Automatic Feature Recognition and Tool Path Generation Integrated with Process Planning

The simulation and implementation of Automatic recognition of features from Boundary representation solid models and tool path generation for precision machining of features with free form surfaces is presented in this thesis. A new approach for extracting machining features from a CAD model is developed for a wide range of application domains. Feature-based representation is a technology for integrating geometric modeling and engineering analysis for the life cycle. The concept of feature incorporates the association of a specific engineering meaning to a part of the model. The overall goal of feature-based representations is to convert low level geometrical information into high level description in terms of form, functional, manufacturing or assembly features. Using the boundary representation technique, the information required for manufacturing process can be directly extracted from the CAD model. It also consists of a parameterization strategy to extract user-defined parameters from the recognized features. The extracted parameters from the individual features are used to generate the tool path for machining operations regardless of the intersection of one or more features. The tool path generation is carried out in two phases such as roughing and finishing. Various types of tool paths such as one-way, zig-zag, contour parallel are generated according to the type of the feature for the roughing operation. The algorithm automatically plans the sequence of machining operation with respect to the feature location, and also selects the type of tool and tool path to be used according to the feature. The finishing operation uses the tool path generation strategy in the same manner as used in roughing operation. The algorithm is implemented using the Solid works API library and verified with CNC milling simulator. The results of the work proved the efficiency of this approach and it demonstrate the applicability

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

Determination of machinable volume for finish cuts in CAPP

Identification of machinable volume for finish cut is a complex task as it involves the details not only of the final product but also the intermediate part obtained from rough machining of the blank. A feature recognition technique that adopts a rule-based methodology is required for calculating this small, complex shaped finish cut volume. This paper presents the feature recognition module in a CAPP system that calculates the intermediate finish cut volume by adopting a rule based syntactic pattern recognition approach. In this module, the interfacer uses STEP AP203/214, a CAD neutral format, to trace the coordinate point information and to calculate the machinable volume. Two illustrative examples are given to explain the proposed syntactic pattern approach for prismatic parts

Optimization of roughing operations in cnc machining for rapid manufacturing processes

This paper presents a method for optimizing roughing operations in CNC machining particularly for parts production through a subtractive rapid manufacturing process. The roughing operation in machining is primarily used to remove the bulk of the material and to approximately shape the workpiece towards the finish form. The manufacturing process described utilizes a 3-axis CNC machine with an indexable 4th axis device that is used to hold and rotate the workpiece. The method used is derived from the multiple approaches in roughing operations that differ in the number and the angle of the orientations. Most of the machining parameters are generalized throughout the process to allow some automation in generating the machining program. Overall, the performance of each of the approaches is evaluated based on the lowest machining time to produce the part

Generative feature-based design-by-constraints as a means of integration within the manufacturing industry

The article examines the development of computer aids within manufacturing industry and proposes an alternative approach to the way we design and the designer's role within manufacturing. A feature-based generative design-by-constraints approach is applied, which requires the designer to specify solutions in terms of manufacturing data, which is captured by means of an interactive simulation of machining processes, in which the constraints of equipment, materials and tools are displayed to the designer. The effect of this approach on the integration of all areas within a manufacturing environment is explored, as is the simultaneous design nature of this approach

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Thread Quality Control in High-Speed Tapping Cycles

Thread quality control is becoming a widespread necessity in manufacturing to guarantee the geometry of the resulting screws on the workpiece due to the high industrial costs. Besides, the industrial inspection is manual provoking high rates of manufacturing delays. Therefore, the aim of this paper consists of developing a statistical quality control approach acquiring the data (torque signal) coming from the spindle drive for assessing thread quality using different coatings. The system shows a red light when the tap wear is critical before machining in unacceptable screw threads. Therefore, the application could reduce these high industrial costs because it can work self-governance.This research was funded by the vice‐counseling of technology, innovation and competitiveness of the Basque Government grant agreements IT‐2005/00201, ZL‐2019/00720 (HARDCRAFT project) and KK‐2019/00004 (PROCODA project)