David Dornfeld

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The detection of the onset of motion and slip between an end effector and workpiece has been of interest for some time and many schemes have been proposed. This paper reviews some of the background on slip detection methods and proposes the use of acoustic emission signal analysis as a slip detection technique. The genesis of acoustic emission during slip(More)
Recent advances in machine automation and sensing technology offer new opportunities for continuous condition monitoring of an operating machine. This paper describes an intelligent machine monitoring framework that integrates and utilizes data collection, management, and analytics to derive an adaptive predictive model for the energy usage of a milling(More)
This paper describes a real-time data collection framework and an adaptive machining learning method for constructing a real-time energy prediction model for a machine tool. To effectively establish the energy consumption pattern of a machine tool over time, the energy prediction model is continuously updated with new measurement data to account for(More)
Increasing awareness of energy consumption and its environmental impacts has prompted a need to better predict the energy consumption of various industrial processes, including manufacturing. Modeling can allow manufacturers to optimize the efficiency of their manufacturing processes. Highly accurate, data-driven models of energy consumption of CNC milling(More)
Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating(More)
Improved data quality and availability, along with lower computation costs, have generated interest in sensor-based tool condition monitoring technologies. In this study, an integrated vibration and acoustic sensor is used for tool condition monitoring, particularly for chatter detection and tool condition classification. Based on feature extraction in the(More)
This paper proposes a classification scheme for performance metrics for smart manufacturing systems. The discussion focuses on three such metrics: agility, asset utilization, and sustainability. For each of these metrics, we discuss classification themes, which we then use to develop a generalized classification scheme. In addition to the themes, we discuss(More)
INTRODUCTION Additive Manufacturing (AM) can improve flexibility and convenience, lower manufacturing costs, and reduce time to market for many manufacturing applications [1,2]. Successfully implementing and expanding AM requires improvements in surface quality, shear and tensile strength, build time, accuracy, and precision of these processes [3]. Of these(More)
The use of data-driven predictive models is becoming increasingly popular in engineering and manufacturing sectors. This paper discusses the deployment of Gaussian Process Regression (GPR) predictive models for smart manufacturing. A scoring engine is developed based on the Predictive Model Markup Language (PMML) standard to illustrate the portability of(More)
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