Tool path compensation strategies for single point incremental sheet forming using multivariate adaptive regression splines

Abstract

Single point incremental sheet forming is an emerging sheet metal prototyping process that can produce parts without requiring dedicated tooling per part geometry. One of the major issues with the process concerns the achievable accuracy of parts, which depends on the type of features present in the part and their interactions with one another. In this study, the authors propose a solution to improve the accuracy by using Multivariate Adaptive Regression Splines (MARS) as an error prediction tool to generate continuous error response surfaces for individual features and feature combinations. Two feature types, viz.: planar and ruled, and two feature interactions, viz.: combinations of planar features and combinations of ruled features are studied in detail, with parameters and algorithms to generate response surfaces presented. Validation studies on the generated response surfaces show average deviations less than 0.3 mm. The predicted response surfaces are then used to generate compensated tool paths by systematically translating the individual vertices in a triangulated surface model of the part available in STL file format orthogonal to the surface of the CAD model, and using the translated model to generate the optimized tool paths. These tool paths bring down the accuracy for most test cases to less than 0.4 mm of average absolute deviations. By further combining the MARS compensated surfaces with a rib offset strategy, the accuracy of planar features is improved significantly with average absolute deviations less than 0.25 mm.

Effect of stress relieving heat treatment on surface topography and dimensional accuracy of incrementally formed grade 1 titanium sheet parts

“…As this contact is independent of the wall angle, the improvement in surface topography parameters with heat treatment does not depend on the slope of the part. However, the dimensional accuracy of the part is dependent on the wall angle, as illustrated by Behera et al [16], where multivariate response surface models for part accuracy were developed as a function of the part wall angles. Furthermore, for shallow wall angle parts, the part distorts significantly after heat treatment close to the top surface at the level of the backing plate.…”

“…Approaches to improve the accuracy have included toolpath optimization techniques [15][16][17][18], use of in-process heating techniques such as laser support or electric heating to soften the material and thereby reduce spring back and plastic deformation [19,20], feature analysis based techniques [21,22], use of tooling strategies [23] etc. Limited improvement in accuracy of titanium sheet parts was demonstrated for a TPIF process using laser support by Göttmann et al [24] and for SPIF by compensating part geometry by Behera et al [25].…”

Part accuracy improvement in two point incremental forming with a partial die using a model predictive control algorithm

“…Recently, many studies concentrated on the toolpath correction/optimisation. The ISF toolpath can be corrected by using error compensation based on trial fabrications [11], a feature-based toolpath generation strategy [12], a Multivariate Adaptive Regression splines (MARS) correction strategy [13], iterative algorithms based on a transfer function [14], and an artificial cognitive system [15]. Moreover, some in-process toolpath correction approaches were performed in SPIF based on a control strategy using spatial impulse responses of the process [16] and a MPC strategy [17].…”

Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation

“…Kitayama and Yoshioka [6] proposed a springback reduction technique with the control of punch speed and blank holder force through sequential approximate optimization using the radial basis function (RBF) network. Behera et al [7] proposed a solution to improve the accuracy of single point incremental sheet forming by using multivariate adaptive regression splines as an error prediction tool to generate continuous error response surfaces for individual features and feature combinations. In order to reduce springback effects after forming, Li [8] also deals with the variable of blank holder force using the least square support vector regression by establishing the adaptive metamodeling optimization system.…”