ABSTRACT
Integrating machine learning algorithms with additive manufacturing has significant potential to enhance the quality control process in the revolution of Industry 4.0. As additive manufacturing becomes increasingly prevalent in the manufacturing industry, the need for quality control has become more critical than ever. In this competitive era, identifying patterns and trends of large volumes of generated data during the additive manufacturing process is very difficult or sometimes impossible for human beings; therefore, deep learning algorithms can be an effective and efficient tool to determine and analyze the trend of such a large amount of data in additive manufacturing. This can help find potential defects at its initial stage, improve quality, and reduce manual inspection costs. In addition, deep learning algorithms can help to optimize the additive manufacturing process, thus proving it cost-effective and efficient. By analyzing data from sensors monitoring the printing process, deep learning algorithms can identify patterns and optimize process parameters, leading to improved print quality and consistency, reduced waste, and minimized risk of defects. Machine learning algorithms in quality control and process optimization can significantly enhance the overall efficiency and productivity of the manufacturing process in Industry 4.0. With the ability to rapidly analyze vast amounts of data, deep learning algorithms can detect subtle defects and help manufacturers identify opportunities for improvement in the additive manufacturing process. In this chapter, various case studies have been used to demonstrate that the effective utilization of deep learning in the field of additive manufacturing and quality control can help manufacturers to improve product quality, reduce waste, and optimize the manufacturing process, making them more competitive, effective, and efficient in the age of Industry 4.0.
