JP2017090453A - Surface roughness evaluation device and surface roughness evaluation method - Google Patents
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本発明は、不規則な粗さを有する物体表面の３次元形状を、より簡単な２次元計測により適切に把握・評価するために用いる表面粗さ評価装置及び表面粗さ評価方法に関する。 The present invention relates to a surface roughness evaluation apparatus and a surface roughness evaluation method used for appropriately grasping and evaluating a three-dimensional shape of an object surface having irregular roughness by simple two-dimensional measurement.
従来、線上で物体表面の粗さを計測（２次元計測）する装置は、接触式、非接触式など様々な原理によるものが知られている。市販されている表面粗さ計測装置には、例えば、取得した粗さ曲線を処理して、最大・最低高さ、１０点平均粗さ、粗さの実効値などといった統計量を計算して出力するものもある。 2. Description of the Related Art Conventionally, apparatuses that measure the roughness of an object surface on a line (two-dimensional measurement) are known based on various principles such as a contact type and a non-contact type. Commercially available surface roughness measuring devices, for example, process acquired roughness curves and calculate and output statistics such as maximum / minimum height, 10-point average roughness, and effective roughness value. Some will do.
しかしながら、このような装置では、忠実に３次元的な曲面形状を計測しているわけではなく、曲面形状を１つの断面で切断し、曲線を取り出して評価を加えている。
このことから、２次元的な線上計測は、実際の物体表面において３次元的な凸部の頂点近くを切断することもあれば、凸部の裾近くを切断することもあり、３次元的曲面を正しく代表するものではない。
However, in such an apparatus, the three-dimensional curved surface shape is not measured faithfully, but the curved surface shape is cut by one cross section, and the curve is taken out for evaluation.
From this, the two-dimensional linear measurement may cut near the apex of the three-dimensional convex part on the actual object surface, or may cut near the skirt of the convex part. Is not a correct representation of
２次元計測の切断面は、必ず３次元的な凸部の頂点を通る保証がないため、２次元計測で計測した凸部の最大値は、３次元的な凸部の最大値よりも小さくなってしまう。
一方で、十分な精度で３次元計測を行うためには、高価な装置を用いて莫大な表面形状データを取得する必要があり、測定時間も長くなってしまうため実用的ではない。
Since there is no guarantee that the cut surface of 2D measurement passes through the apex of the 3D projection, the maximum value of the projection measured by 2D measurement is smaller than the maximum value of the 3D projection. End up.
On the other hand, in order to perform three-dimensional measurement with sufficient accuracy, it is necessary to acquire enormous surface shape data by using an expensive apparatus, and the measurement time becomes long, which is not practical.
このため、より少ない表面形状データの計測から、妥当な確率・統計的処理を行って三次元的な曲面形状を算出する表面粗さ評価装置が求められている。
非特許文献１，２には、粒子又は液滴の直径の分布を、レーザーの光路上の弦（Chord）の確率密度関数から求めることが開示されている。しかしながら、非特許文献１，２に開示された方法は、粒子又は液滴を対象としたものであり、物体表面の粗度にそのまま適用することができるものではない。
For this reason, there is a need for a surface roughness evaluation apparatus that calculates a three-dimensional curved surface shape by performing reasonable probability / statistical processing from measurement of less surface shape data.
Non-Patent Documents 1 and 2 disclose that the diameter distribution of particles or droplets is obtained from a probability density function of a chord on the optical path of a laser. However, the methods disclosed in Non-Patent Documents 1 and 2 are intended for particles or droplets, and cannot be directly applied to the roughness of the object surface.
本発明では、このような現状に鑑み、２次元計測により得られたデータに基づき、迅速に被測定物体表面の３次元形状を予測・評価することが可能な表面粗さ評価装置及び表面粗さ評価方法を提供することを目的とする。 In the present invention, in view of such a current situation, a surface roughness evaluation apparatus and a surface roughness capable of quickly predicting and evaluating the three-dimensional shape of the surface of the object to be measured based on data obtained by two-dimensional measurement. The purpose is to provide an evaluation method.
本発明は、前述するような従来技術における課題を解決するために発明されたものであって、本発明の表面粗さ評価装置は、
被測定物体表面の粗さを評価する表面粗さ評価装置であって、
前記被測定物体表面を２次元的に測定可能な変位計と、
前記変位計により得られた前記被測定物体表面の粗さ曲線データに基づき被測定物体表面の３次元形状を算出する演算手段と、を備え、
前記演算手段は、
前記粗さ曲線データのピーク高さHと、その裾野幅2Rとを順次求め、
複数のピーク高さHと、その裾野幅2RとからH/R比の平均値を算出し、
得られたピーク高さのヒストグラムを近似して得られた２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、３次元確率密度関数3fh(3hpeak)を導出するように構成されていることを特徴とする。
The present invention was invented to solve the problems in the prior art as described above, and the surface roughness evaluation apparatus of the present invention is
A surface roughness evaluation apparatus for evaluating the roughness of the surface of an object to be measured,
A displacement meter capable of two-dimensionally measuring the surface of the object to be measured;
Calculating means for calculating a three-dimensional shape of the surface of the object to be measured based on roughness curve data of the surface of the object to be measured obtained by the displacement meter,
The computing means is
Obtain the peak height H of the roughness curve data and its base width 2R sequentially,
Calculate the average value of the H / R ratio from multiple peak heights H and its base width 2R,
Based on the two-dimensional probability density function 2 f h ( 2 h peak ) obtained by approximating the obtained peak height histogram and the average value of the H / R ratio, the three-dimensional probability density function 3 f h It is configured to derive ( 3 h peak ).
この場合、前記演算手段は、
前記被測定物体の表面粗さを、円錐形、半球形、回転楕円体のいずれかと仮定して、前記２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、前記３次元確率密度関数3fh(3hpeak)を導出するように構成することが好ましい。
In this case, the computing means is
Assuming that the surface roughness of the object to be measured is conical, hemispherical, or spheroid, the two-dimensional probability density function 2 f h ( 2 h peak ) and the average value of the H / R ratio The three-dimensional probability density function 3 f h ( 3 h peak ) is preferably derived based on the above.
また、前記演算手段は、
前記２次元確率密度関数2fh(2hpeak)と、対数正規分布関数又は正規分布関数とに基づき、前記３次元確率密度関数3fh(3hpeak)を導出するように構成することが好ましい。
Further, the calculation means includes:
The three-dimensional probability density function 3 f h ( 3 h peak ) is derived based on the two-dimensional probability density function 2 f h ( 2 h peak ) and a lognormal distribution function or a normal distribution function. Is preferred.
また、本発明の表面粗さ評価装置では、前記演算手段により、
前記３次元確率密度関数3fh(3hpeak)に基づき、前記被測定物体表面の高さ、ピークの個数、断面積のうち少なくともいずれかを算出することができる。
Moreover, in the surface roughness evaluation apparatus of the present invention, by the calculation means,
Based on the three-dimensional probability density function 3 f h ( 3 h peak ), at least one of the height, the number of peaks, and the cross-sectional area of the surface of the object to be measured can be calculated.
また、本発明の表面粗さ評価方法は、
被測定物体表面の粗さを評価する表面粗さ評価方法であって、
前記被測定物体表面を２次元的に測定し、前記被測定物体表面の粗さ曲線データを取得する工程と、
前記粗さ曲線データのピーク高さHと、その裾野幅2Rとを順次求める工程と、
複数のピーク高さHと、その裾野幅2RとからH/R比の平均値を算出する工程と、
得られたピーク高さのヒストグラムを近似して得られた２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、３次元確率密度関数3fh(3hpeak)を導出する工程と、を備えることを特徴とする。
The surface roughness evaluation method of the present invention is
A surface roughness evaluation method for evaluating the roughness of an object surface to be measured,
Measuring the surface of the object to be measured two-dimensionally and obtaining roughness curve data of the surface of the object to be measured;
A step of sequentially obtaining the peak height H of the roughness curve data and its base width 2R,
A step of calculating an average value of the H / R ratio from a plurality of peak heights H and the base width 2R,
Based on the two-dimensional probability density function 2 f h ( 2 h peak ) obtained by approximating the obtained peak height histogram and the average value of the H / R ratio, the three-dimensional probability density function 3 f h Deriving ( 3 h peak ).
この場合、前記被測定物体の表面粗さを、円錐形、半球形、回転楕円体のいずれかと仮定して、前記２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、前記３次元確率密度関数3fh(3hpeak)を導出することが好ましい。 In this case, assuming that the surface roughness of the object to be measured is conical, hemispherical, or spheroid, the two-dimensional probability density function 2 f h ( 2 h peak ) and the H / R ratio It is preferable to derive the three-dimensional probability density function 3 f h ( 3 h peak ) based on the average value.
また、前記２次元確率密度関数2fh(2hpeak)と、対数正規分布関数又は正規分布関数とに基づき、前記３次元確率密度関数3fh(3hpeak)を導出することが好ましい。
また、前記３次元確率密度関数3fh(3hpeak)に基づき、前記被測定物体表面の高さ、ピークの個数、断面積のうち少なくともいずれかを算出することができる。
The three-dimensional probability density function 3 f h ( 3 h peak ) is preferably derived based on the two-dimensional probability density function 2 f h ( 2 h peak ) and a lognormal distribution function or a normal distribution function. .
Further, based on the three-dimensional probability density function 3 f h ( 3 h peak ), at least one of the height of the surface of the object to be measured, the number of peaks, and the cross-sectional area can be calculated.
本発明によれば、３次元計測に比べて計測データの少ない２次元計測により得られた粗さ曲線データに基づき、被測定物体表面の３次元形状を表す３次元確率密度関数3fh(3hpeak)を導出するため、被測定物体表面の粗さを低コストかつ迅速に予測・評価することができる。 According to the present invention, the three-dimensional probability density function 3 f h ( 3 representing the three-dimensional shape of the surface of the object to be measured is based on roughness curve data obtained by two-dimensional measurement with less measurement data than three-dimensional measurement. Since h peak ) is derived, the roughness of the surface of the object to be measured can be predicted and evaluated quickly at low cost.
以下、本発明の実施の形態（実施例）を図面に基づいてより詳細に説明する。
図１は、本実施例における表面粗さ評価装置を説明するための概略構成図、図２は、図１の表面粗さ評価装置の変位計によって得られた粗さ曲線データの一部を示すグラフである。
Hereinafter, embodiments (examples) of the present invention will be described in more detail with reference to the drawings.
FIG. 1 is a schematic configuration diagram for explaining a surface roughness evaluation apparatus in the present embodiment, and FIG. 2 shows a part of roughness curve data obtained by a displacement meter of the surface roughness evaluation apparatus in FIG. It is a graph.
図１に示すように、本実施例の表面粗さ評価装置１０は、被測定物体表面１６を２次元的に点測定可能な変位計１２と、変位計１２により得られた粗さ曲線データに基づき被測定物体表面の３次元形状を算出する演算手段１４とを備えている。 As shown in FIG. 1, the surface roughness evaluation apparatus 10 according to the present embodiment uses a displacement meter 12 capable of two-dimensionally measuring a measured object surface 16 and roughness curve data obtained by the displacement meter 12. And a calculation means 14 for calculating a three-dimensional shape of the surface of the object to be measured based on the calculation object 14.
なお、変位計１２は、被測定物体表面１６を点測定可能であれば特に限定されるものではなく、接触式変位計であっても、非接触式変位計であっても構わない。被測定物体表面１６を安定的に精度良く評価するためには、非接触式変位計が好ましく、特に、レーザー式変位計（例えば、キーエンス社製超高速インラインプロファイル測定器（LJ-V7200）など）が好ましい。 The displacement meter 12 is not particularly limited as long as it can measure the surface 16 of the object to be measured, and may be a contact displacement meter or a non-contact displacement meter. In order to stably and accurately evaluate the object surface 16 to be measured, a non-contact displacement meter is preferable, and in particular, a laser displacement meter (for example, an ultra-high speed inline profile measuring instrument (LJ-V7200) manufactured by Keyence Corporation). Is preferred.
演算手段１４は、変位計１２によって得られた粗さ曲線データに基づき、後述するような演算が可能であれば特に限定されるものではなく、例えば、パーソナルコンピュータなどを用いることができる。 The calculation means 14 is not particularly limited as long as calculation as described later is possible based on the roughness curve data obtained by the displacement meter 12, and for example, a personal computer or the like can be used.
以下、本実施例の表面粗さ評価装置１０を用いて、被測定物体表面１６の３次元形状を算出する流れを説明する。
まず、変位計１２によって、被測定物体表面１６を直線上に走査し、２次元的な線上計測を行う。これによって、図２に示すように、粗さ曲線データが得られる。なお、図２に示す粗さ曲線データは、塗料を塗布して人為的に粗さを生成した被測定物体表面１６を変位計１２によって走査して得られたものである。
Hereinafter, the flow of calculating the three-dimensional shape of the surface 16 to be measured using the surface roughness evaluation apparatus 10 of the present embodiment will be described.
First, the object surface 16 to be measured is scanned on a straight line by the displacement meter 12 to perform two-dimensional linear measurement. As a result, roughness curve data is obtained as shown in FIG. Note that the roughness curve data shown in FIG. 2 is obtained by scanning the object surface 16 to be measured, which has been artificially generated by applying a paint, with the displacement meter 12.
次いで、図３に示すように、図２に示す粗さ曲線データに現れるピークを特定し、その高さ2hpeakを求め、このピーク高さを整理してヒストグラムを計算する。ここで、図３中のdhはヒストグラムの１区分の幅であり、ヒストグラムの計算とは、この区分に納まるピークの数を計数することを言う。本実施例では、ヒストグラムの横軸となる区分数は５０とした。 Next, as shown in FIG. 3, a peak appearing in the roughness curve data shown in FIG. 2 is specified, its height 2 h peak is obtained, and the histogram is calculated by organizing this peak height. Here, dh in FIG. 3 is the width of one section of the histogram, and the calculation of the histogram means counting the number of peaks that fall within this section. In the present embodiment, the number of sections on the horizontal axis of the histogram is 50.
そして、ヒストグラムの形を連続関数で近似し、下記式（１）を満たすように縦軸を調整して、図４に示すように、２次元確率密度関数2fh(2hpeak)を導出する。
別途、図２に示す粗さ曲線データに現れるピーク高さHと、その裾野幅2Rとを順次求め、複数のデータに基づいて、H/R比の平均値を算出する。
Then, the shape of the histogram is approximated by a continuous function, the vertical axis is adjusted to satisfy the following formula (1), and a two-dimensional probability density function 2 f h ( 2 h peak ) is derived as shown in FIG. To do.
Separately, the peak height H appearing in the roughness curve data shown in FIG. 2 and its base width 2R are sequentially obtained, and the average value of the H / R ratio is calculated based on a plurality of data.
確率・統計理論に基づき、被測定物体の表面粗さを円錐形状であるとの仮定のもとに、２次元対数正規分布の連続関数である２次元確率密度関数2fh(2hpeak)と、H/R比の平均値とに基づき、下記式（２）により、図５に示すような３次元確率密度関数3fh(3hpeak)を導出する。 Two-dimensional probability density function 2 f h ( 2 h peak ), which is a continuous function of a two-dimensional lognormal distribution, on the assumption that the surface roughness of the measured object is conical based on probability / statistical theory Based on the average value of the H / R ratio, a three-dimensional probability density function 3 f h ( 3 h peak ) as shown in FIG.
なお、本実施例では、３次元確率密度関数3fh(3hpeak)は対数正規分布に従うものと仮定しているが、３次元確率密度関数3fh(3hpeak)が正規分布に従うものと仮定して導出することもできる。
すなわち、２次元確率密度関数2fh(2hpeak)と、対数正規分布関数又は正規分布関数とに基づき、３次元確率密度関数3fh(3hpeak)を導出することができる。
ただし、対数正規分布を規定するパラメータである（ａ，μ，σ）が、2fh(2hpeak)から推算されることになる。また、区分数は図４に示す２次元ヒストグラムと対応するように、横軸3hpeak上では５０で計算を行っている。
In this embodiment, it is assumed that the three-dimensional probability density function 3 f h ( 3 h peak ) follows a lognormal distribution, but the three-dimensional probability density function 3 f h ( 3 h peak ) follows a normal distribution. It can also be derived assuming that.
That is, the three-dimensional probability density function 3 f h ( 3 h peak ) can be derived based on the two-dimensional probability density function 2 f h ( 2 h peak ) and the lognormal distribution function or the normal distribution function.
However, parameters (a, μ, σ) that define a lognormal distribution are estimated from 2 f h ( 2 h peak ). Further, the number of sections is calculated at 50 on the horizontal axis 3 h peak so as to correspond to the two-dimensional histogram shown in FIG.
また、本実施例では、被測定物体の表面粗さを、円錐形状と仮定しているが、これに限定されるものではなく、半球形や回転楕円体と仮定して、２次元確率密度関数2fh(2hpeak)と、H/R比の平均値とに基づき、３次元確率密度関数3fh(3hpeak)を導出するようにしてもよい。 In this embodiment, the surface roughness of the object to be measured is assumed to be a conical shape. However, the present invention is not limited to this, and a two-dimensional probability density function is assumed assuming a hemispherical shape or a spheroid. A three-dimensional probability density function 3 f h ( 3 h peak ) may be derived based on 2 f h ( 2 h peak ) and the average value of the H / R ratio.
このようにして得られた３次元確率密度関数3fh(3hpeak)の中で、確率密度が０でない値を持つ範囲を区切る横軸の上限値3hpeakが、３次元曲面の凸部の最大高さ3hpeak_maxに相当する。 In the 3D probability density function 3 f h ( 3 h peak ) obtained in this way, the upper limit 3 h peak on the horizontal axis that delimits the range where the probability density is non-zero is the convexity of the 3D surface. This corresponds to a maximum height of 3 h peak _ max .
また、３次元的ピーク高さのある区分3hpeakにおける確率密度3fh(3hpeak)は、その高さの凸部が、被測定物体表面１６の面内に何個存在しているかを表している。例えば、A(m2)の面積におけるそのピークの個数はA×3fh(3hpeak)であり、ピーク同士の平均的間隔はA/3fh(3hpeak)の平方根で与えられる。 The probability density 3 f h ( 3 h peak ) in the section 3 h peak with a three-dimensional peak height indicates how many convex portions of that height exist in the plane of the object surface 16 to be measured. Represents. For example, the number of peaks in the area of A (m 2 ) is A × 3 f h ( 3 h peak ), and the average interval between peaks is given by the square root of A / 3 f h ( 3 h peak ) .
このように、３次元曲面の特性は、３次元ピークの確率密度分布と、H/R比とによって代表されるため、３次元確率密度関数3fh(3hpeak)に基づき、被測定物体表面１６の高さ、ピークの個数、断面積などといった特性値を必要に応じて算出することもできる。 Thus, since the characteristics of the three-dimensional curved surface are represented by the probability density distribution of the three-dimensional peak and the H / R ratio, the measured object is based on the three-dimensional probability density function 3 f h ( 3 h peak ). Characteristic values such as the height of the surface 16, the number of peaks, and the cross-sectional area can be calculated as necessary.
以上、本発明の好ましい実施例を説明したが、本発明はこれに限定されることはなく、本発明の目的を逸脱しない範囲で種々の変更が可能である。 The preferred embodiment of the present invention has been described above, but the present invention is not limited to this, and various modifications can be made without departing from the object of the present invention.
なお、本発明の信頼性を検証するために、以下のような確認実験を行った。
被測定物体表面１６の３次元曲面の形状を正確に測るために、面測定が可能なレーザー変位計（キーエンス社製超高速インラインプロファイル測定器（LJ-V7200））を用いて、被測定物体表面１６を走査して面測定を行い、図６に示すように、面的な粗さ形状データを取得した。
In order to verify the reliability of the present invention, the following confirmation experiment was conducted.
In order to accurately measure the shape of the three-dimensional curved surface of the object surface 16 to be measured, the surface of the object to be measured is measured using a laser displacement meter that can measure the surface (Ultra High Speed Inline Profile Measuring Machine (LJ-V7200) manufactured by Keyence). Surface measurement was performed by scanning 16 to obtain surface roughness shape data as shown in FIG.
この粗さ形状データからピークを探索し、それぞれのピーク高さを集積して、図７に示すようにヒストグラムを作成した。
このように実測した３次元計測により得られたピーク高さのヒストグラムは、図５に示す２次元の粗さ曲線データから導出された３次元確率密度関数とよく一致していることから、本発明の表面粗さ評価装置及び表面粗さ評価方法により、被測定物体表面１６の３次元形状を精度良く予測・評価することが可能であると言える。
Peaks were searched from this roughness shape data, and the respective peak heights were accumulated to create a histogram as shown in FIG.
The histogram of the peak height obtained by the three-dimensional measurement actually measured as described above is in good agreement with the three-dimensional probability density function derived from the two-dimensional roughness curve data shown in FIG. It can be said that the three-dimensional shape of the surface 16 of the object to be measured can be accurately predicted and evaluated by the surface roughness evaluation apparatus and the surface roughness evaluation method.
１０ 表面粗さ評価装置
１２ 変位計
１４ 演算手段
１６ 被測定物体表面
DESCRIPTION OF SYMBOLS 10 Surface roughness evaluation apparatus 12 Displacement meter 14 Calculation means 16 Object surface to be measured
Claims (8)
前記被測定物体表面を２次元的に測定可能な変位計と、
前記変位計により得られた前記被測定物体表面の粗さ曲線データに基づき被測定物体表面の３次元形状を算出する演算手段と、を備え、
前記演算手段は、
前記粗さ曲線データのピーク高さHと、その裾野幅2Rとを順次求め、
複数のピーク高さHと、その裾野幅2RとからH/R比の平均値を算出し、
得られたピーク高さのヒストグラムを近似して得られた２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、３次元確率密度関数3fh(3hpeak)を導出するように構成されていることを特徴とする表面粗さ評価装置。 A surface roughness evaluation apparatus for evaluating the roughness of the surface of an object to be measured,
A displacement meter capable of two-dimensionally measuring the surface of the object to be measured;
Calculating means for calculating a three-dimensional shape of the surface of the object to be measured based on roughness curve data of the surface of the object to be measured obtained by the displacement meter,
The computing means is
Obtain the peak height H of the roughness curve data and its base width 2R sequentially,
Calculate the average value of the H / R ratio from multiple peak heights H and its base width 2R,
Based on the two-dimensional probability density function 2 f h ( 2 h peak ) obtained by approximating the obtained peak height histogram and the average value of the H / R ratio, the three-dimensional probability density function 3 f h A surface roughness evaluation apparatus configured to derive ( 3 h peak ).
前記被測定物体の表面粗さを、円錐形、半球形、回転楕円体のいずれかと仮定して、前記２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、前記３次元確率密度関数3fh(3hpeak)を導出するように構成されていることを特徴とする請求項１に記載の表面粗さ評価装置。 The computing means is
Assuming that the surface roughness of the object to be measured is conical, hemispherical, or spheroid, the two-dimensional probability density function 2 f h ( 2 h peak ) and the average value of the H / R ratio The surface roughness evaluation apparatus according to claim 1, wherein the three-dimensional probability density function 3 f h ( 3 h peak ) is derived based on
前記２次元確率密度関数2fh(2hpeak)と、対数正規分布関数又は正規分布関数とに基づき、前記３次元確率密度関数3fh(3hpeak)を導出するように構成されていることを特徴とする請求項１又は２に記載の表面粗さ評価装置。 The computing means is
Based on the two-dimensional probability density function 2 f h ( 2 h peak ) and a lognormal distribution function or a normal distribution function, the three-dimensional probability density function 3 f h ( 3 h peak ) is derived. The surface roughness evaluation apparatus according to claim 1, wherein the apparatus is a surface roughness evaluation apparatus.
前記３次元確率密度関数3fh(3hpeak)に基づき、前記被測定物体表面の高さ、ピークの個数、断面積のうち少なくともいずれかを算出するように構成されていることを特徴とする請求項１から３のいずれかに記載の表面粗さ評価装置。 The computing means is
Based on the three-dimensional probability density function 3 f h ( 3 h peak ), at least one of the height, the number of peaks, and the cross-sectional area of the surface of the object to be measured is calculated. The surface roughness evaluation apparatus according to any one of claims 1 to 3.
前記被測定物体表面を２次元的に測定し、前記被測定物体表面の粗さ曲線データを取得する工程と、
前記粗さ曲線データのピーク高さHと、その裾野幅2Rとを順次求める工程と、
複数のピーク高さHと、その裾野幅2RとからH/R比の平均値を算出する工程と、
得られたピーク高さのヒストグラムを近似して得られた２次元確率密度関数2fh(2hpeak)と、前記H/R比の平均値とに基づき、３次元確率密度関数3fh(3hpeak)を導出する工程と、を備えることを特徴とする表面粗さ評価方法。 A surface roughness evaluation method for evaluating the roughness of an object surface to be measured,
Measuring the surface of the object to be measured two-dimensionally and obtaining roughness curve data of the surface of the object to be measured;
A step of sequentially obtaining the peak height H of the roughness curve data and its base width 2R,
A step of calculating an average value of the H / R ratio from a plurality of peak heights H and the base width 2R,
Based on the two-dimensional probability density function 2 f h ( 2 h peak ) obtained by approximating the obtained peak height histogram and the average value of the H / R ratio, the three-dimensional probability density function 3 f h And ( 3 h peak ) deriving steps.
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