This paper provides experimental evidence for the analogy between the time-delay feedback in publ... more This paper provides experimental evidence for the analogy between the time-delay feedback in public address systems and chatter in machining. Machining stability theory derived using the Nyquist criterion is applied to predict the squeal frequency in a microphone/speaker setup. Comparisons between predictions and measurements are presented.
A non-dimensional model is developed that relates cutting edge temperatures to process inputs (sp... more A non-dimensional model is developed that relates cutting edge temperatures to process inputs (speed and feed) and outputs (cutting and thrust forces) as well as tool and work thermal properties in micromachining with diamond tools. The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). Activation energy estimates are from 15 to 60 kJ / mole, depending on systematic uncertainties, such as diamond thermal conductivity, that still need to be addressed.
This paper describes the Monte Carlo evaluation of a single equation that can be used to determin... more This paper describes the Monte Carlo evaluation of a single equation that can be used to determine periodic error magnitudes from spectrum analyzer data. In this approach, the optical interference signal is recorded during constant velocity target motion using a spectrum analyzer and the magnitudes of the individual periodic error contributors are used to calculate error magnitudes. This study builds on prior work by treating the general case where both first and second order error components exist and arbitrary initial phase values are considered. Significant experimental results are presented which verify the new approach.
ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the... more ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.
In this paper, a statistical methodology of estimating wear coefficient and predicting wear volum... more In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.
ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting sp... more ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.
... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace E... more ... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace Engineering University of Florida Gainesville, FL KEYWORDS ... [1998], Davies et al. [1998], Schmitz and Donaldson [2000], Schmitz et al. [2001]), holder characteristics (Agapiou et al. ...
Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings
AIAA Journal, 2015
ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is depend... more ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is dependent on many parameters, including the wing design. With the goal of optimizing the wing for hover performance, the initial focus was to reduce the uncertainty in the thrust measurements. This is because lower uncertainty in this metric enables better resolution in comparing the performance of different designs. Aerodynamic performance variability was deemed to be the fault of an imprecise manufacturing technique. Therefore, adjustments were made to the fabrication process until a permissible level of uncertainty was attained for optimization; the goal was less than 5%. This paper chronicles the progression of the wing fabrication process and details how the uncertainty was evaluated. Four fabrication methods and two different wing designs are included in this study: a carbon fiber hand layup technique, carbon fiber cured in a machined mold, and two variations of a machined plastic skeleton reinforced with a carbon fiber rod. The uncertainty in thrust production, expressed in coefficient of variation, improved from 16.8% for the hand layup method to 2.6% for the computer numerically controlled plastic skeleton adhered to the nylon membrane with transfer tape. Additionally, the coefficient of variation for wing weight also reduced (from 11.4 to 2.0%). Read More: http://arc.aiaa.org/doi/abs/10.2514/1.J053884
Optimal Experimentation for Selecting Stable Milling Parameters: A Bayesian Approach
ASME 2009 International Manufacturing Science and Engineering Conference, Volume 1, 2009
Optimal Experimentation for Selecting Stable Milling Parameters: A Bayesian Approach. [ASME Confe... more Optimal Experimentation for Selecting Stable Milling Parameters: A Bayesian Approach. [ASME Conference Proceedings 2009, 277 (2009)]. Michael G. Traverso, Raúl Zapata, Tony L. Schmitz, Ali E. Abbas. Abstract. Bayesian ...
Tool wear monitoring using naïve Bayes classifiers
The International Journal of Advanced Manufacturing Technology, 2014
ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling ... more ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling tests were performed at different spindle speeds and the cutting force was measured using a table-mounted dynamometer. The effect of tool wear on force features in the time and frequency domains was evaluated and used for training the classifier. The amount of tool wear was predicted using the naïve Bayes classifier method. Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. Second, a continuous case is considered and the probability density function of the tool flank wear width is updated. The results are discussed.
This paper provides experimental evidence for the analogy between the time-delay feedback in publ... more This paper provides experimental evidence for the analogy between the time-delay feedback in public address systems and chatter in machining. Machining stability theory derived using the Nyquist criterion is applied to predict the squeal frequency in a microphone/speaker setup. Comparisons between predictions and measurements are presented.
A non-dimensional model is developed that relates cutting edge temperatures to process inputs (sp... more A non-dimensional model is developed that relates cutting edge temperatures to process inputs (speed and feed) and outputs (cutting and thrust forces) as well as tool and work thermal properties in micromachining with diamond tools. The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). Activation energy estimates are from 15 to 60 kJ / mole, depending on systematic uncertainties, such as diamond thermal conductivity, that still need to be addressed.
This paper describes the Monte Carlo evaluation of a single equation that can be used to determin... more This paper describes the Monte Carlo evaluation of a single equation that can be used to determine periodic error magnitudes from spectrum analyzer data. In this approach, the optical interference signal is recorded during constant velocity target motion using a spectrum analyzer and the magnitudes of the individual periodic error contributors are used to calculate error magnitudes. This study builds on prior work by treating the general case where both first and second order error components exist and arbitrary initial phase values are considered. Significant experimental results are presented which verify the new approach.
ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the... more ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.
In this paper, a statistical methodology of estimating wear coefficient and predicting wear volum... more In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.
ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting sp... more ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.
... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace E... more ... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace Engineering University of Florida Gainesville, FL KEYWORDS ... [1998], Davies et al. [1998], Schmitz and Donaldson [2000], Schmitz et al. [2001]), holder characteristics (Agapiou et al. ...
Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings
AIAA Journal, 2015
ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is depend... more ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is dependent on many parameters, including the wing design. With the goal of optimizing the wing for hover performance, the initial focus was to reduce the uncertainty in the thrust measurements. This is because lower uncertainty in this metric enables better resolution in comparing the performance of different designs. Aerodynamic performance variability was deemed to be the fault of an imprecise manufacturing technique. Therefore, adjustments were made to the fabrication process until a permissible level of uncertainty was attained for optimization; the goal was less than 5%. This paper chronicles the progression of the wing fabrication process and details how the uncertainty was evaluated. Four fabrication methods and two different wing designs are included in this study: a carbon fiber hand layup technique, carbon fiber cured in a machined mold, and two variations of a machined plastic skeleton reinforced with a carbon fiber rod. The uncertainty in thrust production, expressed in coefficient of variation, improved from 16.8% for the hand layup method to 2.6% for the computer numerically controlled plastic skeleton adhered to the nylon membrane with transfer tape. Additionally, the coefficient of variation for wing weight also reduced (from 11.4 to 2.0%). Read More: http://arc.aiaa.org/doi/abs/10.2514/1.J053884
Optimal Experimentation for Selecting Stable Milling Parameters: A Bayesian Approach
ASME 2009 International Manufacturing Science and Engineering Conference, Volume 1, 2009
Optimal Experimentation for Selecting Stable Milling Parameters: A Bayesian Approach. [ASME Confe... more Optimal Experimentation for Selecting Stable Milling Parameters: A Bayesian Approach. [ASME Conference Proceedings 2009, 277 (2009)]. Michael G. Traverso, Raúl Zapata, Tony L. Schmitz, Ali E. Abbas. Abstract. Bayesian ...
Tool wear monitoring using naïve Bayes classifiers
The International Journal of Advanced Manufacturing Technology, 2014
ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling ... more ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling tests were performed at different spindle speeds and the cutting force was measured using a table-mounted dynamometer. The effect of tool wear on force features in the time and frequency domains was evaluated and used for training the classifier. The amount of tool wear was predicted using the naïve Bayes classifier method. Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. Second, a continuous case is considered and the probability density function of the tool flank wear width is updated. The results are discussed.
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