How to optimise machining tool life using machine data

The economic investment in tooling, together with the scrapping of defective parts, means that prolonging the life span of the service life of machining tools is an important goal for metal cutting companies. Traditional machining relied on experimentally determined settings to determine the service life of a machine tool. Operators could record part counts until a tool failed or began to develop out-of-specification parts. The average of these part counts could then be used to establish a threshold for the service life of machining tools. The piece count given by the machine tool manufacturer was also used to establish the tool change rate.

These solutions are inaccurate, which leads to one of two things:

• A tool is under-utilised, which means that a tool is replaced before it has reached the end of its useful life
• A tool is overused, resulting in scrap parts and downtime.

Tool monitoring allows manufacturers to monitor and optimise the life of their tools to ensure that they can get the highest return on investment from their tools without suffering the consequences of catastrophic tool failure.

What is the service life of machining tools?

Tool life is the useful life of a machine tool. It can be expressed in the number of good parts that a given tool is capable of machining. Once a tool wears to the point where the parts it creates are out of specification, its useful life is over and the tool must be replaced.

Tool wear, tool failure, and tool wear and machining tool life CNC are a reality. All tools experience wear and tear and will eventually fail if they continue to work. However, it is in managing tool life effectively that manufacturers can drive the most efficient use of their machine tools.

The only possible way to increase machine tool life and at the same time avoid scrap parts and failures is to collect machine tool data and use it to determine when a tool change should be made.

All tools have predictable life curves, i.e. the length of time a tool is expected to last and produce parts to desired quality standards. However, tool life can be difficult to determine accurately, especially if done manually, due to the wide variety of factors that affect the life of a tool. For example, the material being machined, the type of cutting, the speed and feed rate, etc.

Ways to improve the service life of machining tools

There are many factors that affect the machining tool life. Here are some methods to reduce tool wear:

Use appropriate speeds and feeds

While the cutting tool is rotating, drilling or cutting at a given cutting speed, it is also moving along the workpiece. Using the proper speed and feed settings will increase tool life significantly. While actual cutting time is always a consideration, proper speed and feed settings can extend tool life.

Apply cutting fluid

The cutting process generates a lot of heat due to metal to metal contact. The correct type of cutting fluid and the correct amount of fluid application will prolong tool life.

Do not cut shavings

Chips are the way metal material is removed from the workpiece after cutting. They also carry away excess heat generated during cutting but the settings must be correct to avoid chip clipping, which results in increased flank wear, crater wear and other causes of tool wear.

Use the right machining tools

The complexity of the part, the roughness of the surface, the precision CNC machining required, the tolerance requirements of the finished part and other considerations are critical. For example, some parts may be strong enough to require a high-speed steel tool. Knowing these parameters will help select the right tool for the combination of factors.

Distribute wear on the cutting edge

Cutting programmes and plans should be designed to use as much of the cutting edge as possible. Continuously focusing on one part of the cutting tool can increase tool wear.

Controlling eccentricity

The age of the equipment can affect the eccentricity in machining. Modern machines may have an automated tool holder. This tool holder ensures proper positioning and clamping of the tool. On older machines, eccentricity comes from misalignment or misclamping of the centre of the tool spindle with respect to the centre line of the centre spindle and can result in scrap parts and reduced tool life.

Controlling diversion

As heat builds up and cutting is in progress, tremendous cutting forces are produced as the characteristics of the material affect both the tool and the workpiece. The chips that form as the metal is removed and exits the workpiece push back as they accumulate. If the variables are not correct, this can bend or deflect the tool.

Factors influencing machining tool life

Using a calculation of the machining tool lifeAs with the Taylor tool life equation, different factors can be plotted to project different tool life curves depending on the combination of variables applied. These include:

• Cutting speed

  The hardness of the workpiece, the different tool materials, the complexity of the cuts and other factors imply an optimum speed for each combination of factors.

• Feed speed and depth of cut

  As well as the cutting speed, the feed rate and depth of cut can also be optimised. And that optimum point will be determined by the aggressiveness of the cut required.

• Hardness of the part

  Tools used for cutting very hard materials will wear faster than those used for cutting softer metals. The different hardness of the workpiece can trigger different types of tool wear. On soft metals, such as aluminium, edge build-up can occur. Metals such as titanium can cause catastrophic failure on an incorrect tool.

• Tool material

  The tool material must have performance characteristics that meet or exceed the workpiece being cut. Some tools may be specially hardened to work on hardened steel or exotic metals such as titanium. But they must also be designed to work on metals that have a poor surface finish.

• Type of cut

  Some cutting tools exert light abrasion to create a surface finish. As well as cutting speed and feed rate, the type of cutting must be taken into account when plotting tool life curves and reducing wear.

Techniques for extending and optimising the service life of machining tools

Tool monitoring captures machine data directly from the machine control, providing accurate, real-time data on machine performance and status. The data can be collected to monitor machine tool conditions. and develop thresholds for when tool failure is imminent.

In addition, this data can be used to develop algorithms to predict and prevent catastrophic machine tool failures. Instead of manually collecting part counts or using the manufacturer's machine tool life recommendation, users can take advantage of accurate machine tool data to establish more effective life parameters.

Machine tool analytics can be used to optimise the processes around the machine and so improving the productivity of precision machiningenabling communication and automation that support the maximisation of the service life of machining tools.