MANUFACTURABILITY EVALUATION OF PRISMATIC PARTS

Abstract

Design for Manufacturability (DFM) is a philosophy that guides a designer to a good design from a manufacturing perspective. The concept of DFM breaks the isolation between design and manufacturing by simultaneously considering manufacturing constraints in the design stage. DFM is conceptually an alternative solution, conducted earlier in the design stage, to the problems that may occur later in manufacturing stages. With the development of CAD/CAM and the introduction of the concurrent engineering concept in the product development life cycle, manufacturability has become a key element that has to be assessed and analyzed at the design stage. In order to do so, the designer needs to have access to knowledge and information about the manufacturing environment, which facilitates the decision-making activities. The present thesis work focuses on the implementation of DFM methods. In the present work, a DFM methodology has been proposed for the development of a computeraided tool for the quantitative evaluation of the manufacturability of prismatic machined parts. The methodology focuses on the investigation of the factors affecting manufacturability, representation of these complex factors and determination of the manufacturability index to identify the manufacturing problems while the product is designed. The designed part is represented using features, which in turn can be easily used to handle issues related to manufacturability such as feature relationships, tolerances, properties of shape, etc. The feature extraction approach has been selected to capture the design information from the features. A feature recognition approach, which uses volume subtraction and syntactic pattern recognition techniques, is proposed to identify machining features on a prismatic part from BRep data extracted from STEP format. The methodology presented is also capable of identifying features in variety of cases of feature interactions. Further, a setup planning module is integrated into the system, which uses a heuristic-based mathematical reasoning methodology which is developed to give the optimum sequence of operations in the setups as per the available machining resources. The major input required for this module is the list of recognized features along with the possible tool approach directions and set of feasible operations for each feature along with information on the machine tools and cutting tools for each operation. Using this methodology, the optimum setting of the manufacturing resources, such as machines and cutting tools for the selected machining operations for the features, is obtained. With this output, the setup planning module passes the information to the next module, namely manufacturability evaluation, to estimate machine and cutting tool complexities. Finally, the manufacturability of a part is expressed in terms of relative manufacturability indices of its constituting features. Geometrical and technological complexity of the design is established using several parameters such as feature intricacy, tool access direction, feature face orientation, feature accessibility, approach direction depth, feature neighborhood, feature hierarchy, parent and child feature complexities, tolerances, surface finish, tooling complexities etc. which affect the manufacturability of a feature on the part directly or indirectly. Best Worst Method (BWM) is used to assign weights to manufacturability parameters to reflect their relative importance. A case study is presented to show the capability of the system to generate sound indices that could make designs easier to manufacture without compromising on the functional requirements. The presented DFM methodology estimates the difficulties arise in achieving the final design with respect to its geometry and other technological issues. The purpose of quantification is to know the key areas of focus for redesign or modification to reduce cost and time to market. The developed manufacturability indices help designers and machinists to detect and rectify unintentional errors while assigning technological attributes. Most of the interrelationships are built up using inputs of experienced personnel who are well acquainted to workshop conditions. Compared to other rating-based approaches, the present approach incorporates more number of parameters.