A case-based reasoning system for PCB principal process parameter identification
The Printed Circuit Board (PCB) manufacturing process usually consists of lengthy production activities. Each activity is controlled by a number of process parameters. Although numerous process parameters must be determined before fabrication, only a number of parameters called principal process parameters because they affect the quality of a PCB product. As long as the principal process parameters are identified efficiently and controlled well, the manufacturing lead-time can be shortened and the quality of the new PCB product can be assured. This research proposes a Case-Based Reasoning (CBR) system to infer the principal process parameters for a new PCB product. Each case in the case-base stores design specifications, process parameters, and the corresponding production quality specifications. A Significant Nearest Neighbor (SNN) search is developed to retrieve similar cases from a case-base. A Mutual Correlation Parameter Selection (MCPS) method and a correlation-based parameter setting method are developed to identify the principal parameters and infer their reasonable value range. A set of experiments and a practical implementation case are demonstrated to show the efficiency and accuracy of the proposed system.
case-based reasoning printed circuit board principal parameter identification nearest neighbor search feature selection