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Description
The friction condition is an important factor in controlling the compressing process in metalforming. The friction calibration maps (FCM) are widely used in estimating friction factors between the workpiece and die. However, in standard FEA, the friction condition is defined by friction coefficient factor (µ), while the FCM is used

The friction condition is an important factor in controlling the compressing process in metalforming. The friction calibration maps (FCM) are widely used in estimating friction factors between the workpiece and die. However, in standard FEA, the friction condition is defined by friction coefficient factor (µ), while the FCM is used to a constant shear friction factors (m) to describe the friction condition. The purpose of this research is to find a method to convert the m factor to u factor, so that FEA can be used to simulate ring tests with µ. The research is carried out with FEA and Design of Experiment (DOE). FEA is used to simulate the ring compression test. A 2D quarter model is adopted as geometry model. A bilinear material model is used in nonlinear FEA. After the model is established, validation tests are conducted via the influence of Poisson's ratio on the ring compression test. It is shown that the established FEA model is valid especially if the Poisson's ratio is close to 0.5 in the setting of FEA. Material folding phenomena is present in this model, and µ factors are applied at all surfaces of the ring respectively. It is also found that the reduction ratio of the ring and the slopes of the FCM can be used to describe the deformation of the ring specimen. With the baseline FEA model, some formulas between the deformation parameters, material mechanical properties and µ factors are generated through the statistical analysis to the simulating results of the ring compression test. A method to substitute the m factor with µ factors for particular material by selecting and applying the µ factor in time sequence is found based on these formulas. By converting the m factor into µ factor, the cold forging can be simulated.
ContributorsKexiang (Author) / Shah, Jami (Thesis advisor) / Davidson, Joseph (Committee member) / Trimble, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
The focus of this research is to investigate methods for material substitution for the purpose of re-engineering legacy systems that involves incomplete information about form, fit and function of replacement parts. The primary motive is to extract as much useful information about a failed legacy part as possible and use

The focus of this research is to investigate methods for material substitution for the purpose of re-engineering legacy systems that involves incomplete information about form, fit and function of replacement parts. The primary motive is to extract as much useful information about a failed legacy part as possible and use fuzzy logic rules for identifying the unknown parameter values. Machine elements can fail by any number of failure modes but the most probable failure modes based on the service condition are considered critical failure modes. Three main parameters are of key interest in identifying the critical failure mode of the part. Critical failure modes are then directly mapped to material properties. Target material property values are calculated from material property values obtained from the originally used material and from the design goals. The material database is searched for new candidate materials that satisfy the goals and constraints in manufacturing and raw stock availability. Uncertainty in the extracted data is modeled using fuzzy logic. Fuzzy member functions model the imprecise nature of data in each available parameter and rule sets characterize the imprecise dependencies between the parameters and makes decisions in identifying the unknown parameter value based on the incompleteness. A final confidence level for each material in a pool of candidate material is a direct indication of uncertainty. All the candidates satisfy the goals and constraints to varying degrees and the final selection is left to the designer's discretion. The process is automated by software that inputs incomplete data; uses fuzzy logic to extract more information and queries the material database with a constrained search for finding candidate alternatives.
ContributorsBalaji, Srinath (Author) / Shah, Jami (Thesis advisor) / Davidson, Joseph (Committee member) / Huebner, Kenneth (Committee member) / Arizona State University (Publisher)
Created2011