# A Prototype for Computing the Distance of Features of High-Pressure Die-Cast Aluminum Products
## 1 Introduction  
The automotive industry relies on trustworthy suppliers that consistently comply with quality, time, and cost demands [1]. In the beginning stages of this industry, the lifetime of vehicle components was 20 years [2]. Currently, to comply with constant changes in user demand and the widespread introduction of electronic devices in the central functions of cars [3], manufacturers have decreased the vehicle development time and lifetime of their products, causing changes in the supply chain [4]. Hence, auto parts manufacturers produce different models of automotive pieces with complex geometries by executing short batches supported by digital transformation [5]. In this vein, coordinate-measuring machines (CMMs) are employed to guarantee the machined quality of vehicle parts from the early stages of production as a preventive way to avoid automotive recalls [6]. The CMM validates the measurement tolerances of the piece, and based on the results, the production department makes fine adjustments to or conducts the recalibration of themanufacturing equipment to start the production order. While CMMs are considered the benchmark in the automotive industry by being micro-precise, such machines depend on operator skills, tactile methods, or probe calibration [7]. It is worth noting that when parts change frequently or possess complex geometries, operators invest most of their time in analyzing and designing measurement strategies, reprogramming the CMM, or creating a fixture and a clamping strategy [8; 9], making the process slow and expensive. In addition, the costs associated with CMM installation and maintenance are very high to prevent measurements from being affected by contamination or temperature changes [10]. Thus, such a machine is impractical for in situ quality control applications [11]. Artificial intelligence (AI) has solved industrial problems and undergone rapid growth via hardware architectures and software frameworks [12; 13] that support machine learning (ML) and deep learning (DL) applications [14]. A DL architecture that has evolved quite fast is the convolutional neural network (CNN), which several models employ as a backbone [15]. In the automotive sector, AI algorithms are applied successfully in several computer vision (CV) tasks, such as the virtual construction of vehicles [16], the design of electrodes and electrolytes for vehicles' batteries [17], and electric vehicle air conditioning control [18]. Thus, AI-driven software and CV systems offer the flexibility and adaptability required by the industry (without interrupting the manufacturing process) to face constant changes due to a short development time and lifetime and the small-batch production of pieces with a diversity of materials and intricate surfaces [19; 20]. The flexibility patterns required by the automotive industry make suppliers face the need to produce different types of parts in small batches, creating changes in their workflow. Manual defect detection methods suffer from low efficiency [21], and the CMM's limitations are barriers that restrict rapid adaptation to such changes. CV key tasks, such as object and feature detection, support the manufacture of any object to meet specific criteria related to quality control, measurement, and monitoring [22; 23] and provide an alternative way to face several industrial challenges. For instance, [PERSON] et al. [24], based on the fusion of an RGB and depth image, extracted the spatial position parameters of a tire and identified four features, and [PERSON] et al. [25] created a ceramic artifact with a broad assortment of dimensional and geometrical tolerances (e.g., sphere, cylinder, cone) for the metrology benchmarking of four 3D-scanning sensors. The motivation for conducting this work is related to the elapsed time (minutes or even hours) to confirm that an automotive machined workpiece is within the specified tolerances using a CMM.