## 1 Introduction  
Numerous items, including automotive components, home appliance housings, and mobile device cases, have been successfully manufactured through the injection molding process [1]. The quality of manufactured products encompasses various aspects, including surface defects, adherence to geometric dimensions, and mechanical strength. Among these factors, the part weight serves as a crucial indicator to assess potential underfilling, overfilling, or the presence of defects such as voids and flashes [2]. Due to the ease of weight measurement and the ability to be performed before complete cooling, part weight assessment has become the most prevalent method for initial quality inspection in injection molding. In this process, adjustments to machine parameters directly influence the quality of the final plastic components [3]. IM process parameters, which involve high-temperature heating, forced cooling, and high pressure, are energy-intensive and sensitive to quality. The energy efficiency of injection molding remains notably low, with only 50-60% of the total energy being utilized in the forming process [4]. Two distinct approaches are commonly employed to reduce energy consumption in injection molding: enhancing the machinery (including hardware and auxiliary equipment) or optimizing the processing parameters [5]. Researchers have investigated various parameters affecting energy consumption in injection molding to optimize the process and reduce energy use. The cycle time, including packing and cooling times, significantly impacts energy consumption. Reducing these times can lead to substantial energy savings without compromising part quality [6]. Higher injection speeds can reduce the cycle time but may lead to defects like short shots and weld lines [7]. Both the melt and mold temperatures are crucial parameters. Lowering these temperatures can reduce energy consumption while maintaining part quality [8; 9]. The optimal settings for the melt temperature and mold temperature have been identified for energy efficiency [10]. Lowering the screw rotational speed and back pressure can minimize energy consumption. The optimal settings identified include a screw speed of 40 r/min and a back pressure of 1 MPa [11]. The type of power unit and motor significantly affect energy consumption. Servo motors and variable displacement pumps are more energy-efficient compared with fixed-displacement pumps driven by asynchronous motors [12]. The configuration of cooling channels and the flow rate of the coolant are important. Reducing the coolant flow rate by optimizing the cooling system can lead to significant energy savings without affecting part quality [13]. Engineers or operators are required to set up production machines quickly to minimize downtime. Depending on the process complexity and the engineer's or operator's capabilities, manual optimization based on expert knowledge and trial and error can be time-consuming, leading to unexpected delays, significant waste, and suboptimal results for various processes [14]. Unlike established linear modeling techniques such as simple regression, many ML models can adapt to the nonlinear behaviors and relationships between the input and output parameters. Artificial neural networks (ANNs) in particular have shown superior performance in nonlinear modeling tasks [15]. Injection molding (IM) is one such complex manufacturing process characterized by nonlinear behavior [16]. There has been extensive research on the application of machine learning in the manufacturing industry. In the field of injection molding, [PERSON] et al. used the Python programming language to classify parts as either good or bad products using several methods, including Naive Bayes classifiers, decision trees (DTIs), and support vector machines (SVMs) [17]. Other research has employed SVMs to classify a product's quality as good, improvable, or bad [18]. [PERSON] et al.