Abstract: Various embodiments include a method for product quality inspection on a group of products. The method may include: getting for each product in the group of products: image, value for each known fabrication parameter affecting quality of the group of products, and quality evaluation result; training a neural network. A layer of the neural network comprises at least one first neuron and at least one second neuron; each first neuron represents a known fabrication parameter affecting quality of the group of products and each second neuron represents an unknown fabrication parameter affecting quality of the group of products; and the images of the group of products are input to the neural network, the quality evaluation results are output of the neural network, and the value of each first neuron is set to the value for the known fabrication parameter the first neuron representing.

Abstract: Various embodiments include a method for product quality inspection on a group of products. The method may include: getting for each product in the group of products: image, value for each known fabrication parameter affecting quality of the group of products, and quality evaluation result; training a neural network. A layer of the neural network comprises at least one first neuron and at least one second neuron; each first neuron represents a known fabrication parameter affecting quality of the group of products and each second neuron represents an unknown fabrication parameter affecting quality of the group of products; and the images of the group of products are input to the neural network, the quality evaluation results are output of the neural network, and the value of each first neuron is set to the value for the known fabrication parameter the first neuron representing.