Look beyond simple tool wear for complete optimization

Published
April 19,2018 - 08:45am

Related Glossary Terms

  • burr

    burr

    Stringy portions of material formed on workpiece edges during machining. Often sharp. Can be removed with hand files, abrasive wheels or belts, wire wheels, abrasive-fiber brushes, waterjet equipment or other methods.

  • metalcutting ( material cutting)

    metalcutting ( material cutting)

    Any machining process used to part metal or other material or give a workpiece a new configuration. Conventionally applies to machining operations in which a cutting tool mechanically removes material in the form of chips; applies to any process in which metal or material is removed to create new shapes. See metalforming.

  • quality assurance ( quality control)

    quality assurance ( quality control)

    Terms denoting a formal program for monitoring product quality. The denotations are the same, but QC typically connotes a more traditional postmachining inspection system, while QA implies a more comprehensive approach, with emphasis on “total quality,” broad quality principles, statistical process control and other statistical methods.

Article by Seco Tools

The most fundamental elements of any metalcutting process, cutting tools are by their nature consumable. They wear until they are no longer effective. A traditional approach to metalcutting tool management employs wear analysis alone, focused on manipulating tool materials, geometries and application parameters to improve part output and tool life in a selected operation. To maximize the efficiency of your facility’s entire manufacturing process – boosting part-processing productivity and impacting the bottom line – you must go further in the pursuit of optimization.

Photo courtesy of Seco Tools

This means you must go beyond the basic measurement of tool wear to include other significant tooling-related considerations. These include the time spent in tool manipulation, problems/issues other than wear, production economics, shop organization, personnel attitudes and assumptions, value stream management and total manufacturing costs. The key performance metrics remain the same – achieving cost and time efficiency given a certain minimum quality and level of yield – but the scope has grown to encompass every aspect of a shop’s workflow to realize the highest possible benefit.

A major reason for widening the scope of tool wear analysis is the changing realities of manufacturing. Unlike the high-volume, low-product-mix (HVLM) process Henry Ford made famous, a new generation of mass production permits a cost-efficient, high-mix low-volume (HMLV) strategy. To balance inventory overhead with demand while simultaneously accommodating ongoing engineering changes, manufacturers have turned to the HMLV approach, machining fewer and fewer parts instead of the long, unchanging production runs of old.

In HVLM scenarios, shops machine identical parts from the same workpiece material in production runs that may last days, months or even years using the same equipment and the same kind of cutting tools. In that situation, tool life management is relatively simple. Shop personnel use prototyping and trial runs to determine the best average tool life, then divide the desired volume of parts by the expected life of individual tools.

However, tool life analysis becomes far more difficult in HMLV processes, in which a run of ten parts may be followed by one involving only two, five or even a single component. Workpiece materials may also change from steel to aluminum to titanium, and part geometries from simple to complex from one job to the next. In such instances, there is insufficient time available to determine tool life through trials. Instead, shops typically make conservative guesses regarding a tool’s projected life and, to be safe, employ a new tool for each run then discard it well before that tool reaches its full actual productive lifespan.

To get the most out of your machines and boost productivity, tooling OEMs, such as Seco, now recommend a wide-ranging approach to process optimization. As part of this approach, truly comprehensive tool deterioration analysis takes into account five core elements of operational excellence: the overall machining process, production economics, waste reduction, percentage yield goals and workforce management.

Many manufacturing process analysis efforts focus on reviewing the end results in relation to tool life and part output without thoroughly examining the process itself. This focus on tool life means that a shop may miss problems related to cutting tools but not directly to tool life, which can create production bottlenecks. For example, burrs typically have little to do with tool life, but their occurrence interrupts the manufacturing process due to the secondary operation required to remove the burrs.

Burr formation is, however, related to tool geometry and application parameters, which means shops must consider it in tool deterioration analysis processes. Tool breakage, another problem usually unrelated to tool wear, involves tool material, geometry and application parameters, as well as various other machine tool factors. To avoid time spent developing and implementing secondary or tertiary operations, it’s far superior to identify bottlenecks and correct them directly.

The most basic tooling-related economic consideration is often far clearer –  tools cost money, both in terms of total cost of goods sold and in terms of time spent on tool changeover and setup. A comprehensive analysis of the manufacturing process will also factor in related activities like the time spent acquiring and organizing tooling or loading programs into machines’ controls. Only when a shop can compare and contrast the various elements of production costs will they be able to identify candidates for cost reductions and boost operational profitability.

A useful way to track this waste reduction is to take the total time available for production then subtract planned downtime; unscheduled breakdowns, changeovers, minor stops and lost speed; and scrap and rework to arrive at effective machining time expressed as a percentage of the total time available. At 100 percent – a practically unattainable goal – the process results in a part made with the required level of quality produced in the shortest amount of time possible.

At the same time, rapidly changing HMLV manufacturing methods increase the difficulty of achieving high percentage yields for machining operations. In the case of long-run HVLM production, trials and adjustments can produce yield percentages in the high nineties. On the other hand, yield in the HMLV situation may be binary. A successful single-part run represents 100 percent yield, but when the part fails quality assurance or a workpiece is ruined, the yield is zero.

Demands for quality, cost effectiveness and time efficiency remain the same, but in HMLV applications, first-time yield becomes an overriding requirement. In that case, avoiding tool breakage is perhaps the most important consideration. One advantage is that tool wear is a minimal concern in short run situations, and a shop can apply, within reason, more aggressive and productive cutting parameters.

Meanwhile, these HMLV scenarios also reemphasize the role of humans in the manufacturing process. Unlike traditional mass-production processes in which machine operators have little to do after initiating a production run, HMLV operations require a form of traditional craftsmanship that involves creativity and flexibility to efficiently adapt to the continually changing parts and cutting conditions characteristic of HMLV machining. That means hiring different kinds of machine operators and investing more in their ability to think on their feet and solve problems in real time.

Tooling OEM Seco recommends that shops also pair this kind of on-the-fly problem solving and comprehensive analysis with an equally rigorous process for determining how tools are wearing. A machinist examines the cutting edges of a large number of randomly chosen tools from throughout a shop, one edge at a time, then classifies the wear patterns according to type and amount. When combined with the other data, this type of analysis results in a complete overview of the factors that must be optimized to obtain the most cost-efficient process.

Regardless of the process, the most important part of any given machining application is what happens when the tool contacts the metal and what occurs during the in-cut time. And while tool wear is inevitable and its management is essential to achieving successful machining operations, it is only one of the many influences on the efficiency of a shop’s overall manufacturing process. By going beyond wear analysis of single tools to looking at a random sample of edges in the context of the entire manufacturing process, shops can achieve new levels of productivity and profitability.