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One way to extend the life time of the gear is minimizing the distortion during the manufacturing process. One of the most important processes in manufacturing to produce gears is heat treatment process. The purpose of this study is to analyze the distortion of the gear after heat treatment process. The material of gear is AISI 1045, and it was designed with the module (m) 1.75, and a number of teeth (z) 29. Gear was heat-treated in the furnace at a temperature of 800°C, holding time of 30 minutes, and then quenched in water. Furthermore, surface hardening process was also performed on gear teeth at a temperature of 820°C and holding time of 35 seconds and the similar procedure of analysis was conducted. The hardness of gear after heat treatment average 63.2 HRC and the teeth surface hardness after gear to induction hardening was 64.9 HRC at the case depth 1 mm. The microstructure of tested gear are martensitic and pearlite. The highest distortion on tooth thickness to upper than 0.063 can cause high precision at the tooth contact is not appropriate. Besides the shrinkage of tooth thickness will also affect to contact angle because the size of gear tolerance was not standardized. [ABSTRACT FROM AUTHOR]

Roller bearing is one of the vital parts of a rotating machine. Bearing failure can result in serious damage of the machine. This paper aims to develop a bearing fault diagnosis method using parameter evaluation technique to improve the diagnosis accuracy. The parameter evaluation technique is used to select five features that are used as predictors in multi-class support vector machine (SVM) classification. The purpose of this feature reduction was to avoid the curse of dimensionality and to increase the accuracy of the diagnosis. The diagnosis process was performed by classification of bearing states using one-against-one method multi-class SVM. Three types of kernel functions i.e., linear, polynomial, and Gaussian RBF were used in the SVM classification. The bearing conditions which is diagnosed in this paper were normal bearing, inner race fault, and outer race fault conditions. As a result, the classification performance of multiclass SVM using five selected features as the parameter have excellent performance in predict the bearing conditions data for all types of kernel functions. [ABSTRACT FROM AUTHOR]

This research studies the relationship between footprint depths and load in the calcaneal area when human standing in an upright posture. Footprint depths are deformation in the calcaneal area obtained from the z-value extraction of the Boolean operation acquired from unloaded foot scanning using 3D scanner and loaded foot using foot plantar scanner. To compare peak loads estimated from footprint depth maximum, force sensing resistor (FSR) sensor is attached over the shoe insole with zero heel height in the calcaneal area. Twenty participants were selected from students of Mechanical Engineering Department Diponegoro University with the average the age and the body weight 19.5 years and 55.27 kg respectively. Results that were relatively accurate was found on the calcaneal loading estimation by footprint depth is presented by curve and data distribution which are in good agreement with the result of the measurement. A significant difference in estimating calcaneal loading is mainly caused by plantar foot position of research subjects which is not perpendicular to foot ankle and hallux. In addition, plantar foot position which bends to front/back/side affects the result of footprint depths. [ABSTRACT FROM AUTHOR]

One way to extend the life time of the gear is minimizing the distortion during the manufacturing process. One of the most important processes in manufacturing to produce gears is heat treatment process. The purpose of this study is to analyze the distortion of the gear after heat treatment process. The material of gear is AISI 1045, and it was designed with the module (m) 1.75, and a number of teeth (z) 29. Gear was heat-treated in the furnace at a temperature of 800°C, holding time of 30 minutes, and then quenched in water. Furthermore, surface hardening process was also performed on gear teeth at a temperature of 820°C and holding time of 35 seconds and the similar procedure of analysis was conducted. The hardness of gear after heat treatment average 63.2 HRC and the teeth surface hardness after gear to induction hardening was 64.9 HRC at the case depth 1 mm. The microstructure of tested gear are martensitic and pearlite. The highest distortion on tooth thickness to upper than 0.063 can cause high precision at the tooth contact is not appropriate. Besides the shrinkage of tooth thickness will also affect to contact angle because the size of gear tolerance was not standardized. [ABSTRACT FROM AUTHOR]

Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing. [ABSTRACT FROM AUTHOR]