Minimization Method for 3D Surface Roughness Evaluation Area

Viktor Molnár

doi:10.3390/machines9090192

Abstract

3D surface roughness measurement is still a less mature procedure than its 2D version. The size of the evaluation area is not as standardized as the measurement length in the 2D version. The purpose of this study is to introduce a method for minimizing the evaluated surface area. This could help industrial applications in minimizing the time and cost of measurements. Machining experiments (hard turning and infeed grinding) and surface roughness measurements were carried out for automotive industrial parts to demonstrate the introduced method. Some frequently used roughness parameters were analyzed. Basic statistical calculations were applied to analyze the relationship between the surface area and the roughness parameter values and regression analyses were applied to validate the results in case of the applied technological data. The main finding of the study is that minimum evaluation areas can be clearly designated and, depending on the different roughness parameter–procedure version, different evaluation sizes (Sa: 1.3 × 1.3 mm; Sq: 1.4 × 1.4 mm; Ssk and Sku: 2 × 2 m; Sp and Sv: 1.7 × 1.7 mm) are recommended.

Highlights

Surface roughness of machined surfaces has a high impact on the working characteristics [1,2] and wear and on the lifetime of the components built into automotive industrial products
Minimum evaluation areas were designated for each analyzed roughness parameter and regression analyses were applied for validating the results
There is no clear tendency in the analyzed roughness values when the evaluation area is decreased: the data show oscillation/periodicity in some cases and in others decreasing values or show irregularities

Summary

Introduction

Surface roughness of machined surfaces has a high impact on the working characteristics [1,2] and wear and on the lifetime of the components built into automotive industrial products. The final surface topography depends on the tool and workpiece material pair, the tool geometry and the kinematic characteristics of the machining procedure. This effect can clearly be observed by studying theoretical roughness parameter values in machining by fixed [3] or rotational tools [4,5]. If a specified roughness value can be realized by grinding but—e.g., by hard turning too—after the appropriate determination of the cutting data, a more profound analysis is necessary to make the decision [6]. The deep analysis of surface textures can act as a potential predictor of machining performance [7,8]

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Conclusion