A rapid and intelligent approach to design forming shape model for precise manufacturing of flanged part

Adjusting the part shape with complex flanges to compensate springback deformation is key to forming shape design for manufacturing rapidly and precisely. Classical forming shape design by displacement adjustment (DA) method using finite element (FE) simulation is usually time-consuming and not accurate enough for complex surface part in industrial application. In this paper, the forming shape is modeled by changing the relations of geometric features of part model with the new flange control surfaces directly. Control surface processing (CSP) method is presented including control surface trimming, cross section division, springback compensation, and extending to design forming shape model of doubly curved flange part with joggles rapidly. The algorithms of cross section curves division of control surfaces and subsequent subdivision of each curve with circular arc and line segments are proposed. A case-based reasoning (CBR) technique and gray relation analysis (GRA) are used to support the intelligent springback prediction of each bending segment of the cross section curve. The geometric data of control surface is expressed in XML format to realize the integration of the CAD-based tools of control surface division and compensation with the Web-based springback prediction system. The approach is demonstrated on an industrial aircraft wing rib part. The forming shape model could be designed rapidly by comparison with DA method. The part shape deviations of flange angle (−0.465° ~ 0.528°) and surface position (−0.3 mm ~ 0.3 mm) were detected by comparing the desired geometry with the actual digital formed part shape, and the results indicate that the approach can achieve the industrial part manufacturing rapidly and precisely.

[1]  J. Huetink,et al.  The development of a finite elements based springback compensation tool for sheet metal products , 2005 .

[2]  Feng Ruan,et al.  A die design method for springback compensation based on displacement adjustment , 2011 .

[3]  Behrooz Arezoo,et al.  Prediction of springback in sheet metal components with holes on the bending area, using experiments, finite element and neural networks , 2012 .

[4]  Jie Zhou,et al.  Springback compensation of automotive panel based on three-dimensional scanning and reverse engineering , 2016 .

[5]  Alexander Kolesnikov.  Segmentation and multi-model approximation of digital curves , 2012, Pattern Recognit. Lett..

[6]  M. R. Jamli,et al.  Integration of feedforward neural network and finite element in the draw-bend springback prediction , 2014, Expert Syst. Appl..

[7]  R. A. Lingbeek,et al.  Springback Compensation: Fundamental Topics and Practical Application , 2006 .

[8]  J. B. Wang,et al.  Digital Sheet Metal Manufacturing System and Application , 2007 .

[9]  Yue Ma,et al.  Static and dynamic performance evaluation of a 3-DOF spindle head using CAD–CAE integration methodology , 2016 .

[10]  Boris Štok,et al.  An enhanced displacement adjustment method: Springback and thinning compensation , 2012 .

[11]  Timo Meinders,et al.  Theoretical verification of the displacement adjustment and springforward algorithms for springback compensation , 2008 .

[12]  Jie Hu,et al.  Research on high creative application of case-based reasoning system on engineering design , 2013, Comput. Ind..

[13]  Jirapond Tadrat,et al.  A new similarity measure in formal concept analysis for case-based reasoning , 2012, Expert Syst. Appl..

[14]  R. H. Wagoner,et al.  DIE DESIGN METHOD FOR SHEET SPRINGBACK , 2004 .

[15]  Wu-Chih Hu,et al.  Multiprimitive Segmentation of Planar Curves-A Two-Level Breakpoint Classification and Tuning Approach , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Keith Case,et al.  Knowledge reuse in manufacturability analysis , 2008 .

[18]  Mohsen Rezayat,et al.  Knowledge-based product development using XML and KCs , 2000, Comput. Aided Des..

[19]  Hong-Liang Dai,et al.  A novel model to predict U-bending springback and time-dependent springback for a HSLA steel plate , 2015 .

[21]  Narender Neerukonda,et al.  Full length Article: Integration of product design, process planning, scheduling, and FMS control using XML data representation , 2010 .

[22]  Zhaohao Sun,et al.  R5 model for case-based reasoning , 2003, Knowl. Based Syst..

[23]  Jin Qi,et al.  Hybrid weighted mean for CBR adaptation in mechanical design by exploring effective, correlative and adaptative values , 2016, Comput. Ind..

[24]  M. S. Yong,et al.  Design solution evaluation for metal forming product development , 2008 .

[25]  Clare Dixon,et al.  An intelligent process model: predicting springback in single point incremental forming , 2015 .

[26]  Jianjun Wu,et al.  A new iterative method for springback control based on theory analysis and displacement adjustment , 2016 .

[27]  Gary R. Consolazio,et al.  Data storage and extraction in engineering software using XML , 2005, Adv. Eng. Softw..

[28]  H.J.J. Kals,et al.  The Application of Features in Airframe Component Design and Manufacturing , 1993 .

[29]  K. Narasimhan,et al.  A Hybrid Intelligent Systems Approach for Die Design in Sheet Metal Forming , 2000 .

[30]  Carolina Vittoria Beccari,et al.  Subdivision surfaces integrated in a CAD system , 2013, Comput. Aided Des..

[31]  Peng Wang,et al.  A hybrid method using experiment design and grey relational analysis for multiple criteria decision making problems , 2013, Knowl. Based Syst..