Amar Kumar Behera

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.

Manufacture of Accurate Titanium Cranio-Facial Implants with High Forming Angle Using Single Point Incremental Forming

One of the key application areas of Single Point Incremental Forming is in the manufacture of parts for bio-medical applications. This paper discusses the challenges associated with the manufacture of cranio-facial implants with extreme forming angles using medical grade titanium sheets. While on one hand, the failure wall angle is an issue of concern, the parts also need to be manufactured with accuracy at the edges where the implants fit into the human body. Systematic steps taken to overcome these challenges, using intelligent intermediate part design, feature analysis and compensation, are discussed. A number of case studies illustrating the manufacture of accurate parts in aluminium, stainless steel and titanium grade-2 alloy are discussed.

Accuracy Improvement in Single Point Incremental Forming Through Systematic Study of Feature Interactions

Previous studies have shown that feature detection and part segmentation are useful tools to generate compensated toolpaths for single point incremental forming leading to improvement in accuracy of manufactured parts. However, in most practical applications, features do not occur by themselves. Rather, they occur in combination with other features, and the presence of the neighbouring features influences the behaviour of the feature of interest. The final shape of the formed part depends on the interaction between the features. In this study, an attempt has been made to generate a complete taxonomy of common features relevant for incrementally formed parts. This taxonomy is then utilized to generate a matrix of feature interactions, and to classify them as feasible or not. From the subset of feasible feature interactions, a number of cases are analyzed to illustrate the effect of the interactions on the magnitude and nature of inaccuracies resulting in uncompensated parts. Strategies to use the knowledge of the interaction between these features to improve the accuracy of the manufactured parts are then discussed with the help of experimental case studies.

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.

Influence of Material Properties on Accuracy Response Surfaces in Single Point Incremental Forming

The ability to manufacture accurate parts in single point incremental forming is dependent on the capability to properly predict accuracy response surfaces of individual features and feature interaction combinations formed using uncompensated tool paths. Recent studies show that the accuracy profiles obtained are dependent on the choice of material used for forming, in terms of magnitude, geometric shape and nature of errors (under forming and over forming). In this paper, an attempt is made to capture the effect of material properties on the accuracy response surfaces. The response surfaces are modeled using Multivariate Adaptive Regression Splines (MARS), which is a non-parametric multivariate regression technique that helps generating continuous response surfaces. The MARS functions are based on process and feature specific geometric parameters. A set of features and feature interactions for which the response surface dependence on material properties is well predicted is used to illustrate the applicability of the MARS method for predicting the accuracy. An in-process stereo camera system is used to measure the displacement fields for different materials using digital image correlation (DIC) and understand the material dislocation mechanism. Improvements in accuracy for different sheet metal materials based on the predicted response surfaces are then discussed.

Advanced feature detection algorithms for incrementally formed sheet metal parts

Abstract New advanced algorithms for the detection of detailed features in parts formed by single point incremental forming (SPIF) were developed. The features were detected in STL part specifications that took into account the geometry, curvature, location, orientation and process parameters to detect 33 different features within an expert CAPP system for SPIF. The detection process was facilitated by using multi-level edge segmentation routines that first created a frame of edge features. Within this frame, the remaining features were then detected using region growing algorithms. The results show successful detection for a number of test cases. A case study for a double curved hemisphere illustrates the generation of optimal tool paths using compensation for the detected features in the part. These tool paths lead to the improvement in the accuracy of the formed sheet metal parts.

An Integrated Approach to Accurate Part Manufacture in Single Point Incremental Forming Using Feature Based Graph Topology

Previous studies have shown that optimized tool paths based on behavior of individual features and feature interactions can be used to improve the accuracy of features in parts produced by single point incremental forming. These tool paths are generated with compensated CAD files of the part, which result from a prediction of deviations of individual features. However, in order to improve the accuracy of an entire part, it is important to systematically look at behavior of all the individual features and all feasible interactions between features. In this paper, the authors present a graph topology approach to integrating the effects of the behavior of all features present in a part. For any given part, a conceptual graph is constructed representing all the features and connecting them based on their spatial locations with conceptual relations. Next, all possible feature interactions based on the generated graph are analyzed, and the deviations due to the feasible interactions in an uncompensated test are predicted. Depending on the feature types and interactions present, a comprehensive strategy for accurate part manufacture is generated. This strategy may be composed of a selection of one or more complementary tool path strategies for compensating the anticipated deviations on the part. Case studies illustrating improvement in accuracy of parts produced by this technique are discussed next to justify the use of the graph based approach.

Numerical Simulation of a Pyramid Steel Sheet Formed by Single Point Incremental Forming Using Solid-Shell Finite Elements

Single Point Incremental Forming (SPIF) is an interesting manufacturing process due to its dieless nature and its increased formability compared to conventional forming processes. Nevertheless, the process suffers from large geometric deviations when compared to the original CAD profile. One particular example arises when analyzing a truncated two-slope pyramid [. In this paper, a finite element simulation of this geometry is carried out using a newly implemented solid-shell element [, which is based on the Enhanced Assumed Strain (EAS) and the Assumed Natural Strain (ANS) techniques. The model predicts the shape of the pyramid very well, correctly representing the springback and the through thickness shear (TTS). Besides, the effects of the finite element mesh refinement, the EAS and ANS techniques on the numerical prediction are presented. It is shown that the EAS modes included in the model have a significant influence on the accuracy of the results.

Tool path generation for single point incremental forming using intelligent sequencing and multi-step mesh morphing techniques

A new methodology of generating optimized tool paths for incremental sheet forming is proposed in this work. The objective is to make parts with improved accuracy. To enable this, a systematic technique of creating features using a morph mapping strategy is developed. This strategy is based on starting with a shape different from the final shape, available as a triangulated STL model, and using step-wise incremental deformation to the original mesh to arrive at the final part shape. Further, optimal tool path generation requires intelligent sequencing of partial tool paths that may be applied specifically to certain features on the part. The sequencing procedure is discussed next and a case study showing the application of the integrated technique is illustrated.

Electric Energy Consumption Analysis of SPIF Processes

Manufacturing processes, as used for discrete part manufacturing, are responsible for a substantial part of the environmental impact of products, but are still poorly documented in terms of environmental footprint. A thorough analysis on the causes affecting the environmental impact in metal forming processes, especially the innovative but very energy intensive sheet metal forming technologies required to form light-weight products, is nowadays necessary. Therefore, this paper presents an energy consumption analysis, including a power and time study, of Single Point Incremental Forming (SPIF) processes. First, the influence of the most relevant process parameters (e.g. feed rate, step down) as well as the material forming itself are analysed regarding the power demand. Moreover, a comparative study and related energy efficiency assay are carried out on two different machine tools. As the forming time proves to be the dominant factor for the total energy consumption, from environmental point of view, the overall results show many similarities with conventional machining processes. Finally, this paper reports on some potential improvement measures to reduce the SPIF energy consumption.