## 1 Introduction
presented an solution for measuring microclimatic variables through a wireless sensor network [13]. As part of Industry 4.0 implementation, Artificial Intelligence (AI) plays a key role in monitoring variables by providing essential tools for detecting process failures. As [PERSON] et al. stated, AI becomes a powerful problem-solving tool for information analysis, identifying trends and patterns, and making predictions from large volumes of data related to critical variables. Technologies such as Artificial Neural Networks (ANNs), fuzzy logic, Bayesian networks, genetic algorithms, and case-based reasoning are highly useful when implementing a system in any process [14]. Even with a wealth of information about process variables, we cannot assume that we fully understand how each one behaves or how to act on the data without proper analysis. [PERSON] et al. developed a clear approach for determining variable behavior through descriptive statistics using a variable monitoring network in controlled environments [15]. To achieve a meaningful representation of the data and their behaviors, the use of visual interfaces and embedded systems capable of processing information in seconds, as [PERSON] et al. suggested, is critical [16]. [PERSON] et al. introduced a communication system for predictive and prescriptive decision making [17]. [PERSON] et al. developed a distributed control system that can manage various aspects of a process. This system uses remote units responsible for collecting and processing information on the most critical variables and sending it directly to a central unit that aggregates large amounts of data. This type of control has been implemented in industries such as electricity, mining, water treatment, environmental control, and petrochemical sectors [18]. [PERSON] et al. provided valuable insights for decision making by offering a tool for selectingoptimal communication technologies in electrical distribution substations [19]. [PERSON] et al. presented an innovative method for short-term power load forecasting, utilizing feature selection and error compensation in unbalanced samples [20]. [PERSON] et al. applied Artificial Intelligence techniques to detect anomalies in products using various variables available in the manufacturing industry, such as temperature, voltage, and injection time [21]. [PERSON] et al. presented a hybrid model of the behavior of an injection molding process [22]. In this context, the integration of data acquisition and statistical analysis technologies has become a crucial approach for improving efficiency and quality in industrial production. This study focuses on the application of such technologies to analyze the plastic injection process, aiming to identify anomalies, optimize parameters, and enhance process performance. The plastic injection process is a widely used method in the manufacturing industry for producing a variety of products, ranging from automotive components to consumer goods. It involves injecting molten plastic material into a mold, where it solidifies to form the final piece. The quality and efficiency of this process are essential to ensure the consistent production of high-quality products [5]. In plastic injection processes, variables such as injection time, cycle time, and mold pressures play a crucial role in the final product's quality [6]. Controlling and optimizing these variables is essential to ensure product consistency and quality. However, the manual monitoring and optimization of these variables can be challenging and prone to errors [7]. In response to this need, the integration of data acquisition and statistical analysis technologies has become increasingly important in the manufacturing industry. These technologies enable real-time data collection from the plastic injection process and allow for analysis to identify patterns, trends, and anomalies [8].