Zhao Ming-yang

A novel 4-DOF parallel manipulator and its kinematic modelling

In this paper, a novel four-degree-of-freedom (4-DOF) hybrid parallel platform manipulator is presented. The movable platform of the parallel manipulator can translate along two directions and rotate around two axes respectively. The kinematics model is formulated, which describes the inverse kinematic transformation and forward kinematic transformation. A 5-axis parallel machine tool based on the 4-DOF parallel manipulator is presented in this paper.In this paper, a novel four-degree-of-freedom (4-DOF) hybrid parallel platform manipulator is presented. The movable platform of the parallel manipulator can translate along two directions and rotate around two axes, respectively. The kinematics model is formulated, which describes the inverse kinematic transformation and forward kinematic transformation. A 5-axis parallel machine tool based on the 4-DOF parallel manipulator is presented.

A Novel Approach to Fault Detection and Identification in Suction Foot Control of a Climbing Robot

This paper presents a multiple-model and Boolean logic reasoning (MMBLR) approach to detect and identify faults in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the multiple-model adaptive estimation (MMAE) algorithm and the Boolean logic reasoning, the MMBLR approach is properly fit for the fault detection and identification (FDI) application to the climbing robot. In the MMBLR architecture, the MMAE algorithm is used to reliably detect and identify the model-known faults. Then based on the robots states and the results of the MMAE, other faults are detected and identified using the Boolean logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by the MMBLR approachThis paper presents a multiple-model and Boolean logic reasoning (MMBLR) approach to detect and identify faults in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the multiple-model adaptive estimation (MMAE) algorithm and the Boolean logic reasoning, the MMBLR approach is properly fit for the fault detection and identification (FDI) application to the climbing robot. In the MMBLR architecture, the MMAE algorithm is used to reliably detect and identify the model-known faults. Then based on the robots states and the results of the MMAE, other faults are detected and identified using the Boolean logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by the MMBLR approach.

Research on the Line of Laser Tailor Welded Blanks

In this paper, the output of automobile in our country and regions in the world was introduced. Then the use condition of tailor welded blanks in medium-sized car was introduced in detail. What’s more, the characteristic of tailor welded blanks was listed. At last, the rule of length of laser welding tailor blanks was improved, giving other important information for the choice of length of laser welding lines.

Fuzzy Multi-objective Evaluating Method for Hybrid Assembly Line Design

Hybrid assembly line referred to a kind of assembly line with more than one operation mode. Multi-objective optimization is a more practical method for hybrid assembly line design optimization because several design objectives could included in optimization process, such as cost, workload balance, reconfigurability and so on. Multi-objective optimization for assembly line design is an active research field in recent years. The Pareto-based optimization procedures for hybrid assembly line design usually contain two steps. The Pareto optimal frontier is achieved firstly, then the final best solution must be chosen by a decision maker with his experiments from the Pareto optimal frontier. It could lead to more complexity to make a decision with the number of optimization objectives is getting larger. So, how to evaluate the solutions on the Pareto optimal frontier is a problem that related to design quality and efficiency of hybrid assembly line. This paper presents a fuzzy multi-objects evaluating method for hybrid assembly line design. Firstly the mathematical model is constructed for fuzzy multi-objectives evaluating problems. Then several definitions on fuzzy optimization are used to describe the relationship between the Pareto frontier evaluating and fuzzy multi-objectives decision making. Thirdly the procedures for evaluating the solutions on the Pareto optimization frontier are proposed. The method is different from other evaluating methods in two aspects. One of them is that it maps the problem of design solutions evaluating of hybrid assembly line design with fuzzy multi-objective decision making problem. The other one is that a new effective index is defined for evaluating multi-objective solutions on the Pareto optimization frontier. Finally, a numerical example study is presented. The methods is capable of tackling hybrid assembly line design optimization problem more rationally