Johan Verbert

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

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 of 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 of less than 0.25 mm.

Feature Based Approach for Increasing the Accuracy of the SPIF Process

One of the main issues of the single point incremental forming (SPIF) process is still the achievable accuracy. A number of methods have been suggested to increase this accuracy, but many of these contain a significant drawback. Reprocessing the workpiece can increase the accuracy but also significantly increases the manufacturing time and leads to a worse surface finish of the part. Other methods iteratively correct the toolpath based upon the deviations measured on the previously manufactured parts. This method is not very well suited for one of a kind products, since instead of one part, multiple parts need to be manufactured before the desired accuracy can be reached. Our method proposes to use feature detection to split the workpiece in a configuration of planes, edges, freeform surfaces and other features. For each of these features an optimised toolpath strategy can be determined and the toolpath in that zone can be adjusted for this strategy. The proposed method generates a single pass toolpath that leads to more accurate parts compared to the standard CAM toolpaths. This paper describes the feature based optimised toolpath generation method (FSPIF) and contains the results of experiments performed to validate this method.

Multivariate Adaptive Regression Splines as a Tool to Improve the Accuracy of Parts Produced by FSPIF

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.

Study on the Achievable Accuracy in Single Point Incremental Forming

Single-Point Incremental Forming (SPIF) is a sheet metal forming technique that is gradually evolving towards industrial applicability. As recent market analysis studies have shown, accuracy is one of the most important limiting factors for the deployment of SPIF in industrial applications.

Investigation of Deformation Phenomena in SPIF Using an In-Process DIC Technique

Single point incremental forming (SPIF) is a promising new production technique in which a metal sheet is formed stepwise by a spherical tool. However, the technique still shows some particularities. It is observed that the final geometry of a SPIF part can deviate significantly from the programmed tool path. As illustrated in this paper, elastic springback is only to a minor extent responsible for this phenomenon. The goal of the presented paper is to illustrate the gradual emergence of unintended deviations as measured by means of a Digital Image Correlation (DIC) technique. Two CCD cameras were used to take the necessary in-process images. The mechanism of deformation in function of the forming depth is documented and discussed.

Obtainable Accuracies and Compensation Strategies for Robot Supported SPIF

Single point incremental forming (SPIF) is a flexible forming method allowing to shape sheet metal without the need for die. This process is particularly well suited for rapid prototyping and small series production. The classical setup for incremental forming is a specialised rig mounted on a standard milling machine. While this is an economic solution for small to medium size workpieces, large parts require the availability of a big and therefore expensive machining centre. A cheap solution consists in using a robot, which typically has a much better workable range to cost ratio. Unfortunately a robot is usually not a stiff machine, with the consequence that during forming, due to the forming forces, the deformation of the robot can be orders of magnitude greater than the accuracy of the process. In consequence the accuracy of the achieved part is significantly affected. To overcome this problem, a strategy for compensating the deflection of the robot at the level of the tool has been implemented. To support this strategy, two variables have to be examined: the forming forces on one hand and the stiffness of the robot on the other. In this paper it is demonstrated how, based on robot kinematics, the tool deflection can be computed from the knowledge of compliance of each joint in terms of angle deflection versus the moment load applied to the joint. Experimental results illustrate the effectiveness of this approach.