Single point incremental forming accuracy suffers from contradictory material requirements: while a low yield strength and low hardening coefficient are favourable in terms of limiting process forces and springback, they also result in excessive, unwanted plastic deformation in zones bordering processed areas. Dynamic, localised heat input, for example through radiation of the tool contact area by means of a laser beam, allows to differentiate material properties in time and space. Experimental results demonstrate that this process variant results in reduced process forces, improved dimensional accuracy and increased formability for a range of materials. Initial results also indicate that residual stresses can be significantly reduced by means of the dynamic heating system that was developed.

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Single point incremental forming suffers from process window limitations which are strongly determined by the maximum achievable forming angle. Forming consecutive, intermediate shapes can contribute to a significantly enlarged process window by allowing steeper maximum wall angles for a range of part geometries. In this paper an experimentally explored multi-step toolpath strategy is reported and the resulting part geometries compared to simulation output. Sheet thicknesses and strains achieved with these multi-step toolpaths were verified and contribute to better understanding of the material relocation mechanism underlying the enlarged process window.

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.

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Feature Assisted Single Point Incremental Forming (FSPIF) is a technique to increase the accuracy of the SPIF process. FSPIF generates an optimized toolpath based on the features detected in the workpiece geometry and using knowledge of the behavior of these features during incremental forming. Using this optimized toolpath, parts can be formed with higher accuracy. The prediction of the dimensional deviations occurring in different features during forming as a function of their type (e.g. planar, ruled, freeform or ribs ) and various process parameters, such as sheet thickness, wall angle, tool diameter, rolling direction, etc., is an important step in the FSPIF method. Due to the great number of parameters and combinations that are possible, a mathematical tool should be used in order to automate the prediction process. One such tool is MARS or Multivariate Adaptive Regression Splines, a fast, non-parametric multivariate regression technique with automatic variable selection, which generates continuous surfaces as a response function. In this paper, the authors describe and validate the use of MARS as a tool to predict deviations in uncompensated tests by training the MARS model using only a limited number of experiments. Using this validated model, compensation strategies are developed and implemented, which have shown significant improvements in accuracy in new test cases.

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