Evaluation of the Recycling Conditions Through Injection Molding Using Fuzzy Logic Approach

Recycling plastic products is still essential and crucial in every country around the globe due to its positive benefits on the environment and the economy. There are several mixing procedures for recycling, and it is crucial to understand how these mixtures affect the quality of products by standards and specifications. Consequently, it is helpful to apply analysis and prediction techniques to find out scientifically. Artificial intelligence "AI" techniques are widely used in many manufacturing engineering fields such as recycling operations. This is because of the many advantages that artificial intelligence techniques offer, including the ability to reduce human errors, save time, provide digital support, and make objective decisions. This study intends to employ the fuzzy logic method as one of the "AI" techniques for predicting a significant property that customers frequently need based on their quality levels and standards. This study employed the injection molding process to forecast the values of a mechanical characteristic, specifically tensile strength, under specific operating conditions based on data from the authors' earlier work. This investigation was conducted using two distinct mixing plans. The first mixed all the raw materials, while the second mixed 50% of the raw materials with 50% of the recycled materials. The fuzzy logic results were acquired, and the mean absolute percentage error for the two plans was calculated. Additionally, the outcomes of the current study, which employed the fuzzy logic approach, were contrasted with those of the earlier study, which utilized the response surface methodology approach. Furthermore, the results showed that the response surface technique approach is more accurate than the fuzzy logic since it has the lowest mean absolute percentage error.