Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Predicting Assembly Quality of Complex Structures Using Data Mining Ph.D student Ekaterina Ponomareva Supervisor Professor Terje K. Lien Contents • • • • • Problem description Data mining Data mining techniques Manufacturing example Conclusions Problem description Quality improvement by means of: • On-line data collection • Establishing models of the processes that reliable performance predictions can be made from data Manufacturing Example Control Arm Ball Joint • 1 – ball stud • 2 – plastic liner • 3 – housing • 4 – cap • 5 – seal • 6 – clamping ring • 7 – clip ring • 8 – sleeve Manufacturing Example • Assembly problem of the ball joint of suspension system of the car is considered • Assembly process is automated, but there are still many potential sources of failures • It is important to establish a set of data, which could possibly affect functions of the assembly part Manufacturing Example • 1: Ball stud: heat treatment, surface finish, shape, and diameter • 2: Liner: flash, temperature resistance, grease resistance, change in the volume of the part • 3: Housing: heat treatment, inner diameter • 4: Cap: properties of material, stiffness • 5: Seal: diameter, amount of grease • 6: Clamping ring: heat treatment, anti-corrosion properties • 7: Clip ring: heat treatment, anti-corrosion properties • 8: Sleeve: properties of material, flash Manufacturing Example • The process data are examined by means of data mining system to identify the cause of the problem • Data mining system verify the set of “if-then” rules • By means of these rules system indicates significant problems in the process Data Mining • There is a need to turn the data into useful information and to take steps according to the knowledge gained • This knowledge may include complex relationships among process variables that can define the optimal control settings or be used to prevent defects Data Mining Steps • • • • Problem understanding Data preparation Pattern evaluation Knowledge presentation Data Mining Techniques • The statistical techniques are regression or clustering algorithms (require more prior domain knowledge) • The artificial intelligence techniques are decision trees, neural networks and cluster analysis (require extensive computing recourses) ”IF-THEN” Rules Conclusion • Data mining can be used to extract previously unknown manufacturing knowledge • These knowledge can be used to discover and analyse relationship between parameters that can cause failures • It also can be used to improve manufacturing processes through more effective process and quality control as well as safety enhancements Acknowledgements • Professor Terje K. Lien (NTNU) • Professor Kesheng Wang (NTNU) • Kristian Martinsen (Raufoss)