###### Abstract  
Form deviation generated during the milling profile process challenges the precision and functionality of industrial fixtures and product manufacturing across various sectors. Inspecting contour profile quality relies on commonly employed contact methods for measuring form deviation. However, the methods employed frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system (\(RAM\)-\(Starlite^{TM}\)), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The \(RAM\)-\(Starlite^{TM}\) vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. The results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, and valuable information to improve machining parameters, as well as quality classification. c 2024 14 8144 10.10.  
material properties, and tool-work vibration, to the generation of profile deformation and resulting surface modulation variations. Today, the quality of contour profiles plays a crucial role in the functionality of fixtures and assembly parts in manufacturing products across various applications, including automotive, aerospace, and household appliances [9]. The industry's rapid adoption of the computer numerical control (CNC) process has prompted the development of stringent geometric tolerance standards for CNC milling parts, leading to rigorous inspection protocols designed to optimize functionality [10]. The protocols could include diagnostic technologies such as automated visual inspection (AVI), X-ray inspection, thermographic inspection, ultrasonic inspection, and laser inspection. In some instances, conducting a thorough inspection can be quite expensive. [PERSON] et al. [11] outline common instruments for measuring contour profiles, including co-ordinate measuring machines (CMMs), optical profilometers, and 3D scanners. The choice of common instruments depends on specific measurement task requirements, such as precision, surface type, and contour profile complexity. While common instruments excel in measuring profile contour with excellent accuracy, they have limitations, including time-consuming measurements, stylus wear, and the potential for scratches on finished part surfaces. Conversely, non-contact methods, particularly those involving computer vision and machine learning, present innovative solutions for efficiently measuring and assessing form deviation. For instance, the study by [PERSON] et al. [12] underscores the transformative advancement of non-contact methods in contour profile inspection, offering benefits such as speed, accuracy, efficiency, and adaptability to complex geometries. In a similar vein, the authors of ref. [13] utilize multilayer perceptron (MLP) and 2D convolutional neural network (CNN) models to classify surface profiles with an average accuracy rate exceeding 99.6%. Ref. [14] harnesses pre-trained networks to classify and recommend cutting conditions, with an effectiveness rate of 99.4%.