An automated feature extraction method with application to empirical model development from machining power data

Machining shop floor jobs are rarely optimised for minimisation of the energy consumption, as no clear guidelines exist in operating procedures and high production rates and finishing quality are requirements with higher priorities. However, there has been an increased interest recently in more energy-efficient process designs, due to new regulations and increases in energy charges. Response Surface Methodology (RSM) is a popular procedure using empirical models for optimising the energy consumption in cutting operations, but successful deployment requires good understanding of the methods employed and certain steps are time-consuming. In this work, a novel method that automates the feature extraction when applying RSM is presented. Central to the approach is a continuous Hidden Markov model, where the probability distribution of the observations at each state is represented by a mixture of Gaussian distributions. When applied to a case study, the automated extracted material cutting energies lay within 1.12% of measured values and the spindle acceleration energies within 3.33% of their actual values

An application of autoregressive hidden Markov models for identifying machine operations

Due to increasing energy costs there is a need for accurate management and planning of shop floor machine processes. This would entail identifying the different operation modes of production machines. The goal for industry is to provide energy monitors for all machines in factories. In addition, where they have been deployed, analysis is limited to aggregating data for subsequent processing later. In this paper, an Autoregressive Hidden Markov Model (ARHMM)-based algorithm is introduced, which can determine the operation mode of the machine in real-time and find direct application in intrusive load monitoring cases. Compared with other load monitoring techniques, such as transient analysis, no prior knowledge of the system to be monitored is required

A digital toolkit to model energy consumption of manufacturing operations within the industry 4.0 factory

A digital toolkit to model energy consumption of manufacturing operations within the industry 4.0 factor

A cyber physical system for tool condition monitoring using electrical power and a mechanistic model

© 2020 Tool Condition Monitoring (TCM) systems, which aim to identify tool wear automatically to avoid damage to the machined part, are often not suitable for industrial applications due to: (i) sensing methods which can be expensive and invasive to install and (ii) testing regimes that only evaluate a narrow operating window under controlled conditions. To combat these issues, the research outlined in this paper explores the feasibility of TCM using electrical power consumption of an industrial Computer Numeric Control (CNC) cutting machine in combination with a mechanistic model for end milling operations. End milling of aluminium 6082 was performed over a range of cutting parameters (i.e. spindle speed, feed rate, tool diameter) and repeated with increasing levels of tool wear to ascertain the suitability of this approach. Reasonable correlation between the predicted and observed tool wear was found using the mechanistic model (R2 = 0.801), however variance between the cutting parameters highlights the limitations of the predictions. To combat this variance, an averaging window is taken over the course of a cutting program to reduce the overall error. A concept TCM system is then proposed, utilising cyber-physical models of the milling process to determine the width and depth of cuts for complex geometries which change in real time, with the challenges of implementing such a system discussed