###### Abstract
According to the state-of-the-art presented in [15; 16], the most common methods for preprocessing and feature extraction are principal component analysis (PCA), variational mode decomposition (VMD), empirical mode decomposition (EMD), wavelet transform (WT), and Fourier transform (FT). The most common classification methods are support vector machines (SVMs) because of their feasibility of implementation in lineal classification, convolutional neural networks (CNN) because they can find abstract features, and recurrent neural networks (RNNs). After all, they can find patterns in time series and historical information on profile deviation, texture, tool wear, or chatter features. Various methods have been proposed for measuring contour profiles using signal processing and computer vision algorithms. [PERSON] et al. [17] developed an algorithm using higher-order Wavelet transform coefficients to evaluate the form deviation in electric discharge machine (EDM) machining. [PERSON] and [PERSON] [18] and [PERSON] and [PERSON] [19] developed other approaches for surface roughness evaluation involving the utilization of histograms, gray-level co-occurrence matrices, Fourier and Wavelet transforms, and neural networks. The works in [20; 21; 22] developed applications of Fourier-invariant moments and descriptors in geometric and profile property inspection, image retrieval, and object recognition. In addition, refs. [23; 24] utilized Fourier descriptors for dimensional inspection, shape classification, and form deviation evaluation in machining processes. However, developing a system that tries to monitor form deviation, tool wear, and chatter with real-time processing and accurate methodology is necessary. Moreover, fixture design in CNC milling processes is crucial because it directly impacts various facets of the manufacturing workflow. Fixtures are the foundation for securing and positioning workpieces during milling operations, which ensures precision, repeatability, and efficiency. Based on these principles, the novel intelligent inspection method (NIIM) is proposed to inspect the contour profile quality of machined parts. NIIM comprises a calibration piece, a vision system, and a machine learning method that analyzes the shape and quality of the contour profile deformation generated by the CNC machined process. The calibration piece is a machined part designed to find the contour profile deformation generated during the milling process with a profile composed of curve, line, and curve shapes. The vision system is a machine that captures three images, one for each shape of the calibration piece. The images are processed with a machine-learning method that employs Fourier descriptors and a feed-forward neural network (FFNN) that analyzes the form deviations to classify the quality of the shape deformation generated during the CNC milling process. For the experiments, 60 calibration pieces were machined to obtain a dataset of 356 images for training and testing. The rest of the paper is organized as follows. Section 2 describes the methodology based on the vision inspection system and machine learning method. Section 3 explains the experimental setup, which consists of the machined process. Section 4 reports the results. Finally, Section 6 presents the conclusions.