Process planning methodology and evaluation of tool life for micromilling with an application to the fabrication of thin wall structure

Abstract

The scaling down effect on feature geometries and tools used in micromilling results in low feature stiffness and excessive tool wear. To achieve the required costs and tolerances, optimisation of the machining processes and their associated parameters are necessary which requires a thorough understanding of machining characteristics. Furthermore, the compensation must be sought for downscaling issues that arise at the process planning stage. Hence, the effect of the characteristics of the cutting tool, workpiece material and machining parameters are investigated in this research through a critical review of the literature followed by a numerical and experimental study of the impact of process variables. The research findings are used in the development of a process planning methodology for micromilling of components with application to high aspect ratio structures, to assist machine operators and to fill the gap between industrial and academic machining knowledge. From the investigation of machining sequences, the study of machining layer strategy considering the sequence of removal of excess material using numerical simulation, strategic planning of machining layers in relation to feature stiffness is required, in particular to the machining of high aspect ratio features. The results from numerical simulation recommend an improved layer strategy for micromilling of thin wall structures, which were then experimentally validated in relation to machining time and geometrical and surface accuracy. The importance of planning tool entry and exit position in relation to feature rigidity was highlighted. The increase in depth of cut shows to improve the tool engagement reducing the thin wall deflection by 168 μm and appearance of the burr along the wall edge indicated by up to 200% drop in burr width. The investigation of tool paths showed the suitability of strategies for machining of circular and linear geometries. Also, the experimental findings emphasise on considering the feature geometry type in the selection of tool paths to achieve a balance between high-performance machining and improved productivity. This study also investigates tool life, associated with flank wear rate, surface roughness, volumetric tool loss and the degradation of the cutting edge radius for micro endmills where a direct correlation between cutting speed and tool wear rate has been found. The new procedure for tool life prediction in conjunction with clear tool rejection criteria for the micro end mill is recommended. Along with standard procedure for the evaluation of tool change intervals to avoid tool failure and consequential defects in parts produced. In addition to the findings in the literature on machine process planning and findings from the study of machining sequence on the thin wall structure and tool life investigation conducted, a new process planning methodology for micromilling has been proposed. The process planning methodology includes four distinct modules i.e. feature recognition, tool selection, machining parameter selection and machining sequence planning. The feature recognition module proposes a new approach to identify key feature faces and their corresponding machining attributes required for tasks in process planning. In the tool selection module, a new methodology for the evaluation of the machinability index and the tool replacement strategy for micro endmills are proposed to guide the operator in the task of tool selection and estimating tool replacement intervals. The machining parameter module provides a systematic approach for the selection spindle speed, feedrate and depth of cut. The machine sequence planning module assists the operator in selecting a suitable tool path and tool layer strategy along with a compensate technique for tool path errors. An artefact with thin wall features has been fabricated using the methodology proposed and the conventional process planning method. The results show the part processed using the proposed methodology achieved better geometrical tolerance, and improved repeatability. It also show a 17% improvement in mean surface roughness, which demonstrates the effectiveness of the proposed methodology.