<![CDATA[This research utilizes deep learning with tensorflow to enhance the efficiency of incoming quality control (iqc) in material quality inspection. iqc, As a critical stage in the production chain, ensures the quality of incoming materials and plays a significant role in the final product quality. However, iqc effectiveness is often hindered by issues of accuracy and inspection speed. the solution lies in an advanced approach, employing deep learning technology, especially with the use of the tensorflow framework. deep learning is applied for image segmentation, object detection, and material quality classification. The methodology involves cnn on tensorflow, expected to enhance accuracy and inspection efficiency. The objective is to generate an accurate model, reduce inspector involvement, and improve iqc efficiency. The implementation of deep learning is anticipated to create highly accurate models, speed up inspection processes, automate tasks, and reduce operational costs and human error risks. This research has the potential to provide a positive contribution to the advancement of material quality testing technology, making it more sophisticated, efficient, and effective, with a positive impact on final product quality and operational efficiency.]]>
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