Rolling contact fatigue (RCF) induces a complex subsurface stress state, which produces significant microstructural alterations within bearing steels. A novel modeling approach is presented in this paper, which investigates the effects of microstructural deterioration, phase transformations, and residual stress (RS) formation occurring within bearing steels subject to RCF. The continuum damage mechanics approach was implemented to capture microstructural decay. State and dissipation functions corresponding to the damage mechanics process were used via an energy criterion to predict the phase transformations of retained austenite (RA). Experimental measurements for RA decomposition and corresponding RS were combined to produce a function providing RS formation as a function of RA decomposition and stress history within the material. Microstructural decay, phase transformations, and internal stresses were implemented within a two-dimensional (2D) finite element analysis (FEA) line contact model to investigate variation in microstructural alterations due to RSs present within the material. In order to verify the model developed for this investigation, initial simulations were performed implementing conditions of previously published experimental work and directly comparing to observed RA decomposition and RS formation in 52100 steel deep groove ball bearings. The finite element model developed was then used to implement various RS profiles commonly observed due to manufacturing processes such as laser-shot peening and carburizing. It was found that some RS profiles are beneficial in altering RA decomposition patterns and increasing life while others proved less advantageous.

This paper presents a study on dimensional variations and tolerance analysis and synthesis for polymer matrix fiber-reinforced composite components and assemblies. A composite component dimensional variation model was developed with process simulation based on thermal stress analysis and finite element analysis (FEA). Using the FEA-based dimensional variation model, the deformations of typical composite structures were studied and the regression-based dimensional variation models were developed. The regression-based dimensional variation models can significantly reduce computation time and provide a quick design guide for composite products with reduced dimensional variations. By introducing a material modification coefficient, the comprehensive regression models can handle various fiber and resin types and stacking sequences, which eliminates the complicated, time-consuming finite element meshing and material parameter defining process. A structural tree method (STM) was developed for rapid computation of composite assembly dimensional variations resulting from deformations on individual components, as well as the deformation of composite components with complex shapes. With the STM and the regression-based dimensional variation models, rapid design optimization was conducted to reduce the dimensional variations of composite assemblies. Cost-tolerance functions were developed using a fuzzy multiattribute utility theory based cost-estimation method. Based on the developed dimensional variation and cost-tolerance models, composite assembly tolerance analysis and synthesis were performed in this study. The exploring research work presented in this paper provides a foundation for developing practical and proactive dimensional control techniques for composite products.

Coordinate measuring machines (CMMs) are already widely utilized as measuring tools in the modern manufacturing industry. Fast and accurate probing is the current trend for the next generation of CMMs. However, measuring velocity of CMM applications are limited by dynamic errors that occur in CMMs. In this paper, the dynamic errors of coordinate measuring machines are analyzed theoretically and experimentally. The limited stiffness of air bearings were found to cause dynamic errors due to the existence of Abbe’s offsets. The characteristics of the air bearings used on CMMs were modeled by the finite element analysis (FEA). The load capacity and stiffness of the air bearings were computed. Using this model, the dynamic errors of the CMM were reduced through revising the air bearing design. To verify the effectiveness of this approach, the performance of the air bearings was tested both statically and dynamically. The results show that the dynamic errors can be significantly reduced.

In-process measurement and control of CNC machines can improve machining accuracy while increasing productivity and eliminating waste in a cost effective way. This paper describes an in-process measurement and control system called radial error feedback control (REFC) for bar turning in CNC turning centers. REFC was designed to compensate for the radial error due to the time-varying bar deflection caused by the cutting force, which moves along the bar at a constant velocity. In a dry cutting environment, a noncontact laser sensor was used to directly measure the radial error of the workpiece while it is being cut. To compensate for the radial error, a gain-scheduled Kalman filter for prediction together with PI control law was designed, based on a time-varying model of the deflection of the bar. Analytical modelling (using classical beam vibration theory) was employed to develop a simple model for the bar bending. For non-uniform bars finite element modelling can be used. Experimental results show that the dimensional and geometric accuracy of the workpiece is substantially improved by this radial error feedback control technique.

Coordinate measuring machines (CMMs) are already widely utilized as measuring tools in the modern manufacturing industry. Rapidly approaching now is the trend for next-generation CMMs. However, the increases in measuring velocity of CMM applications are limited by dynamic errors that occur in CMMs. In this paper, a systematic approach for modeling the dynamic errors of a touch-trigger probe CMM is developed through theoretical analysis and experimental study. An overall analysis of the dynamic errors of CMMs is conducted, with weak components of the CMM identified with a laser interferometer. The probing process, as conducted with a touch-trigger probe, is analyzed. The dynamic errors are measured, modeled, and predicted using neural networks. The results indicate that, using this mode, it is possible to compensate for the dynamic errors of CMMs.