As manufacturing facilities move towards Industry 4.0, automation is becoming increasingly prevalent. The development of innovative manufacturing technologies, complex parts and high-precision parts has resulted in the deficiency of traditional manual inspection methods. This has led to computer-aided inspection systems (CAIs), including image-based systems and 3D scanning technologies. In this work, a quality inspection system using 3D scanners has been developed and proposed. These innovative systems reduce human error and maintain accurate and reliable results to address traditional quality control limitations. The current literature indicates that part inspection should be automated to avoid limitations in manual visual inspection, establish stronger and more systematic inspection methods, and make full use of point cloud data. The proposed system aims to improve quality inspection processes in manufacturing. The system is based on the Artec Leo 3D scanner system. The operation of the 3D scanner was studied and the point cloud data was processed. Finally, the reliability and efficiency of the proposed system are studied and analyzed. The system focuses on trends and challenges in Industry 4.0 by dealing with the development of quality assurance topics. This complete inspection system gathers relevant quality characteristics and forms a complete basis for the evaluation of product quality. The developed system can be used to measure parts' dimensions accurately, with an average accuracy of 98.69%, comparable to the literature review system, as the inspection time has been optimized and the requirements for the required equipment are kept to a minimum.