A Neural Network Based Process Planning System to Infer Tool Path Pattern for Complicated Surface Machining

Die and mold are necessary for the manufacture of present industrial products. In recent years, the requirement of high quality and low cost machining of complicated surfaces has increased. However, it is difficult to generalize process planning that depends on skillful experts and decreases the efficiency of preparation in die and mold machining. To overcome an issue that is difficult to generalize, it is well known that neural networks may have the ability to infer a valid value based on past case data. Therefore, this study aims at developing a neural network based process planning system to infer the required process parameters for complicated surface machining by using past machining information. The result of the conducted case studies demonstrates that the developed process planning system is helpful for determining the tool path pattern for complicated surface machining according to the implicit machining knowhow.

From Requirements Engineering to UML using Natural Language Processing – Survey Study

In the paper process of moving from software requirements to UML diagrams has been studied. It shows the importance of this process and discusses many comparative studies in the field. A questionnaire related to the study was distributed worldwide to many research groups, academia, and industry to know the current status of using requirement management tools, knowledge of using UML in software development, frequently used UML diagrams, and the methodology used to generate UML diagrams from requirements. The paper emphasises that there is a need to do some important research in the area of requirements NLP to obtain UML diagrams, and generalize process of using automatic or semi-automatic methodology to generate UML diagrams from requirements.