An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

Modular architecture is very conducive to the development, maintenance, and upgrading of electromechanical products. In the initial stage of module division, the design structure matrix (DSM) is a crucial measure to concisely express the component relationship of electromechanical products through the visual symmetrical structure. However, product structure modeling, as a very important activity, was mostly carried out manually by engineers relying on experience in previous studies, which was inefficient and difficult to ensure the consistency of the model. To overcome these problems, an integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm (GA) is presented. First, the product information extraction algorithm is designed based on the automatic programming structure provided by commercial CAD software, to obtain the assembly, degrees of freedom, and material information needed for modeling. Secondly, based on the evaluation criteria of product component correlation strength, the structural correlation DSM and material correlation DSM of components are established, respectively, and the comprehensive correlation DSM of products is obtained through weighting processing. Finally, the improved GA and the modularity evaluation index Q are used to complete the product module division and obtain the optimal modular granularity. Based on a model in published literature and a bicycle model, comparative studies are carried out to verify the effectiveness and practicality of the proposed method.

Research on NC Machining Programming Technology in the New Era

with the application and development of intelligent technology in the machining industry, the machining industry is gradually transforming from automation to intelligence. The basis of automatic machining comes from the programming of NC machining program. In the new era, CNC machine tools have high requirements for the accuracy and digitization of machining production. Macro programming technology and modularization design are introduced in NC programming. The first part of this paper introduces the NC machining programming technology. The second part analyzes the technical requirements of NC machining programming technology in improving the accuracy of machining parts; the third part discusses the automatic programming technology of NC machining from the perspective of macro programming technology. The purpose is to provide some reference for the application of NC machining coding technology.

Integrating k-means clustering into automatic programming assessment tool for student performance analysis

Computer programming is a subject involving a large number of logic programming activities. A programmer is compulsory to master skills of algorithms, logic, and programming language to conduct programming. An automatic programming assessment tool is an automated tool used to assist instructors in assessing programming tasks. The technology used in this application is open-source based with an evaluation module that will evaluate the sent program code, assessment, and classification. The evaluation results were then processed in the assessment module, where a comparison process with the test case was performed along with the point calculation. The classification module was used to divide students into five groups based on the point of each practicum. This study used k-means clustering classification method. The entities included were lecturers, assistants, students, and compilers. This application had 2 levels of users namely admin and students. Scoring results were then used in the process of determining the classification of student’s performance based on the k-means clustering method. In connection with the classification test results with three iterations, three practicum scores resulted that the classification process was successfully carried out with student’s performance divided into five groups covering very good, good, sufficient, less, and very less. The data used in the clustering process consisted of 41 students with 10 attributes which were then grouped into 3 groups (clusters).