SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Simple, inexpensive milling machine tapers panels from a common apex to a uniform height at panel edge regardless of the panel perimeter configuration. The machine consists of an adjustable angled beam upon which the milling tool moves back and forth above a rotatable table upon which the workpiece is held.
Digital computer prototype for use in automatic control program for machining on a milling machine
An alternative to reequipping metalworking enterprises is to modernize outdated CNC machines. In modernization, the aggregation principle permits the introduction of up-to-date high-speed cutting and multiaxial machining technology.
Versatile milling/sawing machine performs angle cuts, flat and profile milling, machining of grooves and slots, and edge trimming of phenolic panels. The machine is mounted on rails above a table equipped with vacuum capability for holding workpieces.
OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.
Calculator obtains the Rail and Z Scale dimensions on the tape-controlled Sundstrand milling machine. It provides computer with depth information required to process numerical control programs which, in turn, provide the tapes for operation of N/C milling machines.
Computer program automatically directs a numerically controlled milling machine through a series of cutting and trimming actions. It accepts engineering data points, passes smooth curve segments through the points, breaks the resulting curves into a series of closely spaced points, and transforms these points into the form required by the mechanism.
An experimental investigation was conducted to assess the effect of surface finish on the longitudinal and lateral aerodynamic characteristics of a highly-swept wing at supersonic speeds. A study of the effects of wing dihedral was also made. Included in the tests were four wing models: three models having 22.5 degrees of outboard dihedral, identical except for surface finish, and a zero-dihedral, smooth model of the same planform for reference. Of the three dihedral models, two were taken directly from the milling machine without smoothing: one having a maximum scallop height of 0.002 inches and the other a maximum scallop height of 0.005 inches. The third dihedral model was handfinished to a smooth surface. Tests were conducted in Test Section 1 of the Unitary Plan Wind Tunnel at NASA-Langley over a range of Mach numbers from 1.8 to 2.8, a range of angle of attack from -5 to 8 degrees, and at a Reynolds numbers per foot of 2 x 10(6). Selected data were also taken at a Reynolds number per foot of 6 x 10(6). Drag coefficient increases, with corresponding lift-drag ratio decreases were the primary aerodynamic effects attributed to increased surface roughness due to milling machine grooves. These drag and lift-drag ratio increments due to roughness increased as Reynolds number increased.
A spiral orbit tribometer (SOT) intended to characterize friction and wear phenomena has been constructed on a milling machine. The instrument, essentially a retainerless thrust bearing with one ball and flat races, is exceedingly simple and inexpensive to construct. The capabilities of the tribometer to measure both the coefficient of friction and contact electrical resistance are demonstrated with clean specimens as well as with well known lubricants such as molybdenum disulphide and Krytox oil. Operation in a purged environment of inert gas is also demonstrated. The results with these lubricants are quite close to what is obtained by other methods. Suggestions for extending the capabilities of the tribometer are given. This arrangement may find use in university mechanical engineering laboratories to introduce and study rolling contact motion as well as for research in contact mechanics and tribology.
For many years, the structure of milling and drilling machines has remained basically the same: the spindle chuck is attached in cantilever fashion to the vertical column. In the present work, a new configuration is considered.
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.
The error of metal-cutting machines must be kept within standard limits when a numerical control system is first introduced. A mechanism for more precise control of the motion is proposed on the basis of control modules in the core of the CNC. The results of using the proposed approach when the AxiOMA Control numerical control system is introduced at the Quaser MV184 machine tool (produced by “KEMZ” Ltd.) are outlined.
The measurement of power demand risks in manufacturing power systems will benefit manufacturers and the wider society. If the risks can be characterized using manufacturing parameters, manufacturers can better control risks originating in those parameters, select less power-risky production plans, and reduce utility costs and resource consumption. The measurement of risk can also help manufacturers and power suppliers to protect their power systems from unexpected disturbances. Existing measures of risk, however, do not consider time duration, and thus cannot accurately quantify the risks in manufacturing power systems; the risks of a period of high power demand must be evaluated with the duration of the surge. Therefore, new methods of measuring power demand risks are proposed, adapting measures drawn from the field of finance. With a focus on milling operations, processing power is shown to be a function of processing amount and processing time, and a power demand distribution is directly derived as a joint distribution of and A and T. A bivariate random variable model with copulas is applied to examine the correlation in the joint distribution. Then, based on evaluation of a probability distribution of power demand from and, new risk measures are introduced. Herein illustrative examples are provided to show how the proposed measures can quantify the power demand risks from milling machines, based on manufacturing parameters. Certain manufacturing parameters are found to affect overall power demand risks, including i) raw material type, ii) variability in processing time, and iii) correlation between and. In the examples, these three factors increase power demand risks by up to 108%, 67%, and 1% respectively.
TAPDIE, a generalized macro written for the APT numerical control system, cuts threads in stock too big for conventional machines or for which conventional methods are unsuitable. TAPDIE computes the machine tool path necessary and the information is passed on to a post-processor which produces a control tape.
Axis position and sequence number indicator system is designed to work in conjunction with numerical tape controller. Visual display of coordinate-axis position to nearest 0.0001 inch and sequential blocks of input data up to maximum of 999 are provided.
This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.
Mill profiler machines bevels, slots, and grooves in soft materials, such as styrofoam phenolic-filled cores, to any desired thickness. A single operator can accurately control cutting depths in contour or straight line work.