Last updated on Nov 14, 2024

How do you use predictive models and data analysis to estimate tool life and plan tool changes?

Tool life and tool wear are critical factors that affect the quality, productivity, and cost of machining operations. To optimize your machine tool performance and reduce downtime, you need to know how to use predictive models and data analysis to estimate tool life and plan tool changes. In this article, we will explain the basics of tool life and tool wear, the types of predictive models and data analysis methods, and the benefits and challenges of using them.

1 Tool life and tool wear

Tool life is the amount of time or cutting distance that a tool can perform before it needs to be replaced or sharpened. Tool wear is the gradual deterioration of the tool geometry and cutting edge due to friction, heat, and mechanical stress. Tool wear affects the surface finish, dimensional accuracy, and cutting forces of the machined parts. Therefore, it is important to monitor and control tool wear to maintain the quality and efficiency of the machining process.

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Godwin Josh

Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
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To estimate tool life and plan tool changes using predictive models and data analysis, machine monitoring systems can be integrated to continuously track key parameters such as cutting forces, temperature, vibration, and tool wear indicators. Machine learning algorithms can analyze this data to predict tool wear patterns and the remaining useful life (RUL) of tools. Predictive models like regression analysis or time-series forecasting can be used to correlate these factors with tool degradation, allowing for timely predictions of tool failure. This helps in scheduling proactive tool changes, minimizing unplanned downtime, and improving operational efficiency while maintaining machining quality and precision.

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2 Predictive models

Predictive models are mathematical equations or algorithms that relate tool life or tool wear to various parameters, such as cutting speed, feed rate, depth of cut, tool material, workpiece material, coolant, and temperature. Predictive models can be empirical, mechanistic, or hybrid. Empirical models are based on experimental data and statistical regression, such as the Taylor equation or the Kienzle equation. Mechanistic models are based on physical principles and mechanistic analysis, such as the Oxley model or the Lee-Shaffer model. Hybrid models are combinations of empirical and mechanistic models, such as the artificial neural network (ANN) model or the fuzzy logic model.

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3 Data analysis

Data analysis is the process of collecting, processing, and interpreting data to extract useful information and insights. Data analysis can be descriptive, diagnostic, predictive, or prescriptive. Descriptive data analysis summarizes the past or current state of the tool condition and performance, such as tool wear rate, cutting force, vibration, acoustic emission, or temperature. Diagnostic data analysis identifies the causes or factors that affect the tool condition and performance, such as tool geometry, cutting parameters, or workpiece material. Predictive data analysis forecasts the future state of the tool condition and performance, such as tool life or tool wear progression. Prescriptive data analysis recommends the optimal actions or decisions to improve the tool condition and performance, such as tool change timing, cutting parameter adjustment, or tool selection.

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4 Benefits and challenges

Using predictive models and data analysis to estimate tool life and plan tool changes can offer several benefits, such as improving the quality and consistency of machined parts, increasing productivity and efficiency of the machining process, reducing cost and waste, and enhancing safety and reliability. However, there are also some challenges and limitations to consider, such as choosing the appropriate predictive model, acquiring and processing accurate data, validating and updating the predictive model with actual tool performance, and integrating the predictive model with the control system.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Mike Payne

Manufacturing Leader | Manufacturing Investor | Podcast Host | Huge fan of all entrepreneurs!
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Implementing machine monitoring for tool life and wear will answer so much of this and the options on the market are going to explode over the next few years. Growth of this involves three key areas: accurate data collection (sensors monitoring tool wear, cutting forces, vibrations, etc.), predictive modeling (using collected data to forecast tool failure), and data analysis (interpreting model predictions to optimize tool changes and maintenance). The systems improve part quality, boosts productivity, reduces waste, and enhances safety, but requires careful model selection, data validation, and integration with existing controls.

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Dritero Islami

Director of Operations And Business Development
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Collect data on variables like temperature, vibration, and cutting speed. Analyse this data to identify wear patterns and key factors affecting tool longevity. Use predictive models, such as regression or time-series analysis, to forecast tool wear and determine the remaining useful life. Schedule tool changes based on these predictions, allowing for proactive maintenance and reducing downtime. This approach helps optimise tool usage and ensures a smoother, more cost-effective production process.

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How do you use predictive models and data analysis to estimate tool life and plan tool changes?

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1 Tool life and tool wear

2 Predictive models

3 Data analysis

4 Benefits and challenges

5 Here’s what else to consider

Machine Tools

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