Data-driven Quality Estimation for Production Processes with Lot-level Quality Control

In high volume production processes such as injection molding, inline inspection of parts is generally not feasible due to additional sensor requirements that incur costs. Production facilities often inspect for defective parts at the lot-level after multiple production runs and not for each run. Individual lots are accepted or rejected based on the manufacturer’s acceptable quality levels determined by the number of faulty parts in a lot. In this paper, a Lot-level Convolutional Neural Network (L-CNN) is proposed that implements two variants to improve quality estimation at lot-level using run-level sensor data. Layer-wise L-CNN uses a separation of layers within the model architecture to extract relevant features for each run. Custom loss L-CNN utilizes a standard CNN architecture with a custom loss function to handle the data’s multi-input-single-output structure. The model is evaluated using data from injection molding and wafer testing applications in the semiconductor industry. L-CNN achieves better F
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and G-Mean scores compared with existing benchmarks. Even though trained with partial information (i.e., lot-level quality), custom loss L-CNN provides both lot-level and run-level quality predictions.