Transcript Slide 1

Predicting Assembly Quality of
Complex Structures
Using Data Mining
Ph.D student Ekaterina Ponomareva
Supervisor Professor Terje K. Lien
Problem description
Data mining
Data mining techniques
Manufacturing example
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
• 3 – housing
• 4 – cap
• 5 – seal
• 6 – clamping
• 7 – clip ring
• 8 – sleeve
Manufacturing Example
• Assembly problem of the ball joint of
suspension system of the car is
• 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
• 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
• 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
”IF-THEN” Rules
• 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
• Professor Terje K. Lien (NTNU)
• Professor Kesheng Wang (NTNU)
• Kristian Martinsen (Raufoss)