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Separating breast meat with low water-holding capacity, conformation parameters (thickness, volume, bottom sarea, and perimeter), and color of chicken breast meat were measured by direct measurement and by imaging analysis with a digital camera. Samples were obtained from a production line. The L* value was used to separate the samples by three characteristics designating the quality of the meat: dark-colored samples (L*＜50), normal-colored samples (50≤L*≤56), and light-colored samples (L*＞56). Light-colored samples had higher moisture content, thawing loss, drip loss, and lower pH compared with those of normal-and dark-colored samples. Lower thickness was observed in the light-colored samples compared with those of normal-and dark-colored samples. Light-and normalcolored samples had a greater volume of meat than did the dark-colored samples. Imaging analysis showed that lightcolored samples had a greater bottom area and perimeter compared with those of normal-and dark-colored samples. However, these conformation parameters showed low correlation with water-holding capacity, which was determined as thawing and drip loss of the samples. Therefore, the conformation parameters, determined by direct measurement or imaging analysis, could not be used to predict the water-holding capacity of breast meat. Nevertheless, waterholding capacity showed high correlation with the L* value of breast meat. Imaging analysis could be used to separate light-colored breast meat with mostly low water-holding capacity. The accuracy of determining the characteristics of light-, normal-, and dark-colored samples by imaging analysis was evaluated. The characteristics of lightcolored samples were determined with higher accuracy by imaging analysis than were the characteristics of normaland dark-colored samples. This result indicated that imaging analysis using a digital camera could be used to separate light-colored breast meat with mostly low water-holding capacity from normal-and dark-colored meat.

The main objective of this paper is to propose a new machining feature definition model focused on 5D-machining features. The machining features describing a Prisronal part, which includes both prismatic and rotational features, are introduced. The unique model is not only taking into account geometric entities, manufacturing aspects but also including machining processes and knowledge-based rules for intelligent process planning system of five-axis lathe. Geometric entities are specified by defining feature parameters corresponding to the shape of feature. Manufacturing aspects include properties of blank part, settings up and technological data like tolerances and surface finishing. Machining processes and knowledge-based rules attached with each feature are used as constraints to guide the system for automatically selecting suitable machining operations. Various examples of 3D & 5D-machining feature attached with knowledge-based for machining those features by five-axis lathe are shown. Finally, the pilot implementation of machining features for operation selection is demonstrated using the expert system shell named Visual Rule Studio TM from Rule Machines corporation.

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Although widely used in construction and industrial applications, wood is more prone to defects of different kinds than other materials. These defects are unpredictable and differing randomly from plank to plank. This uncertain nature of the defects complicates establishment of manufacturing plans. In this study, a probabilistic model of wood defects was constructed as a function of three variables which were quantity of defects, position of defects and size of defects. The Kolmogorov-Smirnov hypotheses testing on distributional forms of these variables were carried out. Results showed that Poisson, uniform, and log-normal distributions were suitable to represent the variables statistically. Being knowledgeable of how the defects are distributed on the plank will be of benefit in profitability justification of a cutting plan.