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"Sculptured surface machining technology is increasing its role in modern manufacturing. Products? surfaces become more complicated each day and require technology that is able to perform machining processes more accurately, precisely and take lesser time and cost. Nowadays, CAM helps so much on these matters. One of the difficulties in performing sculptured surface machining process is machining on feature which can not be reached by ordinary 3 axis machining process, called closed bounded volume (CBV).The thesis is discussing the development of algorithm that not only can analyze a model as a whole, but also can automatically detect CBV on a model, which is a 3D faceted model, through Paired Normal Vectors Bucketing (PNVB). And the algorithm also automatically generates tool path for multi axis roughing on the CBV. The algorithm development and the model analysis are worked based on faceted model in STL file format. As the result, the algorithm has successfully detected CBV in a complex-shaped model and listed the tool?s coordinates and orientations during machining process on the CBV."

"Kualitas permukaan produk hasil proses pemesinan adalah salah satu parameter penting dalam proses manufaktur. Metode yang paling umum untuk mengukur nilai kekasaran permukaan adalah metode kontak mekanik antara pergerakan jarum dengan permukaan produk. Metode ini memiliki banyak kelemahan karena bisa merusak permukaan poduk dan cenderung lama. Untuk itu maka dikembangkan teknologi optik-elektrik yang mampu mengevaluasi kekasaran permukaan berdasarkan image hasil identifikasi kamera digital.Penelitian ini bertujuan untuk mengidentifikasi fitur permukaan produk hasil pemesinan turning dan melakukan analisa korelasi dengan nilai kekasaran rata-ratanya (Ra). Material yang diuji adalah carbonsteel dengan diameter 20 mm dan panjang 100 mm sejumlah 10 sampel. Pengukuran kekasaran rata-rata (Ra) memakai stylus-profile meter. Identifikasi profil permukaan menggunakan kamera digital Canon EOS 350D yang terhubung pada mikroskop dengan perbesaran 100 kali. Pencahayaan yang digunakan adalah 10 buah LED warna putih dengan sudut pencahayaan sebesar 45°. Software yang digunakan untuk melakukan image processing adalah Matlab.Hasil yang dicapai menunjukan adanya pola yang khas pada image berupa garis hitam dan putih yang bervariasi. Lebar garis putih, jarak antar garis putih dan grafik histogram warna menunjukan adanya korelasi dengan nilai kekasaran rata-ratanya.

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Surface quality of machined-part is an important parameter in manufacturing process. Recently, measuring of surface roughness is commonly performed by mechanical contact between stylus and product surface. However, this method is not fast enough and can potentialy damage the product. Therefore, a different method, which is used here, relied on optic-electric relationship has been developed based on digital camera images.

The objective of current study is to identify the surface features of turned-parts machining and their correlation with respect to Roughness average (Ra) of stylus-profile meter. Ten samples of carbonsteel specimen, i.e., 100 mm length and 20 mm wide, are used during experiment. The identification of surface features is done by Canon EOS 350D digital camera and 100 times microscope magnification using 10 white LED and 45 degrees angle lighting. Sample images produced by the identification is then processed in Matlab.

Finally, a unique pattern, i.e., black and white line, can be observed on the processed images which indicates correlation with roughness average."