An automated approach to reuse machining knowledge through 3D – CNN based classification of voxelized geometric features

The enhanced digitalization in the manufacturing sector is claimed to facilitate the generation or the use of the existing process data incorporating the production variations and offers a significant increase in the productivity and efficiency of a system. Moreover, manufacturing companies possess substantial knowledge while designing a product and manufacturing procedures. The primary requirement is to link and organize all the information sources related to the operation design and production. This research is concerned with the reuse of machining knowledge for existing and new parts having similarities in geometric features and operational conditions. The proposed methodology starts by extracting each machining operation's geometric information and cutting parameters using industrial part programs in the numerical control (NC) simulator VERICUT. The removed material between two consecutive operations is obtained through mesh comparison in the simulator to analyze the feature interactions. A deep learning approach based on 3D convolutional neural networks (CNN) is applied to classify similar geometries to reuse the process design knowledge by creating a library of operations. The proposed approach is implemented on actual machining data, and the results demonstrate the effectiveness of the proposed solution. The obtained knowledge clusters in the operations library assist in making propositions related to operational parameters for similar geometric features during the process planning phase reducing the planning and designing time of operations

Standardizing the Process Information for Machining Operations Through Self-Contained Structures

A mechanical product is manufactured through multiple processes and procedures. The process information is coded in a part program, and a large amount of unstructured information comes from the shop floor. This results in the loss of logic formulated for the creation of a code. Moreover, it is impossible to track the modifications carried out during these processes. Thus, the unavailability of appropriate and standard knowledge of part processing leads to the situation where the information must be recreated every time a similar part is manufactured, hence, increasing the process planning time. One solution is to divide it into two steps: first, by fetching the information and coding it in a standardized structure; second saving it in a suitable form, facilitating in improving the efficiency and effectiveness of process design for available parts as well as anticipating the new parts. This was achieved by using the previous information related to the process combined with the one obtained from the shop floor. The proposed work concerns capturing the unstructured information from the existing part programs and regaining it using process simulation (VERICUT). Through the extraction of theoretical and graphical geometric data, the interactions between the operations were analyzed. The operational knowledge in this work includes: origin, feed-rate, rotating speed of the tool, rapid movement, cutting tool, material knowledge, and some geometric information of the process. The proposed approach based on simulations and mathematical programming logic is a way to improve flexibility at process and system level by formalizing the available operational knowledge. To illustrate the applicability of the proposed approach, a case study was carried out on a real industrial part program

Optimum machine capabilities for reconfigurable manufacturing systems

Reconfigurable manufacturing systems constitute a new manufacturing paradigm and are considered as the future of manufacturing because of their changeable and flexible nature. In a reconfigurable manufacturing environment, basic modules can be rearranged, interchanged, or modified, to adjust the production capacity according to production requirements. Reconfigurable machine tools have modular structure comprising of basic and auxiliary modules that aid in modifying the functionality of a manufacturing system. As the product’s design and its manufacturing capabilities are closely related, the manufacturing system is desired to be customizable to cater for all the design changes. Moreover, the performance of a manufacturing system lies in a set of planning and scheduling data incorporated with the machining capabilities keeping in view the market demands. This research work is based on the co-evolution of process planning and machine configurations in which optimal machine capabilities are generated through the application of multi-objective genetic algorithms. Furthermore, based on these capabilities, the system is tested for reconfiguration in case of production changeovers. Since, in a reconfigurable environment, the same machine can be used to perform different tasks depending on the required configuration, the subject research work assigns optimum number of machines by minimizing the machining capabilities to carry out different operations in order to streamline production responses. An algorithm has also been developed and verified on a part family. As a result of the proposed methodology, an optimized reconfigurable framework can be achieved to realize optimal production of a part family. Finally, the proposed methodology was applied on a case study and respective conclusions were drawn