitivity derivatives of the reliability to the parameters of the probability distributions. The approach is useful for analysis problems where reliability bounds need to be calculated because the probability distribution of the input variables is uncertain or for design problems where the design variables are random. The accuracy and efficiency of PRA is demonstrated on vibration analysis of a car and on system reliability-based optimization (RBDO) of an internal combustion engine.

In this study a dynamic simulation was conducted on a forged steel crankshaft from a single cylinder four stroke engine. Finite element analysis was performed to obtain the variation of the stress magnitude at critical locations. The dynamic analysis resulted in the development of the load spectrum applied to the crankpin bearing. This load was then applied to the FE model and boundary conditions were applied according to the engine mounting conditions. Results obtained from the aforementioned analysis were then used in optimization of the forged steel crankshaft. Geometry, material, and manufacturing processes were optimized using different geometric constraints, manufacturing feasibility, and cost. The first step in the optimization process was weight reduction of the component considering dynamic loading. This required the stress range under dynamic loading not to exceed the magnitude of the stress range in the original crankshaft.

The National Science Foundation recently sponsored a Workshop on Environmentally Benign Manufacturing (EBM) for the Transportation Industries. The objective of the workshop was to determine future directions of research in the EBM area and to construct a roadmap for development of future research programs. While research in the fields of Design for the Environment (DfE) and Life Cycle Analysis (LCA) have focused on the product and product life cycles, an additional focus is needed to find and develop processes with less environmental impact within the manufacturing environment. This workshop explored EBM issues with respect to the enterprise, the products, the processes and the materials.

Fatigue behavior of two types of automotive steel, quenched and tempered SUJ2 and carburized SCM820PRH, which are applied as powertrain parts are studied. These two types of steel are different in their hardness distribution from surface to core. The hardness of quenched and tempered SUJ2 is homogenous, in contrast to that of carburized SCM820PRH (SCM) which decreases from surface to core. These steels are investigated in terms of their monotonic tensile properties and fatigue behavior. A number of predictive methods were used to describe the fatigue behavior of these steels. A simple predictive method is based on approximation of S-N curve from ultimate tensile strength. The well-known Murakami’s defect area method was also applied for the prediction of the high cycle fatigue strength.

The framework for environmentally conscious manufacturing in industry is the life cycle assessment structure developed by the Society of Environmental Toxicology and Chemistry and incorporated into ISO 14000 Environmental Management Systems. Plant managers subject to this standard have the responsibility for environmental improvement projects. Often, applying these projects creates significant risks, particularly if the project is unsuccessful or requires a new technology that has not been widely applied. Plant managers are inherently risk adverse. Thus plant managers need to know not only how a project will succeed but also what could happen if the project fails or results in a state different than intended. Based on that knowledge, plants managers prepare contingency plans. This paper illustrates a method by which the optimum plan and all possible contingency plans can be selected based upon minimizing project cost while maximizing project success to arrive at an improvement goal.

In reliability design, often, there is scarce data for constructing probabilistic models. It is particularly challenging to model uncertainty in variables when the type of their probability distribution is unknown. Moreover, it is expensive to estimate the upper and lower bounds of the reliability of a system involving such variables. A method for modeling uncertainty by using Polynomial Chaos Expansion is presented. The method requires specifying bounds for statistical summaries such as the first four moments and credible intervals. A constrained optimization problem, in which decision variables are the coefficients of the Polynomial Chaos Expansion approximation, is formulated and solved in order to estimate the minimum and maximum values of a system’s reliability. This problem is solved efficiently by employing a probabilistic re-analysis approach to approximate the system reliability as a function of the moments of the random variables.

The crash performance of an automobile largely depends on the ability to identity impact damage, maintain the passenger safety through deployment of various safety restraint systems, and steer away the vehicle from impact. So, this work is focused on the impact response of an automobile structure so as to find the location, magnitude of impact and asses the severity of damage. The results of the developed generalized forward plate model compared within 2% for FEM and previous other theoretical approaches. The inverse model compared within 7% for location and reconstructed force. Damage severity assessment is also investigated.

Fatigue is the primary cause of failure of crankshafts in internal combustion engines. The cyclic loading conditions and the stress concentrations in the crank pin fillets are unavoidable, and can result in fatigue failure. The objectives of this study were to compare the fatigue behavior of forged steel and ductile iron crankshafts from a one-cylinder engine as well as to determine if the fatigue life of a crankshaft can be accurately estimated using fatigue life predictions. Monotonic tensile tests as well as strain-controlled fatigue tests were conducted using specimens machined from the crankshafts to obtain the monotonic and cyclic deformation behavior and fatigue properties of the two materials. The forged steel had higher tensile strength and better fatigue performance than the ductile cast iron. Charpy v-notch impact tests were also conducted using specimens machined from the crankshafts to obtain and compare the impact toughness of the materials.

A procedure for fatigue life estimation of components and structures under variable amplitude multiaxial loadings based on simple and commonly available material properties is presented. Different aspects of the analysis consisting of load cycle counting method, plasticity model, fatigue damage parameter, and cumulative damage rule are presented. The only needed material properties for the proposed procedure are hardness and monotonic and axial cyclic deformation properties (HB, K, n, K′ and n′). Rainflow cycle counting method is used for identifying number of cycles. Non-proportional cyclic hardening is estimated from monotonic and axial cyclic deformation behaviors. A critical plane approach is used to quantify fatigue damage under variable amplitude multiaxial loading, where only material hardness is used to estimate the fatigue curve, and where the needed deformation response is estimated based on Tanaka's non-proportionality parameter.

A vehicle steering knuckle undergoes time-varying loadings during its service life. Fatigue behavior is, therefore, a key consideration in its design and performance evaluation. This research program aimed to assess fatigue life and compare fatigue performance of steering knuckles made from three materials of different manufacturing processes. These include forged steel, cast aluminum, and cast iron knuckles. In light of the high volume of forged steel vehicle components, the forging process was considered as base for investigation. Monotonic and strain-controlled fatigue tests of specimens machined from the three knuckles were conducted. Static as well as baseline cyclic deformation and fatigue properties were obtained and compared. In addition, a number of load-controlled fatigue component tests were conducted for the forged steel and cast aluminum knuckles. Finite element models of the steering knuckles were also analyzed to obtain stress distributions in each component.

It is challenging to perform probabilistic analysis and design of large-scale structures because probabilistic analysis requires repeated finite element analyses of large models and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability based design optimization of large scale structures that consists of two re-analysis methods; one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. The deterministic re-analysis method can analyze efficiently large-scale finite element models consisting of tens or hundreds of thousand degrees of freedom and large numbers of design variables that vary in a wide range. The probabilistic re-analysis method calculates very efficiently the system reliability for many probability distributions of the design variables by performing a single Monte Carlo simulation.

During metal forming processes such as rolling and forging, deformable manganese sulfide (MnS) inclusions become elongated. Such elongated MnS inclusions can have considerable adverse effects on mechanical properties, if the inclusions are not aligned with the loading direction. The objectives of this study were to evaluate and compare fatigue, monotonic tensile and CVN impact behavior of SAE 4140 steel with high (0.077% S), low (0.012% S) and ultra low (0.004% S) sulfur contents at two hardness levels (40 HRC and 50 HRC). The longitudinally oriented samples at 40 HRC, where MnS inclusions were oriented along the loading direction, did not exhibit any significant sensitivity of tensile or fatigue properties to the sulfur content. For the transversely oriented MnS inclusions, however, the monotonic tensile test results indicate very low ductility of the high sulfur material at both hardness levels, where specimens failed shortly after yielding.

In this study a dynamic simulation was conducted on a crankshaft from a single cylinder four stroke engine. Finite element analysis was performed to obtain the variation of stress magnitude at critical locations. The pressure-volume diagram was used to calculate the load boundary condition in dynamic simulation model, and other simulation inputs were taken from the engine specification chart. The dynamic analysis was done analytically and was verified by simulation in ADAMS which resulted in the load spectrum applied to crank pin bearing. This load was applied to the FE model in ABAQUS, and boundary conditions were applied according to the engine mounting conditions. The analysis was done for different engine speeds and as a result critical engine speed and critical region on the crankshaft were obtained. Stress variation over the engine cycle and the effect of torsional load in the analysis were investigated.

An optimization study was performed on a steel forged connecting rod with a consideration for improvement in weight and production cost. Since the weight of the connecting rod has little influence on its total production cost, the cost and the weight were dealt with separately. Reduction in machining operations, achieved by change in material, was a significant factor in manufacturing cost reduction. Weight reduction was achieved by using an iterative procedure. Literature survey suggests cyclic loads comprised of static tensile and compressive loads are often used for design and optimization of connecting rods. However, in this study weight optimization is performed under a cyclic load comprising dynamic tensile load and static compressive load as the two extreme loads. Constraints of fatigue strength, static strength, buckling resistance and manufacturability were also imposed. The fatigue strength was the most significant factor in the optimization of the connecting rod.

This paper discusses the effects of changes in specimen geometry, stress gradient, and residual stresses on fully-reversed constant amplitude uniaxial fatigue behavior of a medium carbon steel. Axial fatigue tests were performed on both flat and round specimens, while four-point rotating bending tests were performed only on round specimens. All the tests were performed using shot peened and unpeened flat and round samples, to investigate the effects of compressive residual stresses on fatigue behavior. The specimens in the rotating bending tests experienced longer life for a given stress amplitude than in the axial test. Shot-peening was found to be beneficial in the long life region, while in short life tests the shot-peened samples experienced a shorter life than the unpeened samples under both axial and bending test conditions.

In reliability design, often, there is scarce data for constructing probabilistic models. Probabilistic models whose parameters vary in known intervals could be more suitable than Bayesian models because the former models do not require making assumptions that are not supported by the available evidence. If we use models whose parameters vary in intervals we need to calculate upper and lower bounds of the failure probability (or reliability) of a system in order to make design decisions. Monte Carlo simulation can be used for this purpose, but it is too expensive for all but very simple systems. This paper proposes an efficient Monte-Carlo simulation approach for estimation of upper and lower probabilities. This approach is based on two ideas: a) use an efficient approach for reliability reanalysis of a system, which is introduced in this paper, and b) approximate the probability distribution of the minimum and maximum failure probabilities using extreme value statistics.