Finite Element Simulation Combination to Predict the Distortion of Thin Walled Milled Aluminum Workpieces as a Result of Machining Induced Residual Stresses

Machining induced residual stresses (MIRS) are a main driver for distortion of monolithic thin walled aluminum workpieces. A typical machining process for manufacturing such geometries for the aerospace industry is milling. In order to avoid high costs due to remanufacturing or part rejection, a simulation combination, consisting of two different finite element method (FEM) models, is developed to predict the part distortion due to MIRS. First, a 3D FEM cutting simulation is developed to predict the residual stresses due to machining. This simulation avoids cost intensive residual stress measurements. The milling process of the aluminum alloy AA7050-T7451 with a regular end mill is simulated. The simulation output, MIRS, forces and temperatures, is validated by face milling experiments on aluminum. The model takes mechanical dynamic effects, thermomechanical coupling, material properties and a damage law into account. Second, a subsequent finite element simulation, characterized by a static, linear elastic model, where the simulated MIRS from the cutting model are used as an input and the distortion of the workpiece is calculated, is presented. The predicted distortion is compared to an additional experiment, where a 1 mm thick wafer was removed at the milled surface of the aluminum workpiece. Furthermore, a thin walled component that represents a down scaled version of an aerospace component is manufactured and its distortion is analyzed. The results show that MIRS could be forecasted with moderate accuracy, which leads to the conclusion that the FEM cutting model needs to be improved in order to use the MIRS for a correct prediction of the distortion with the help of the linear elastic FEM model. The linear elastic model on the other hand is able to predict the part distortion with higher accuracy when using measured data instead of MIRS from the cutting simulation

Modelling of grinding mechanics : a review

Grinding is one of the most widely used material removal methods at the end of many process chains. Grinding force is related to almost all grinding parameters, which has a great influence on material removal rate, dimensional and shape accuracy, surface and subsurface integrity, thermodynamics, dynamics, wheel durability, and machining system deformation. Considering that grinding force is related to almost all grinding parameters, grinding force can be used to detect grinding wheel wear, energy calculation, chatter suppression, force control and grinding process simulation. Accurate prediction of grinding forces is important for optimizing grinding parameters and the structure of grinding machines and fixtures. Although there are substantial research papers on grinding mechanics, a comprehensive review on the modeling of grinding mechanics is still absent from the literature. To fill this gap, this work reviews and introduces theoretical methods and applications of mechanics in grinding from the aspects of modeling principles, limitations and possible future trendencies

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas