A developed case-based reasoning system for machine tool selection

Abstract

PurposeSubtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues.Design/methodology/approachCase-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem.FindingsA software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose.Originality/valueThe developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.