Accurate prediction of machining feedrate and cycle times considering interpolator dynamics

Abstract

This paper presents an accurate machining feedrate prediction technique by modelling the trajectory generation behaviour of modern CNC machine tools. Typically, CAM systems simulate machines’ motion based on the commanded feedrate and the path geometry. Such approach does not consider the feed planning and interpolation strategy of the machine’s numerical control (NC) system. In this study, trajectory generation behaviour of the NC system is modelled and accurate cycle time prediction for complex machining toolpaths is realised. NC system’s linear interpolation dynamics and commanded axis kinematic profiles are predicted by using finite impulse response (FIR)–based low-pass filters. The corner blending behaviour during non-stop interpolation of linear segments is modelled, and for the first time, the minimum cornering feedrate that satisfies both the tolerance and machining constraints has been calculated analytically for 3-axis toolpaths of any geometry. The proposed method is applied to 4 different case studies including complex machining toolpaths. Experimental validations show actual cycle times can be estimated with > 90% accuracy, greatly outperforming CAM-based predictions. It is expected that the proposed approach will help improve the accuracy of virtual machining models and support businesses’ decision-making when costing machining processes.

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

“…For instance, the surface quality of parts can be estimated in parallel to the process within a GPU-enabled (graphical processing unit) material removal simulation with a subsequent virtual measurement, which significantly reduces the latency between machining and the detection of defective parts [166,167]. A sufficiently accurate virtual representation of the machining process [26,[168][169][170][171]] also enables cause-and-effect analysis and, therefore, robust process design and control [172][173][174][175][176] as well as the exploitation of potential process productivity [177]. Further cases of production processes with knowledge-based approaches include welding [178,179], injection molding [180,181], linear winding [182], tape laying [183], metal forming [184], laser polishing [185], automated fiber placement [186], sheet molding compound [187], fused filament fabrication [188], and metal additive manufacturing (AM) [189][190][191][192][193].…”

Selected Aspects of Precision Machining on CNC Machine Tools

“…As an alternative measuring technique for tool geometry accuracy monitoring, it was proposed to use a trigger touch probe integrated into the NC control system [17] or laser measuring tools or interferometers to control angular errors, horizontal and vertical straightness errors, parallelism errors [18], and squareness errors [19]. Another way to affect and improve machining accuracy is to predict feed rate and cycle times considering interpolator dynamics using a finite impulse response-based low pass filter, which estimated more than 90% more accurate cycle times than CAM-based prediction [20]. Holub et al found that the roundness of the machined surface when milling on a CNC machine can be improved by 40% using volumetric compensation accompanied by a LaserTRACER self-tracking interferometer [21].…”

Machining cycle time prediction: Data-driven modelling of machine tool feedrate behavior with neural networks

“…Therefore to model the non-stop interpolation behavior of modern NC systems significant research has been conducted in non-stop interpolation and geometric blending methods. The research spans from circular arcs, cubic [12] and quintic splines [13] through to modern Finite Impulse Response based filtering methods [14] [15].…”