Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Closed channel fabrication using micromolding of metallic glass

Casting and molding are attractive options for low cost mass production. Metallic glasses may avoid many of the problems associated with micromolding of metals such as high temperatures and pressures, costly mold production, and shrinkage due to crystallization. In this study, we have produced 100 μm², enclosed channels in metallic glass using a novel multilayer Si stack micromolding process. Scanning electron microscope (SEM) images of the enclosed channels are provided to verify that the closed channels extended through the sample.     

Deformation Machining - A New Hybrid Process

This paper describes a novel hybrid process, Deformation Machining, that combines two emerging manufacturing processes - machining of thin structures and single point incremental forming. This hybrid process enables the creation of structures that have geometries that would be difficult or impossible to create using any other processes. A feasibility study has been conducted exploring the toolpath planning and deformation force data was collected. Because the forming operations occur on sheet-like material made by machining rather than rolling, we have conducted experiments testing the formability of machined sheet. Preliminary results are encouraging, and point to a broad range of industrial applications for this process.     

Numerical analysis and parameter study of a mechanical damper for use in long slender endmills

A mechanical damper has been introduced to reduce tool vibration during the high-speed milling process. The mechanical damper is composed of multi-fingered cylindrical inserts placed in a matching cylindrical hole in the center of a standard end-milling cutter. Centrifugal forces during high-speed rotation press the flexible fingers against the inner surface of the tool. Bending of the tool/damper assembly due to cutting forces or chatter vibration causes relative axial sliding between the tool inner surface and the damper fingers, and dissipates energy in the form of friction work. In this paper, a simple numerical method is presented to estimate the amount of friction work during tool bending. Non-linear static finite element analysis is used to estimate normal and frictional contact forces due to centrifugal forces and cutting forces, and calculate the amount of frictional work dissipated by the damper. The numerical results are compared with analytical results, and show a similar trend. Parameter studies are also carried out using the numerical model to identify the best configuration to maximize the amount of friction work.     

Automated measurement and compensation of thermally induced error maps in machine tools

In this paper, a direct method of machine tool calibration is adopted to model and predict thermally induced errors in machine tools. This method uses a laser ball bar (LBB) as the calibration instrument and is implemented on a two-axis computerized numerical control turning center (CNC). Rather than individually measuring the parametric errors to build the error model of the machine, the total positioning errors at the cutting tool and spindle thermal drifts are rapidly measured using the LBB within the same experimental setup. Unlike conventional approaches, the spindle thermal drifts are derived from the true spindle position and orientation measured by the LBB. A neural network is used to build a machine model in an incremental fashion by correlating the measured errors with temperature gradients of the various heat sources during a regular thermal duty cycle. The machine model developed by the neural network is further tested using random thermal duty cycles. The performance of the system is also evaluated through cutting tests under various thermal conditions. A substantial improvement in the overall accuracy was obtained.     

Uncertainty propagation in calibration of parallel kinematic machines

This paper outlines in detail a method for determining the uncertainty present in the kinematic parameters (joint locations, initial strut lengths, and spindle location and orientation) for parallel kinematic devices after calibration. The uncertainty estimation method using Monte Carlo simulations was applied to a sequential method for determining the kinematic parameters of fully assembled Hexel Tornado 2000 (a 6–3 Stewart platform) milling machine. Results for the uncertainty present in the kinematic parameters of a Hexel Tornado 2000 milling machine after calibration using a SMX 4,000 laser tracker are shown.     

Durability evaluation of software error correction on a machining center

Machine tool software error correction has been successfully demonstrated in laboratories for at least the last 20 years. However, no results could be found that evaluate the durability of such an approach. To be cost beneficial for industry, the time consuming measurement procedure needs to be as infrequent as possible. This study attempts to quantify the durability of such an approach on a commercial 3-Axis machining center. A traditional correction model is created and implemented inside the control system along with a first order thermal correction of the scales. Measurements are made with the Laser Ball Bar, which minimizes data collection to less than 30 min. The compensation system was evaluated over a 9 month period in which the machine experienced normal machining and common mishaps (tool crash). The results show that during this 9 month period, the compensation system was still capable of significantly reducing the machine's inherent errors.     

Employment Discrimination: The Role of Implicit Attitudes, Motivation, and a Climate for Racial Bias.

This study is an attempt to replicate and extend research on employment discrimination by A. P. Brief and colleagues (A. P. Brief, J. Dietz, R. R. Cohen, S. D. Pugh, & J. B. Vaslow, 2000). More specifically, the authors attempted (a) to constructively replicate the prior finding that an explicit measure of modern racism would interact with a corporate climate for racial bias to predict discrimination in a hiring context and (b) to extend this finding through the measurement of implicit racist attitudes and motivation to control prejudice. Although the authors were unable to replicate the earlier interaction, they did illustrate that implicit racist attitudes interacted with a climate for racial bias to predict discrimination. Further, results partially illustrate that motivation to control prejudice moderates the relationship between explicit and implicit attitudes. Taken together, the findings illustrate the differences between implicit and explicit racial attitudes in predicting discriminatory behavior. (PsycINFO Database Record (c) 2010 APA, all rights reserved)