Abstract
Deep drawing is characterized by extremely complex deformation that is influenced by process characteristics such as die and punch shapes, blank shape, blank holding force, material properties, and lubrication. The optimization of the deep drawing process is a challenging issue due to the complicated functions that define and relate the process parameters. However, the optimization is essential to enhance the productivity and the product cost in the deep drawing process. In this paper, a MATLAB toolbox (Pattern Search) was employed to minimize the maximum deep drawing force (Fd-min) at different values of the operating and the geometrical parameters. As a result, a minimum deep drawing force chart (carpet plot) was generated to show the best combination of friction coefficients at the blank contact interfaces. The extracted friction coefficients guided the selection of proper lubricants while minimizing the deep drawing force. A finite element analysis (FEA) was applied through 3D model to simulate the deep drawing process. The material modeling was implemented utilizing the ABAQUS/EXPLICIT program with plastic anisotropy. The optimization results showed that the deep drawing force and the wrinkling decrease when compared with experimental and numerical results from the literature.
Highlights
In terms of production capacity, energy consumption, and process improvement potential, the deep drawing process is one of the most significant technologies in current manufacturing [1,2]
Singh et al [6] discovered the effectiveness of deep drawing variables, including die shoulder radii, punch nose radii, friction coefficients, and deep drawing ratios for cylinder-shaped parts
After multi-objective optimization methods are created for a sheet metal forming problem, special considerations are given to conflicting objectives [8]
Summary
In terms of production capacity, energy consumption, and process improvement potential, the deep drawing process is one of the most significant technologies in current manufacturing [1,2]. Manoochehri and Kolahan [23] developed a combined finite element (FE), artificial neural network (ANN), and simulated annealing (SA) algorithm as a method to model and optimize the deep drawing operation for stainless steel 304 (SUS304). In this method, the parameters (die radius, punch radius, blank holder load, and frictional coefficients) were considered as input factors. Volk et al [25] generated a new optimizing method for optimizing the deep drawing operation utilizing the finite element technique incorporated with the response surface method (RSM) This optimizing method was a procedure for obtaining the optimal values of blank holder loads and the desirable final product. The round blank sheet metal (45 mm radius × 0.8 mm thickness) was modeled as a deformable planar shell base and meshed with shell-type elements of reduced integration S4R elements [29]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
