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by Stephen Birman

Process control in metalworking hasn't changed much during the past 100 years. At first the process was a kind of brute force method: Make some parts and measure them, make some more and measure again, throw out the bad ones, measure an entire batch of parts if you thought they were all suspect, and so on. Then came Walter Shewhart and statistical process control. SPC provided a means that aided in process monitoring and identifying special causes of variation. Yet, even today, process control in metalworking is still more art than science. Material inconsistency, variation of tool geometry, temperature oscillation on the cutting edge and the sheer physics involved when a tool comes in contact with a work piece create a process that is difficult to predict and control. This article discusses some of the difficulties of process control in metalworking and then describes how another technique, intelligent process-adaptive control technology, can be used in discrete-part manufacturing to control a process to achieve zero defects.

Intelligent process-adaptive control technology

The interaction between a machine tool and the part being worked is extremely complex and introduces variables that are unique and problematic to this industry. First, the process is a combination of a continuously deteriorating tool and dynamically changing variation--random and nonrandom. This deterioration is both unidirectional (the tool is constantly shrinking) and nonlinear (the wear rate varies during the life of the tool).

Typically, a tool-wear curve consists of small, steep slopes of irregular duration within a single tool lifespan: Cutting-edge round-off and initial flank wear are low-rate processes, but, in the latter stages of tool wear, flank wear and cratering might be a high-rate process. Tool wear in an ideal metal-cutting process progresses in three phases: round-off of cutting edge, low-rate tool wear and transition to high-rate tool wear. The duration of each phase isn't repeatable even in the most stable process conditions. Multiple sources of variation on the cutting or forming edge change the tool wear curve every time an insert is indexed or a form tool is sharpened.

Next, most metalworking processes are designed to use a tool for creating more than one dimension, e.g., one tool cutting two diameters. It's difficult to avoid a displacement between the locations of each dimension relative to specification limits. The difference in cutting speed for associated dimensions (the cutting speed of the larger diameter is faster than that for the smaller diameter) leads to different tool-wear rates on the same tool. Roundness, taper and surface finish are all related to a tool. The objective of any process control technology is to determine the moment when a tool should be compensated for, or changed, in order to provide compliance with quality requirements, including all dimensions and tolerances created by this tool.

Predictive modeling provides an accurate and easy-to-use method to control such processes. MICRONITE, an expert system based on the iPACT concept and developed by High Tech Research Inc. of Deerfield, Illinois, uses predictive modeling as part of its intelligent process-adaptive control technology.

There are four major components of variation that iPACT addresses. The following causes of variation identified for each component can be found in any metalworking process:

Primary variation--Includes measurement error (e.g., accuracy, precision and repeatability), shape variation (e.g., taper and roundness) and machine precision (e.g., piece-to-piece variation, inter-spindle and inter-fixture variation). The extent of this variation determines whether the tolerance is wide-open, open, close or extremely close. As an example, a close tolerance on a multispindle screw machine becomes an open tolerance on a CNC lathe. Quantifying primary variation helps uncover causes and reduce variations that force frequent and unnecessary adjustments and tool changes.

Process-dependent variation--Reflects tool wear and the instability of a dynamic system, including nonrandom variation due to tool-wear trend and random variation due to the instability of process variables.

Cumulative product variation--Depends on the alignment of process runs. These include variation due to the displacement of the locations of sample averages between sequential processes and variation caused by indiscriminate process interruption and adjustment.

Special causes of variation as defined by SPC--Variation related to machine, material and people

Knowledge of the dynamic nature of discrete processes helps us understand different ways to achieve a state of process control. Adaptability is where it all begins. An intelligent system should be capable of controlling a multitude of relatively stable and unstable processes at once. The system should cope with a range of quality requirements, from compliance-to-specification to Six Sigma. This means that unique sampling design and automatic execution of predictive modeling is required for every operation and characteristic.

MICRONITE uses three types of predictive models: nonlinear trend control, control of probability of defects and control of extent of tool wear. Because sampling time is critical, adaptive intervals for every tool are updated after each data entry. The system also allows predetermined levels of product variation. Real-time control by a CpK model (without using X-bar and R charts) guarantees a customer-required variation index.

How does it work?

A workstation is located near a machine, a group of machines or a cell. Upper and lower specification limits are entered for each dimension, as is the cycle time for each cutting operation. Based on the tool-wear model, the number of parts run and other parameters, the software prompts the user to measure a unit or sample. The software then tells the operator to continue, input a compensation adjustment into the machine or change the tool. It will also tell the operator how long before the next measurement must be taken. The software adapts the model as new data is collected and analyzed.

Here's an oversimplified description of what actually occurs. First, a process (i.e., tool wear) curve is segmented in real time by one of iPACT's rule-based models. After every data entry (either sample or unit), a model predicts the risk of defects until the next inspection, the rate of tool wear related to the location of data averages, and sample variation. If the system determines that a risk of defects has increased, a compensation adjustment will be recommended; if tool-wear rate has dangerously increased, a tool change is advised (See figure).

A trend control model will stop a process at the point of accelerating tool wear and increased risk of defects. A model controlling the extent of tool wear will stop a process before severe tool deterioration occurs. Stable processes with relatively low tool-wear rates are controlled by separate estimates of the probability of defects on the lower and upper specification limits. Sampling time is critical and is adjusted dynamically after each measurement; a stable process with little variation would require a longer sampling interval than a process with large variation and high-rate tool wear.

When a tool is indexed, sharpened or changed, you can't expect duplication of a process curve; all tool-wear processes are nonrepetitive. This means that all parameters, such as sample variation, tool-wear slopes, rate of tool-wear acceleration and a number of mini-processes, will probably change. Therefore, a new model is needed in order to control a new process. Whenever the user indicates a process change, iPACT will start a new modeling cycle. Only when mature and long-running processes repeat will iPACT use historical data and statistical process control for process control decisions.

Predictive trending and adaptive sampling intervals ensure that a machine is operated to its maximum capacity. Tools achieve maximum usage before change-out, and sampling is performed only when needed. Both of these decrease waste and increase machine uptime and operator efficiency.

Control of operations with multiple tool-bonded processes

Let's look at how predictive modeling works with different tools and process types.

An ideal condition for process control exists when a finishing tool creates only one critical characteristic and a process can be easily adjusted to nominal specifications. If metal cutting were only this, SPC would be an obvious solution. However, most metal-removal and -forming operations are designed to create multiple characteristics with a single finishing tool. These multiple tool-bonded processes are divided between the following:

Type one--Single-point and simple-shape tool (e.g., insert, mill or reamer). Individual characteristics are cut sequentially using the same tool. A process control solution must be able to control for all misaligned tool-bonded characteristics and control primary variation.

Type two--Step tool (e.g., form tool, step drill or grinding wheel). Individual characteristics are cut simultaneously. A process control solution must control for all misaligned tool-bonded characteristics, different tool-wear rates and primary variation.

Type one is considered a single process with multiple outputs (i.e., one tool cutting several dimensions). Type two is considered multiple processes with multiple outputs (i.e., a step-wise tool generating related dimensions and tolerances). Tool-bonded processes of type one can be controlled by a key characteristic if misalignment and primary variation aren't significant. It's here that SPC can be used effectively. Typically, however, a close-tolerance metalworking operation faces the problem of misalignment between related characteristics and a substantial primary variation. All of these may be key characteristics that need to be controlled. In this case, iPACT uses multiple models to control each critical product characteristic, one model per characteristic.

Step tools, or type two, are even more problematic, having two or more critical characteristics with different dynamic patterns in terms of tool-wear rates, variation and the location of sample averages. If more than one process is running on a complex tool-cutting edge, then iPACT uses a multiprocess model. This forces the optimization of internal specifications for tool-bonded characteristics and helps develop solutions for maximum tool efficiency.

Six-level data organization

Although we've been considering the use of predictive modeling at the part level, it's most effective when applied across the entire plant. This is achieved when all workstations are networked and data is shared upstream.

The basic pattern of data organization is divided into six levels.

Production plant--At this level, the software provides an overview of all jobs and operations and alerts for problems.

Multioperation job--Here, data continuity is provided for all operations.

Metal-cutting or -forming machines--The equipment's capability to hold tolerance is verified by in-process capability control and off-line studies.

Operation--Control of individual and tool-bonded characteristics, along with roughing and finishing tools, is aimed at increased efficiency and limited machine attendance.

A group of tool-bonded characteristics--Reducing misalignment and variation leads to an extended time between tool adjustments and changes.

Individual characteristics--Compliance with specification, a sampling discipline and preventing tool failure is accomplished.

We've only briefly discussed this exciting new technology, which may be the only current viable solution for in-process metal-cutting and -forming control; in many instances, it does what SPC can't. Considering that in 1998, metal-cutting and -removal operations accounted for more than $400 billion of domestic expenditure, billions of dollars per year could be saved through waste reduction and increased machine and operator efficiencies using this technology.

About the author

Stephen Birman, Ph.D., is president of High Tech Research Inc. He has 20 years of experience developing expert-based control systems for discrete part manufacturing. He developed the concept of intelligent process adaptive control technology and led a team of engineers to create MICRONITE, a commercial implementation of the iPACT technology. Letters to the editor regarding this article should be e-mailed to letters@qualitydigest.com.