Development of Product Complexity Index in 3d Models Using A Hybrid Feature Recognition Method with Rule-based And Graph-based Methods

A machining process is very dependent on the model created. The more complicated the model, the greater the design difficulty and the greater the machining process. Reduced production costs can help a company increase profits. A focus on production cost can be achieved in a number of ways, the first of which is by replacing materials or changing the design. It is better to reduce product costs during the design stage than during the manufacturing stage. The main objective of this research is to develop an application that can recognize features in a CAD program and calculate the complexity index of shapes in real time. In this study, the prismatic features and slab features classified by Jong-Yun Jung were used. The feature recognition method applied in this study is a hybrid of the rule-based and graph-based methods, which uses the STL file developed by Sunil and Pande to obtain all the information needed. Then, the results are extracted from feature recognition data and are used to calculate the product complexity index of the model being studied. This study applied the product complexity index, following the model developed earlier by El Maraghy. Validation is performed by comparing the software count with the complexity index calculated with the STEP method by Hendri and Sholeh et al. This research develops a program that recognizes features in CAD software and calculates the index complexity of shapes in real time. This will allow designers to calculate the expected complexity value during the design process. As a result, the estimated production cost can be seen early on. Finally, this software is tested for calculating the index values for the complexity of a combined features model. The use of eight slots and eight pockets as a benchmark scoring for shape produces a more accurate product complexity index