Sihao Deng

Nozzle Mounting Method Optimization Based on Robot Kinematic Analysis

Nowadays, the application of industrial robots in thermal spray is gaining more and more importance. A desired coating quality depends on factors such as a balanced robot performance, a uniform scanning trajectory and stable parameters (e.g. nozzle speed, scanning step, spray angle, standoff distance). These factors also affect the mass and heat transfer as well as the coating formation. Thus, the kinematic optimization of all these aspects plays a key role in order to obtain an optimal coating quality. In this study, the robot performance was optimized from the aspect of nozzle mounting on the robot. An optimized nozzle mounting for a type F4 nozzle was designed, based on the conventional mounting method from the point of view of robot kinematics validated on a virtual robot. Robot kinematic parameters were obtained from the simulation by offline programming software and analyzed by statistical methods. The energy consumptions of different nozzle mounting methods were also compared. The results showed that it was possible to reasonably assign the amount of robot motion to each axis during the process, so achieving a constant nozzle speed. Thus, it is possible optimize robot performance and to economize robot energy.

Plasma Spray Process Operating Parameters Optimization Based on Artificial Intelligence

During plasma spray process, many intrinsic operating parameters allow tailoring in-flight particle characteristics (temperature and velocity) by controlling the plasma jet properties, thus affecting the final coating characteristics. Among them, plasma flow mass enthalpy, flow thermal conductivity, momentum density, etc. result from the selection of extrinsic operating parameters such as the plasma torch nozzle geometry, the composition and flow rate of plasma forming gases, the arc current intensity, beside the coupled relationships between those operating parameters make difficult in a full prediction of their effects on coating properties. Moreover, temporal fluctuations (anode wear for example) require “real time” corrections to maintain particle characteristic to targeted values. An expert system is built to optimize and control some of the main extrinsic operating parameters. This expert system includes two parts: (1) an artificial neural network (ANN) which predicts an extrinsic operating window and (2) a fuzzy logic controller (FLC) to control it. The paper details the general architecture of the system, discusses its limits and the typical characteristic times. The result shows that ANN can predict the characteristics of particles in-flight from coating porosity within maximal error 3 and 2xa0% in temperature and velocity respectively. And ANN also can predict the operating parameters from in-flight particle characteristics with maximal error 2.34, 4.80 and 8.66xa0% in current intensity, argon flow rate, and hydrogen flow rate respectively.

Effect of spray angle on Ni particle deposition behaviour in cold spray

ABSTRACT In this work, cold sprayed Ni particles depositing onto stainless steel (SS) and aluminium substrates was studied the deposition behaviour and bonding mechanism at different spray angles. Contrary to common investigation which is limited to the cross-section or surface, the fractured contact surface on deposited particle was obtained by detaching the as-sprayed particle from the substrate. The results show that the contact surface of Ni particle detached from Al substrate was smooth without signs of metallurgical bonding or interfacial remelting. Compared to the relatively hard SS substrate where dimple-like fractures were found, the substrate hardness is found to play an important role in the formation of metallurgical bonding. Additionally, finite element analysis results show that the relatively hard SS substrate leads to a higher maximum contact pressure, which can promote the formation of metallurgical bonding. It can be convinced that the high contact pressure area is the determining factor for metallurgical bonding.