Yubao Chen

Feature-based part modeling and process planning for rapid response manufacturing

Rapid Response Manufacturing (RRM) attempts to put the product manufacturing into effect in the minimum time to satisfy the customer requirements. This paper presents the methodologies of dynamic and integrated part feature modeling and process planning for RRM applications. In order to demonstrate the functions of such system, an integrated model of rotational part feature modeling and process planning has been developed. The part feature modeling is constructed on the basis of a hierarchical and dynamic structure that consists of feature syntax, feature semantics and feature operations. For part comparison in RRM, a two-level comparison strategy has proposed and introduced. The process planning module can fulfill both variant and generative process design tasks, and perform part manufacturability evaluation by using a multi-level knowledge reasoning. To support the integration of RRM, a manufacturing environment model was built. An engine crankshaft was selected as the test case. The implementation and sensitivity of the model which was demonstrated by using the crankshaft is included in the paper.

Integrated diagnosis using information-gain-weighted radial basis function neural networks

A new approach, the information-gain-weighted radial basis function neural network (RBFNN), has been proposed for machinery diagnosis in a manufacturing environment. This approach is based on the composite neural network, in which a series of RBFNNs are integrated together to perform the task of classification. Each RBFNN has only one output node and is treated as a sub-network. Unlike the conventional RBFNNs, in which only the outputs of hidden nodes are weighted, the information-gain-weighted RBFNN uses a weighting vector also in its input layer. In addition, the weighting vector is obtained according to the information gain of each input index to the process of diagnosis. In the diagnosis strategy, one sub-net is responsible for diagnosing one specific fault, while the composite network as a whole can diagnose different kinds of faults. By this approach, classification among known faults can be made and a novel fault, if any, can also be identified. This approach has been tested in the diagnosis of an internal thread tapping process. The results showed that the information-gain-weighted RBFNN can produce better distinction between conditions and the scheme of a composite neural network is indeed an improved structure for machinery diagnosis in the manufacturing environment.

Evaluation of Minimum Zone Straightness by a Nonlinear Optimization Method

In this paper, a new algorithm, based on a nonlinear optimization method (NOM), has been developed. The accuracy as well as the reliability/robustness of the new algorithm have been verified by applying it to more than 200 CMM measured data sets on differently manufactured parts. The results have been compared with that of Least Squares Method (LSM) and Convex Hull (CVH) method applied to the same data sets. A data filter is proposed to be enclosed in the new algorithm to detect and delete outliers in the data sets.

A study of multimedia applications in education and training

Abstract This paper discusses the emergence of multimedia, its characteristics and advantages. Some of the different applications of multimedia in the field of education and training are cited, and the promising future outlook of multimedia is being examined.

Genetic algorithm and fuzzy C-means based multi-voting classification scheme in data mining

This paper presents a practical scheme used in data mining for classifications based on fuzzy logic and multivoting decision algorithms. It combines the information gain heuristic and genetic algorithm (GA) to minimize the uncertainty level when estimating the weighting functions used in the multiple voting decision scheme. A preliminary test of this scheme using a well-know data set demonstrated its competency and performance improvement for classifications.

Reliability improvement of paper feeder by the integrated robust design and control approach

A methodology for system reliability improvement is presented based on the integration of reliability modeling, robust design, and on-line monitoring and control techniques. Such method has been successfully applied for the quality improvement of a paper handling system.

Minimum Zone Evaluation of Cylindricity Using Nonlinear Optimization Method

Minimum Zone Evaluation (MZE) of cylindricity, as mathematically defined in ASME standard, yields a nonlinear optimization problem which could be an arduous task due to the convergence difficulty. In this paper, the problem is formulated by introducing a new expression and simplification. The contribution of the new formulation to the problem implementation is revealed through the investigation of Objective Function Images (OFIs). This formulation possesses improved characteristics in comparison with the conventional one. This improvement is one of the keys to a successful optimum procedure. Based on the new formulation and the simplex searching technique, an algorithm has been accomplished and verified statistically and experimentally. The verification results are satisfactory in view of accuracy and robustness of the algorithm.

Thermal EMF method for monitoring drilling tool wear

This paper describes a techniQue for on-line monitoring of drilling tool wear based on the thermal EMF (electromotive force) signal. The EMF signal was obtained from a natural thermocouple consisting of the tool (H. S. S.) and workpiece (AISI 1045) metals. The natural thermocouple is thus used as a kind of functional sensor which is sensitive to the cutting zone. The signal was collected and analyzed for three experiments at different cutting conditions. Analysis was carried out in time, frequency and amplitude domains. Several indices for the EMF signal were computed and their relationships with the tool wear were constructed. The results showed that the thermal EMF signal can be used to identify the occurrence of abnormal tool wear on major cutting edges and can indicate the end of tool life. Tool breakage can also be predicted. Consequently, a methodology for monitoring drilling tool wear can be established.

Feature-Based Process Planning for Direct Engineering

Computer-aided engineering (CAE) has been used in industry for several decades. Many innovative approaches and methodologies have been introduced. Most of them, such as computer-aided design (CAD), computer-aided manufacturing (CAM), computer-integrated manufacturing (CIM), concurrent engineering (CE), and design for manufacturing and assembly (DFM/A), have been implemented with varying degrees of success and complexity. The trend has moved from component-based design (e.g., looking at one element of the entire product design and production cycle) to system-based design (e.g., looking at the entire design and process cycle). Direct engineering is an aggressive and innovative concept that seeks to improve the entire life cycle of production by developing a system that dynamically integrates all of the key components from stage A (product design) to stage Z (the final manufactured product). DE aims for variant design but also can be extended to generative design.

Tool condition monitoring by acoustic analysis through a solid path

A sensing method using an acoustic signal obtained in a relative low frequency range through a solid path for the monitoring of tool wear has been investigated. Such acoustic signals could be in the form of stress waves that are released during a machining process, which can be picked up by a regular ferroelectric microphone. Data analysis was conducted in both time and frequency domains. A clear pattern in such signals corresponding to the tool wear conditions has been identified. Several components in spectra were found in the pattern for indicating sudden changes of tool wear or breakage occurring at major cutting edges. It was also observed that the RMS and variance values of the signals could indicate the specific wear condition of the tool. Therefore, this kind of acoustic signal carries sensitive information about the progress of tool wear and can be implemented on line for monitoring tool wear.