Sheet metal-based products serve as a major portion of the furniture market and maintain higher quality standards by being competitive. During industrial processes, while converting a sheet metal to an end product, new defects are observed and thus need to be identified carefully. Recent studies have shown scratches, bumps, and pollution/dust are identified, but orange peel defects present overall a new challenge. So our model identifies scratches, bumps, and dust by using computer vision algorithms, whereas orange peel defect detection with deep learning have a better performance. The goal of this paper was to resolve artificial intelligence (AI) as an AI landing challenge faced in identifying various kinds of sheet metal-based product defects by ALDB-DL process automation. Therefore, our system model consists of multiple cameras from two different angles to capture the defects of the sheet metal-based drawer box. The aim of this paper was to solve multiple defects detection as design and implementation of Industrial process integration with AI by Automated Optical Inspection (AOI) for sheet metal-based drawer box defect detection, stated as AI Landing for sheet metal-based Drawer Box defect detection using Deep Learning (ALDB-DL). Therefore, the scope was given as achieving higher accuracy using multi-camera-based image feature extraction using computer vision and deep learning algorithm for defect classification in AOI. We used SHapley Additive exPlanations (SHAP) values for pre-processing, LeNet with a (1 × 1) convolution filter, and a Global Average Pooling (GAP) Convolutional Neural Network (CNN) algorithm to achieve the best results. It has applications for sheet metal-based product industries with improvised quality control for edge and surface detection. The results were competitive as the precision, recall, and area under the curve were 1.00, 0.99, and 0.98, respectively. Successively, the discussion section presents a detailed insight view about the industrial functioning with ALDB-DL experience sharing.

中文翻译：

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使用深度学习（ALDB-DL）进行基于钣金的抽屉箱缺陷的AI着陆

基于钣金的产品是家具市场的主要部分，并通过竞争保持较高的质量标准。在工业过程中，将钣金加工成最终产品时，会观察到新的缺陷，因此需要仔细识别。最近的研究表明，可以识别划痕，颠簸和污染/灰尘，但是橙皮缺陷总体上提出了新的挑战。因此，我们的模型通过使用计算机视觉算法来识别划痕，颠簸和灰尘，而采用深度学习的橙皮缺陷检测则具有更好的性能。本文的目的是解决人工智能（AI）的难题，这是通过ALDB-DL工艺自动化识别各种基于钣金的产品缺陷时面临的AI着陆挑战。所以，我们的系统模型由两个角度不同的多个摄像机组成，以捕获基于钣金的抽屉盒的缺陷。本文的目的是解决多缺陷检测问题，这是通过自动光学检查（AOI）用于基于钣金的抽屉盒缺陷检测的工业流程集成与AI的设计和实现，称为AI着陆，用于基于钣金的抽屉盒缺陷。使用深度学习（ALDB-DL）进行检测。因此，给出的范围是使用计算机视觉和深度学习算法对基于多相机的图像特征进行提取，以实现AOI中的缺陷分类。我们使用SHapley Additive exPlanations（SHAP）值进行预处理，并使用LeNet和（1×1）卷积滤波器，以及全局平均池（GAP）卷积神经网络（CNN）算法来达到最佳效果。它可用于基于钣金产品的行业，具有针对边缘和表面检测的简易质量控制功能。结果的准确性，查全率和曲线下面积分别为1.00、0.99和0.98，因此具有竞争力。继而，讨论部分提供了有关具有ALDB-DL经验共享的工业功能的详细见解视图。