Turning To Surface Quality Optimization

This article covers the fundamental principles of surface roughness and presents several tips proven to enhance surface quality in turning operations.

Understanding Surface Quality

Surface quality, commonly referred to as surface roughness, provides a numerical value representing how smooth a surface appears. This measurement can be challenging since it converts a three-dimensional visual characteristic into a single numerical value.

Three primary approaches exist for obtaining surface roughness measurements.

• Mechanical Scanning: The most widely employed technique for measuring surface quality involves scanning the surface using a stylus that documents the microscopic peaks and valleys across a linear path. This measurement generates a profile (see Surface Finish Scan chart below) that undergoes mathematical analysis to produce a surface quality value.
• Visual Assessment: An older conventional approach involves comparing a surface visually against a reference set of various surface quality standards using human observation.
• Optical Digital Scanning: A high-resolution digital camera linked to a computer conducts an optical examination of a surface area and computes the surface quality.

Surface Finish Scan

Surface Quality Measurement Units

Multiple units exist for measuring surface quality, with R_a and R_z being the most widely used. Given the frequent confusion between these units, let’s clarify their differences.

R_a, known as the roughness average, is the most frequently used unit. According to ASME B46.1, R_a represents the arithmetic mean of the absolute values of profile height variations from the centerline, measured within the evaluation length. Using the same parameters that correspond with those shown in the Surface Finish Scan chart, the calculation follows the formula shown above.

R_z, is the “Peak to Valley” average, and is the second most common unit. The calculation involves measuring the vertical distance between the highest peak and the lowest valley within the scanned profile. The top five peaks and the five deepest valleys are measured, and then these distances are averaged. R_z consistently produces higher values than R_a. The calculation uses the formula shown below.

A frequent question from machinists and engineers concerns the conversion of R_a to R_z values. Direct conversion from R_a to R_z (or R_z to R_a) is impossible since they represent different characteristics.

This would be like asking, “How can I convert height to weight?” Height cannot be converted directly to weight, but rough estimations based on statistical data are possible. For example, you can say with 99% probability that a 6-foot-tall person typically weighs between 140 and 290 pounds. Weights of 100 or 400 pounds would be highly unlikely.

For R_a to R_z conversions or vice versa, statistical analysis enables a reasonable prediction of the R_z range for a specific R_a value. You can use conversion charts or an online R_a/R_z conversion tool.