Abstract
There is an urgent demand for free-form products in industry at the present time because of their superior appearance and the wide variety of functions they perform. Five-axis high-speed CNC machining technology has developed to satisfy this demand, but further improvement in surface quality metric inspection technology is the big challenge it now faces. In this study, the effects of jerk on the performance of five-axis synchronous high-speed CNC ball nose end mills on a freeform turbine mold were investigated. The relationships of characteristics of the images of 14 jerk-cluster finished workpieces with different jerk setting values were established, allowing surface texture features to be analyzed and surface roughness predicted. In addition, machine learning methods were integrated with the surface feature analysis to construct a virtual machining module that acts as a performance prediction system, merging the virtual machine tool functions, surface texture processor, and AI roughness prediction processor. Using the geometric information of the workpiece, cutting parameters and machine tool parameters as inputs, product performance metrics combining surface roughness and machining time can be predicted as outputs of the system. The integrated system provides users with a way to evaluate manufacturing performance before performing actual operations and to reduce test time for cutting parameter development. The model is suitable for complex surface finishes as well as for the production of small batches with high parametric variance. In addition, the partial set of image processing and roughness prediction modules can be used alone as an effective intelligent surface quality inspection system.
Highlights
There are two main practical problems that engineers face in the manufacturing process: to determine the values of the process parameters that will yield the desired product quality [1,2] and to maximize manufacturing system performance using the available resources [3,4]
As part of the product development flow and manufacturing process chain, as shown in Fig. 1, workpiece geometry can be parametrically defined in CAD/CAM computer code, and the tool path can be generated for given dimensions of a tool according to the desired sequences of machining
The engineer is responsible for selecting tools, feedrates, and speeds to generate the tool paths according to the process planning strategy for the kinematic machining operation [5]
Summary
There are two main practical problems that engineers face in the manufacturing process: to determine the values of the process parameters that will yield the desired product quality [1,2] and to maximize manufacturing system performance using the available resources [3,4]. With respect to the latter, industry nowadays has entered into a phase that focuses on customized, small batches and high parametric variance production, pursued alongside traditional mass-production. The dynamic accuracy of machine tools is determined by the feed-axis acceleration required to produce precise movement between the workpiece and tool [6,7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
