Multimode algorithm helps to get a better handle on tool deterioration

Jul 16, 2014
Multimode algorithm helps to get a better handle on tool deterioration
An innovative algorithm can improve the productivity of computer-controlled milling machines used in high-tech manufacturing. Credit: A*STAR Singapore Institute of Manufacturing Technology

Computer numerically controlled (CNC) milling machines—used in high-tech industries such as aeronautical manufacturing—operate continuously to maximize the production efficiency. However, this requires careful monitoring for any tool deterioration. Omid Geramifard and co-workers from the Singapore Institute of Manufacturing Technology at A*STAR have now developed an improved algorithm for diagnosing tool-wear problems before they occur.

CNC milling machines cut and shave metal materials into precisely specified structures. To ensure they can operate 24 hours a day without any unnecessary downtime, the milling tools are carefully monitored with unobtrusive sensors and analytical computer models. The algorithm developed by Geramifard's team uses a sophisticated, multicomponent model that narrows down tool sensor data to the most effective group for analysis—an innovation that boosts its predictive capabilities while maintaining its .

As it is hard to estimate when tools will fail by simply using physical models of actual milling machines, researchers tend to use data-driven models that analyze historical tool-wear patterns. One such method, known as the hidden Markov model (HMM), hypothesizes that a tool's condition depends only on its past behavior, a simplification known as the Markov assumption. 'Hidden' states are then introduced to account for degradation factors that cannot be inspected directly. Using probability equations to relate observable data to the hidden states, HMMs can calculate how tools change over time.

Geramifard and co-workers improved current models by developing a multimode HMM-based approach that captures and analyzes several different tool-wear parameters. They 'trained' each mode of the HMM with selected segments of experimental data, and then combined the multiple outputs using weighting schemes. However, running simultaneous HMMs requires intense computational power, which significantly slows the CNC machine monitoring process.

To overcome this problem, the researchers devised a 'windowing' technique that reduces the computational cost by performing HMM calculations over a short time frame—selected through a cross-validation process in the training phase—instead of over the full observation sequence. When the team used their multimodal approach to predict tool wear in a real CNC milling machine, they found their approach outperformed conventional techniques when appropriate window lengths were adopted. This breakthrough in accuracy was realized by removing unnecessary connections to old observations.

"This model opens the path to more effective stochastic modeling of tool wear and degradation," says Geramifard. "With more accurate, data-driven diagnostics and prediction, more efficient tool usage can occur for assured quality of the produced workpiece."

Explore further: Dubai plans to build 3-D printed office building

More information: Geramifard, O., Xu, J.-X., Zhou, J.-H. & Li, X. Multimodal hidden Markov model-based approach for tool wear monitoring. IEEE Transactions on Industrial Electronics 61, 2900–2911 (2014). dx.doi.org/10.1109/TIE.2013.2274422

Related Stories

Real-time energy audit reduces power consumption

Oct 23, 2013

Governments are pressuring industries to reduce energy consumption for both environmental and economic reasons. Optimizing factory processes and improving equipment can lower energy usage but this not only ...

Revolutionising European machine tools

Sep 09, 2013

From lathes and shapers to cutting and grinding machines, machine tools helped put Europe at the forefront of manufacturing in the past and remain essential to many industries, including aerospace, automotive, ...

Recommended for you

Revealing faded frescos

10 hours ago

Many details of the wall and ceiling frescos in the cloister of Brandenburg Cathedral have faded: Plaster on which horses once "galloped" appears more or less bare. A hyperspectral camera sees images that remain hidden to ...

Device could detect driver drowsiness, make roads safer

12 hours ago

Drowsy driving injures and kills thousands of people in the United States each year. A device being developed by Vigo Technologies Inc., in collaboration with Wichita State University professor Jibo He and ...

New capability takes sensor fabrication to a new level

Jun 30, 2015

Operators must continually monitor conditions in power plants to assure they are operating safely and efficiently. Researchers on the Sensors and Controls Team at DOE's National Energy Technology Laboratory ...

Smart phones spot tired drivers

Jun 30, 2015

An electronic accelerometer of the kind found in most smart phones that let the device determine its orientation and respond to movement, could also be used to save lives on our roads, according to research ...

User comments : 0

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.