A Decision Framework to Select Manufacturing Method Based on Part Complexity (Additive or Subtractive)

Abstract

At the end of the twentieth century, the industrial sector introduced a new manufacturing method known as additive manufacturing, alongside traditional methods such as subtractive, joining, dividing, and transformative manufacturing. Recently, additive manufacturing technology has become increasingly competitive among these traditional methods due to its unique characteristics and the rapid development of its applications and features. The goal of the current study was to develop a method that enables users to make informed decisions regarding the most appropriate manufacturing method (i.e., additive or subtractive) for producing specific parts. The selection of the appropriate manufacturing method is complex and often relies on subjective judgment or experience, which can lead to suboptimal decisions. This study used part complexity as a basis for determining the most suitable manufacturing method for a specific part. Initially, the study identified factors that affect machining time from the literature, such as pocket internal small radius corners, thin walls, and 3D form surfaces. Actual manufacturing time, which is a reliable indicator of overall machining difficulty, was used along with the identified factors to develop a model that can predict part complexity using multiple regression with 54 parts as training data. The model was successfully validated by applying it to 31 parts as test data. Subsequently, the study developed a decision model that used the part complexity model and shape volume as factors to decide between additive and subtractive methods. The results show that these factors are critical in making the decision. The study also tested how production volume impacts the decision and found that it significantly shifts the choice between these technologies.