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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)