Automated Defect Detection Using Image Recognition in Manufacturing

Abstract

This abstract examines the process of creating a binary classification project on defect detection in manufacturing, highlighting significant learnings and prospective areas that might be tackled differently if the project were to be redone. Obtaining data from internet sources is the first critical stage in the procedure. This stage involves obtaining essential datasets from reputable web sites. Acquiring high-quality and varied data is critical to the project's success since it serves as the foundation for further analysis and modeling. After acquiring the data, the investigation and comprehension of the dataset begin. This process entails extensive study and analysis to get insights into the data's structure, properties, and distribution. Visualization tools are used to comprehend the insights of the data. After understanding the data, the following step is to construct an efficient input pipeline. This entails preparing and processing the data in order to provide a streamlined and efficient pipeline for the model. The model is constructed using convolutional neural networks (CNNs) in TensorFlow using Python after the input data has been set up. CNNs are a good choice for this project since they do jobs involving images very effectively. To improve the model's performance and avoid overfitting, activation functions, optimization methods, and regularization approaches are carefully selected. The Early Stopping strategy is used with the patience parameter to optimize the training process. When using Early Stopping, the training process is stopped if the performance on the validation set does not increase after a predetermined number of epochs. The model architecture is effectively developed, trained, and optimized by utilizing TensorFlow and Python, enabling effective defect identification in the manufacturing process.

Received: 12 July 2024 | Revised: 10 September 2024 | Accepted: 26 October 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Sercan Dikici: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Project administration. Rachel John Robinson: Conceptualization, Methodology, Validation, Formal analysis, Writing - Original draft, Writing - Review & editing, Visualization, Supervision, Project administration.