應用機器視覺方法於鋼珠表面瑕疵之檢測

An application of machine vision for steel ball surface inspection

張翔昇(Hsiang-Sheng Chang)

指導教授 ： 蔡篤銘

元智大學/工程學院/工業工程與管理學系/碩士(2004年)

摘要

鋼珠（Steel ball）的應用普遍存在於多種日常用品中，而在精密工業中更是一個不可或缺的元件。目前鋼珠主要應用於精密軸承（Bearing）之製造，鋼珠的表面品質與精密度將主宰軸承運轉的磨耗情形以及使用壽命，進而影響整個機械設備的功能，因而顯示鋼珠品質檢測的重要性。鋼珠表面瑕疵包含表面凹陷、過度粗糙以及因金屬疲勞產生的碎裂等，在傳統的檢測方法中多採用人眼檢視方式，容易因疲勞及個人主觀判斷造成誤判或漏檢，因此本研究提出以自動視覺檢測方法改善人眼檢視所造成之缺失。
鋼珠具金屬表面的高反射性，以及形狀為一球面的特性，在本研究中應用機器視覺方法取像時，將探討最佳光源、鏡頭的選擇以及環境的設定，以適合檢測。本研究使用二種方法檢測鋼珠表面之瑕疵，其一為灰階對比度擴展法（Contrast stretching），並以熵函數法（Entropy）進行二值化分割，以區別正常區域與瑕疵；另一方法為灰階切片法（Gray-level slicing）保留瑕疵範圍灰階資訊，再以視窗檢測方法計算視窗內灰階的平均值與變異數，訂定一瑕疵分割臨界值，便可得到瑕疵區塊的面積以及個數。判斷鋼珠好壞的方式，採計算最大瑕疵面積以及平均瑕疵面積兩量測標準進行判定。本研究在適當的影像尺寸與解析度之下，可取得一張包含四顆鋼珠之影像進行檢測，使減少重複取像次數，提高檢測工作之效率。

關鍵字

鋼珠 ； 機器視覺 ； 表面瑕疵檢測 ； 灰階對比度擴展 ； 灰階切片

並列摘要

Steels ball are important elements in a precision machine, especially as components of stainless steel bearings and automotive parts. The quality of steel balls makes a great impact on the operating condition and the life cycle of the composed product. Traditionally, the inspection of steel balls in the manufacturing process requires highly experienced operators with their naked eyes; this method is out of time nowadays. In this research, we propose a machine vision method to tackle the deficiency of manual vision inspection and to achieve high effectiveness and efficiency of automatic inspection.
Owing to the high reflectance and the spherical surface of steel balls, the inspection lighting must be selected appropriately to acquire an optimum image. A ring-shaped LED light along with a dome-shaped unite reflective board is employed to eliminate the reflectance of light and enhance surface texture of the inspection steel ball. The resulting inspection region is a ring-like area under the experiment framework. In this thesis, we use two image enhancement techniques (contrast stretching and gray-level slicing) to stress the difference of gray-level information between the defect and normal areas. According to the contrast stretching and gray-level slicing results, we use Entropy method and gray-level statistics in a neighborhood window, respectively, to compute a threshold value for distinguishing defect areas from the background. Since the surface roughness consists of irregularities such as large scaled crack, dent, cuts and highly concentrated small defects, we compute the maximum defect blob area and average defect blob area as two quantitative measures to determine whether the steel ball under inspection is good or bad. In order to improve the inspection rate, multiple steel balls inspected in the same image is also evaluated using the proposed method. Experimental results have shown that the proposed two inspection methods are able to detect the defects of steel ball effectively for both single and multiple steel ball inspections.

並列關鍵字

Steel Ball ； Machine Vision ； Surface Inspection ； Contrast Stretching ； Gray-level Slicing

參考文獻

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