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 predictive models among different statistical tools and different platforms. Specifically, we evaluate the tradeoffs between embedding GPR-based predictive models on a physical device and executing the predictive models on a managed cloud platform like the Google Compute Engine. We compare the performance of the two deployment strategies with two predictive models, namely an energy consumption model and a milling tool condition model, that are built with data from a Mori Seiki CNC milling machine. We describe how the response time of the two deployment strategies is related to the network latency and computational speed of the scoring machine hardware. It is shown that the time required to calculate model predictions is a significant factor in the overall response time of the embedded scoring engine. We demonstrate that the scoring engine on the cloud platform can achieve a lower response time and higher prediction rate than the microcomputer, due to the superior computational performance of the cloud-based hardware.

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 time-varying effects of the machine tool, such as tool wear and machine tool deterioration. In this work, a real-time data collection and processing framework is developed to retrieve raw data from a milling machine tool and its sensors and convert them into relevant input features. The extracted input features are then used to construct the energy prediction model using Gaussian Process (GP) regression. To update the GP regression model with real-time streaming data, we investigate the use of sparse representation of the covariance matrix to reduce the computational and storage demands of the GP regression. We compare computational efficiency of sparse GP to that of full GP regression model and show the effectiveness of the sparse GP regression model for tracking the variation in the energy consumption pattern of the target machine.

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 machine. This model is designed using a Gaussian Process (GP) regression algorithm, which is a flexible regression method that also provides an uncertainty estimate. To improve computational efficiency, we propose a Collective Gaussian Process (CGP) in which the overall energy prediction is made by constructing local GP models weighted by probability distribution functions obtained using the Gaussian Mixture Model (GMM) technique. Finally, we demonstrate the ability of the proposed monitoring framework to construct an energy prediction model to predict the energy used to machine a part.

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 use of acoustic emission signal analysis a slip detection technique. The genesis acoustic emission during slip is discussed and experimental evidence presented to the sensitivity of acoustic emission to slip between two surfaces. Preliminary tests with a robot gripper have also done to demonstrate the feasibility of acoustic emission slip detector.