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New model for improved computer numerical control machining

By Bhalchandra Chorghade, Editor, CNCTimes.com

CNC machining requires specific input parameters from human operators. NC code has to be generated from the 3D model of the product; feed rate, cutting speed, etc. are to be fed into the machine and so on, so that it produces the desired outcome.

The output of the process is judged by parameters called Key Performance Indicators (KPI), such as energy consumed during machining, surface roughness of the product, how efficiently machines are running, actual throughput versus projected, percentage of defective pieces, and so on.

A team of researchers from the Centre for Product Design and Manufacturing (CPDM), Indian Institute of Science (IISc), Bengaluru, have proposed a model to make Computer Numerical Control (CNC) more efficient using Machine Learning (ML).

There is currently no way to accurately know which input parameters would result in which KPIs. The team at IISc, led by B Gurumoorthy, has worked on this using ML. They have suggested a Data-Driven Digital Twin (DT) framework that predicts KPIs in a CNC machining environment. ML is a branch of Artificial Intelligence (AI) and computer science which focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy.

CNC machining is a manufacturing process in which pre-programmed computer software controls and dictates the movement of factory tools and machinery. The process can be used to control a range of complex machinery, from grinders and lathes to mills and CNC routers. With CNC machining, three-dimensional cutting tasks can be accomplished in a single set of prompts.

In the current study, the researchers have integrated their model with the CNC machining interface for predicting KPIs. The insights obtained from this combination can help human operators better decide the input parameters needed to get the desired KPIs, says the IISc release.

The model can also track and correct any deviations in planned values during the operation and instantly inform the human operators via a dashboard, which helps the operator to adjust the parameters. It also illustrates the choice of predictive modelling methods in both the stages of CNC machining and its outcomes. Such predictive models can significantly cut down production time and improve the efficiency of the CNC processes.

The study has been published in the International Journal of Computer Integrated Manufacturing.

https://kernel.iisc.ac.in/using-machine-learning-to-improve-cnc-machining/

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