Parameter Identification for Finite Element Based Models in Dry Machining Applications

Abstract

Dry machining is a challenging topic in industrial manufacturing: The absence of coolants results in ecological and economical benefits, but also in a significant increase of the occurring thermal stress. This leads to geometrical deviations of the machined workpiece impairing the functional performance of the final part. The increased thermal stress is especially important in high-precision manufacturing and requires careful process planning. To determine process parameters in order to minimize geometrical errors, optimal control is an appropriate tool. A precise partial differential equation model representing the material behavior is required. The partial differential equation contains unknown physical quantities, such as heat fluxes, which cannot be measured directly. One common method to derive these quantities is solving an inverse problem by parameter identification, i.e. determining the model parameters, such that the model matches the measurements best. We will present a simultaneous analysis and design approach which states a numerical finite element discretization of the model as constraints of the resulting nonlinear optimization problem (NLP). The proposed method takes advantage of both, direct consideration of state constraints, like temperature boundaries, and lower computational costs compared to the nested analysis and design approach. The additional implementation costs can be significantly reduced by using an external finite element library for assembling the optimization constraints and its derivatives. This approach for determining physical parameters of milling experiments is user-independent and works fully automated. It is the first step for an optimal control of dry machining processes. We will give a description of this method as well as numerical identification results of real heat flux densities by the sparse NLP solver WORHP.

Inverse surface integrity problem in ultrasonic impact-treated AISI 304 stainless steel components

“…For example, in machining of titanium alloys and nickel-based alloys, it is concluded that further modeling studies are needed to create predictive physics-based models that are in good agreement with reliable experiments, while explaining the effects of many parameters [3]. However, the correlations between the process loads and surface integrity state variables or surface integrity parameters are hardly to be established quantitatively since a general relation independent from processes is not available to derive the dependence of surface integrity parameters on the various manufacturing processes, although many investigations have confirmed that different surface integrity states of manufactured components can be obtained by varying the process method and parameters [4][5][6]. The processindependent assessing of the various processes during manufacturing is more systematically concerned by Brinksmeier et al [7,8] with proposing a concept of process signature as a new approach to comprehensibly characterize the influence of machining processes on the material modifications generated in the component surface layer.…”

New Coordination Software for Parameter Identification Applied to Thermal Models of an Actuator Strut

“…Most such algorithms take less assumptions on the model structure than analytical methods and can therefore be used for a wide variety of model types or even unknown (black-box) models. Therefore, they are the usual way of process identification for complex model types like finite element models [7,8]. But also for simple models, where analytical methods exist, such optimization methods are sometimes used; a well-known example is the Matlab System Identification Toolbox [9].…”