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This research conducts a comparative study of finite element analysis (FEA) in tube hydroforming (THF). The investigated tube is stainless steel grade 304 (SS304) of as received and after annealing (AN). Three different finite element modeling techniques have been examined and compared with corresponding experimental results. The main modeling parameters of interest range from element technologies, symmetry modeling, and solving schemes. Other parameters being sensitive to each model are discussed. Formability issues have been investigated through FEA and forming limit diagram (FLD). Computational aspects have been illustrated and discussed for simulating THF and an intermediate AN. Prediction of the required forming pressure is challenging. Both shell and continuum elements have different advantages and drawbacks in simulating THF. A shell element with explicit time integration tends to underpredict the required forming pressure while both axisymmetric and 3D continuum elements with implicit time integration tend to overpredict the required forming pressure. The draw-in observation can also provide an insight of the state of deformation.

Tube hydroforming (THF) is an important manufacturing technology for producing tube components by means of fluid pressure. In comparison to other basic forming processes like deep drawing, forming steps can be reduced and more complex shape is allowed. In this work, it was aimed to establish the forming limit curve (FLC) of stainless steel tube grade 304 for the THF process by using finite element (FE) simulations coupled with the Gurson–Tvergaard–Needleman (GTN) damage model as failure criterion. The parameters of the GTN model were obtained by metallography analysis, tensile test, plane strain test of the examined steel in combination with the direct current potential drop (DCPD) and digital image correlation (DIC) techniques. These parameters were well verified by comparing the predicted FLC of steel sheet with the experimental FLC gathered from the Nakazima test. Then, the FLC of steel tube 304 was established by FE simulations coupled with the damage model of tube bulging tests. During the bulge tests, pressure and axial feed were properly controlled in order to generate the left-hand FLC, while pressure and external force needed to be simultaneously incorporated for the right-hand FLC. Finally, the FLC was applied to evaluate material formability in an industrial THF process of the steel tube.

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An unpredictable breakdown often occurs and tends to complicate production scheduling in a steelmaking-continuous casting (SCC) plant. Because of particular characteristics and technology constraints of the SCC plant, traditional robust scheduling often provides an excessively conservative solution. This paper proposes an effective proactive scheduling that utilizes robustness adopting a distribution curve of a system performance created as a mix-integer model. The proposed robustness is designed to work effectively with the existing factory operation and is based on uncertainty assessment. In this paper, artificial neural network (ANN) is adopted with a challenge of designing an accurate model due to the model complexity from the discrete and nonlinear properties of the system performance. The ANN model is achieved by applying k-mean clustering, which decomposes machines to smaller groups having similar effect to the uncertainty. A case study based on data from a real SCC plant is conducted to demonstrate the methodology. The experimental result shows that the proposed methodology is successful in designing a robust schedule that provides a lower production cost under an acceptable breakdown probability while also consuming less computational time compared with the traditional approach.