Research And Implementation Of Trajectory Planning Of Ultrasonic Testing Based On Time Optimization

Ultrasonic nondestructive testing technology that scholars is researching hasbecome a hot issue in recent years, which plays an important role in modern life. Studythe trajectory planning technology also has important significance to improve theflooding ultrasonic flaw detector efficiency, and can increase productivity and improvetracking accuracy. This article which aim at the shortest time and meet within theconstraints of kinematics, an improved adaptive genetic algorithm for ultrasonictesting trajectory planning is proposed.Firstly, ultrasonic nondestructive testing, ultrasonic flaw detector and other theorieswere introduced, so that we have a conceptual understanding of them. Then followingultrasonic testing system trajectory planning processes, has introduced the establishmentof ultrasonic testing system kinematics modeling, surface reconstruction, the generationof surface sampling points, trajectory planning equation and improved adaptive geneticalgorithm and so on. D-H method is used in kinematic model establishment ofultrasonic testing system, and according to the solution of inverse kinematics equationsobtain respectively movement point values and threshold values. For CAD unknownworkpiece surface reconstruction, and surface parameters such as non-discrete, generateparameters to meet the requirements of non-detection point. Cubic equation is used tofit the trajectory and the track is used to meet kinematic constraint equations, such asspeed constraint acceleration constraints.We research genetic algorithms and adaptivegenetic algorithm, analyze its strengths and weaknesses, and make improvements on itsbasis.At last,improved adaptive genetic algorithm to meet the higher requirements isobtained.Finally, an algorithm instance is introduced to verify the study which have beendone have proved the feasibility of the algorithm. According to comparison of thesimulation results, the advantage of improved adaptive genetic algorithm is obtained.The study will be applied to practical projects with some practical significance.