Fe was added to Al-25 %Si to make a model alloy of recycled Al-25 %Si. The effects of Fe addition on flow length, the formability of thin fins, casting cracks, and heat dissipation were investigated. The addition of Fe did not significantly affect flow length, the formability of thin fins, casting cracks, and heat dissipation. To produce an economical aluminum alloy with a Si content of 25 %, Si was added to ADC12 aluminum alloy for die casting. ADC12, which contains about 10 % Si, was made from recycled aluminum alloy and was economical. A heat sink with thin fins, where the fin tip was 1 mm and the fin height was 50 mm, could be made using Si-added ADC12 with a Si content of 25 %. The addition of Si to ADC12 up to 25 % was effective in creating an economical heat sink with thin and tall fins.

Semisolid high-speed roll casting was developed to improve the ductility of recycled aluminum alloys and aluminum alloys for casting. High-speed roll casting (high productivity) and rapid solidification could be realized using copper rolls, without using a parting material on the roll and at a low pouring temperature of molten metal. As a result, a semisolid strip could be continuously cast at a high speed. The casting speeds (rotation speeds of rolls) in this study were 20, 30 and 60 m/min. Detoxifying the Fe impurity included in recycled aluminum alloys from the outer and inner panels of automobiles was attempted by refining the impurity by rapid solidification. The detoxification of the Fe impurity was shown from results of tensile testing, bending and deep drawing. The possibility of sheet forming of aluminum alloys for casting was shown using deep drawing and three-roll bending. Eutectic Si of aluminum for casting was also refined by rapid solidification. Deep drawing with a limiting drawing ratio greater than 1.7 and three-roll bending were possible using by aluminum alloys for casting. It is shown in this study that the ductility of strips of recycled aluminum alloys and aluminum alloys for casting could be improved by semisolid high-speed roll casting.

Reducing energy consumption and emission is a key environmental issue. Although industry requires high-quality steel sheets for forming, the production of these sheets by the steel industry consumes enormous amounts of electric energy and emits significant amounts of CO₂. In addition to reducing energy costs, there is a need to control CO₂ emission, improve production efficiency, stabilize operations and solve several difficult problems simultaneously. Traditionally, the hot rolling process has been empirically determined to realize the desired mechanical properties, with the quality of the hot-rolled product taking precedence over energy consumption. To solve these problems, it is important to have a predictive technology to determine the amount of energy required in the future and its timing. Therefore, in this study, we compared the power demand prediction system with conventional statistical methods and developed a power demand prediction system that uses deep learning technology to predict the power demand of a hot rolling mill. In this paper, we will outline the development of a visualization and forecasting function for power consumption in hot rolling mills, and introduce our method of constructing a simulation technology to optimize power consumption.

The fracture of tailor-welded blanks (TWBs) fabricated from high-strength steel plates was predicted using integral-type ductile fracture prediction models, specifically, Ayada's model and Cockcroft & Latham's model. The test materials with tensile strengths of 1470 MPa and 440 MPa were laser-welded, and small tensile test specimens with a width of 1 mm were cut from five areas on or near the weld bead. Autonomously-driven finite element analysis (ADFEA) was used to identify the flow curves and critical damage values from the results of small tensile tests. ADFEA is a parameter identification system based on inverse analysis that combines finite element analysis with optimization algorithms driven by machine learning. It is highly effective in reducing the cost of determining simulation conditions for TWBs, which requires the identification of multiple parameters. The accuracy of the identified critical damage value was confirmed through notched small tensile tests. The fracture behavior of the material with a tensile strength of 440 MPa near the weld bead was well reproduced in the FEA.

We conducted an in-situ observation experiment and an autonomously-driven finite element analysis (ADFEA) of the shearing process for a low-carbon cold-rolled steel. The ADFEA consists of finite element analysis and an optimization method based on machine learning. A critical damage value of Cockcroft and Latham’s ductile fracture criterion was identified to minimize the errors with ADFEA. Two errors were defined as the differences between the experimental and analytical results of the sheared surface lengths of the blank and the scrap. The sheared surface length was maximized by ADFEA with respect to shearing process parameters: the punch tip radius, the die tip radius, and the clearance between the punch and the die. We established a digital knowledge archive (DnA), which visualizes the ADFEA results to identify the critical relationships between process parameters and product properties to facilitate automatic technology transfer.

A nanostructured rectangular Tungsten Carbide Cobalt (WC-Co) punch was utilized for piercing the non-oriented electrical steel sheets under the specified clearances with and without reverse holding force. The sheared surfaces were analyzed by Scanning Electron Microscopy (SEM) to determine the burnished and fractured surface area ratios. The punched-out work material was sliced into three layers to obtain a hardness map and estimate the plastic dissipation work. The hardened area and plastic dissipation work were reduced by themselves by decreasing the clearance and applying the reverse holding force.