Papers by Author: Li Bao An

Abstract: Developing automatic assembly techniques is essential for the application of advanced nanomaterials such as carbon nanotubes (CNTs). This paper presents a review of current research progress towards the automatic assembly of CNTs by dielectrophoresis (DEP). The mechanisms and experimental set-up of three methods for controlled DEP assembly of CNTs are introduced. One is using a limiting resistor to control the electric field after the deposition of a single CNT or a bundle of CNTs. The other two measure electrode gap electrical signals, gap impedance and current respectively, indicating CNT connections to stop the DEP process. The real-time electrical signal monitoring during the DEP process and controlling the DEP using the detected signals represent a research direction in the manipulation of a wide category of nanomaterials and will speed up the industrial applications of the materials.

Abstract: Parameter optimization in multi-pass cutting operations involves optimal selection of cutting speed, feed rate, depth of cut, and the number of passes, duo to significant influence of these parameters on the quality of machined parts and machining economics. In this paper, a non-linear mathematical model based on minimum production cost for multi-pass milling operations is presented. The unwanted material is removed by one finishing pass and one or multiple roughing passes depending on the total depth of cut. Various realistic constraints are considered when developing the model. Optimal values of machining parameters are found by Genetic Algorithms. An example is presented to illustrate the optimization model and solution approach. The method yields lower unit production costs compared with the results from the literature and machining data handbook.

Abstract: In this paper, the parameter optimization problem for multi-pass turning operations is studied. A mathematical model based on the minimum production cost criterion is developed. The unwanted material is removed by one finishing pass and at least one roughing passes depending on the total depth of cut. Maximum and minimum allowable cutting speeds, feed rates and depths of cut, as well as tool life, surface roughness, cutting force and cutting power consumption are constraints of the model. Optimal values of machining parameters are found by three methods: integer programming, nonlinear programming, and genetic algorithms. An example is presented to illustrate the effectiveness of the optimization model and solution methods. The model generates lower unit production costs compared with the results from the literature and machining data handbook.

Abstract: Cutting parameters including cutting speed, feed rate, depth of cut and the number of passes have significant influence on both machining quality and machining economics. Cutting parameter optimization involves optimal selection of a combination of these parameters. It is an essential part of a computer-aided manufacturing system. In this paper, an optimization model based on minimum production cost for multi-pass turning operations is developed. Various realistic machining conditions and machining requirements are incorporated in the model as constraints. Optimal solutions are found by a nonlinear programming solution approach. A turning example is presented to test the model. Compared with the literature and the cutting regimes recommended in machining data handbook, our model and solution method are simple and generate much lower unit production costs.