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Experimental Part Experimental Materials and Equipment.
Journal of Fudan University (Natural Science Edition), 41(4):429-432, 438, 2002
Journal of Functional Materials, 37(3)345-350, 2006
Science China Materials, 60(7)646-653, 2017
Materials Research Express, 3(7)075025, 2016.

Acknowledgments This paper is sponsored by the National Natural Science Foundation of China (50905162) and the Science and Technology Project of Zhejiang Province (no. 2009R50008).
Materials and Manufacturing Processes. 23(2008) 261-268
Journal of Non-Crystalline Solids. 352(2006) 1704-1707
Journal of Zhejiang University. 42(2008) 858-862
Polymer Engineering and Science. 42(2002) 551-566

Norb, Optimization of Micro Metal Injection Molding for Highest Green Strength by Using Taguchi Method, International Journal of Mechanical and Materials Engineering. 5 (2) (2010) 282-289
Chakartnarodom, Fabrication of Injection Moulded 304L Stainless Steels Reinforced with Tungsten Carbide Particles, Materials Science Forum. 706-709(2012)638-642
Ahn, Water-Soluble Binder with High Flexural Modulus for Powder Injection Molding, Journal of Materials Science. 40 (2005)1105 -1109
Muhamad, A New Starch-Based Binder for Metal Injection Molding, Journal of Materials Processing Technology. 210 (6-7)(2010)961-968
Maeda, Materials Letters 48, 31 (2001)

Plastic strain ratio (r value) is the characterization of materials.
Fan, et al., Static recrystallization kinetics of Haynes230 superalloy, Journal of Aeronautical Materials. 31 (2) (2011) 8-11
Taptik, Effect of hot rolling conditions to produce deep drawing quality steels for continuous annealing process, Journal of Materials Processing Technology. 170 (1-2) (2005) 17-23
Liu, Cold rolling reduction on IF steel fiber texture evolution of quantitative study, Journal of Materials and Metallurgy. 2 (2) (2003) 137-141
Mao, Metal materials with the different crystallographic textures, Science Press, Beijing, 2002, pp. 118-125.

A material blank symmetrically was arranged on the gap.
Acknowledgements This work was financially supported by the National Natural Science Foundation of China (51275185).
References [1] Wang Chao, Zhang YiSheng, Tian XiaoWei, Zhu Bin,Li Jian: Thermal contact conductance estimation and experimental validation in hot stamping process,SCIENCE CHINA-TECHNOLOGICAL,JUL.2012,Vol.55,7: 1852-1857 [2] Zhongxiang Gui, Yisheng Zhang, Hongqing Li, Mingtu Ma: Hot stamping and blank designing for a vehicle bumper using ultra high strength steel (UHSS)，Advanced Materials Research，Vols. 690-693 (2013) pp 2240-2244 [3] Zhu,B, Zhang,Y-S,Li,J;Wang, H,Ye, Z-C: Simulation research of hot stamping and phase transition of automotive high strength steel, Materials Research Innovations, Vol.15, Supplement 1, Feb.,2011, pp.s426-s430 [4] Karbasian, H. and A.E.
Journal of Materials Processing Technology, 2010. 210(15): p. 2103-2118
Journal of Materials Processing Technology, 2012(212):2386-2399.

Among them, cutting materials, rolling and kneading, freezing and packing have already been done with automation[3,4].
Research is carried out based on the analysis of the basic features of soft solids, and materials processing ( with the fresh pork) with macroscopic observation under different loads.
Behavior of Soft Solids under Compressive Loads British Engineering Physical Science Research Council (EPSRC) first proscribed in 1994 the rough definition of soft solids: “those between the non-Newton's body to dry powder, or solids of all materials (but not including dry powder and solid)”.
Acknowledgements The study is supported by Fujian Natural Science Fund (2010J01295) and by Heilongjiang Natural Science Fund (E201043).
Journal of Dalian Jiaotong University, 2009, 30 (4) p18-21 [7] Jia Yujing.

In addition, the use of dissimilar materials will also greatly affect the distortion that occurs in the T welded joints.
Welding parameter optimization continues to be developed to get small distortions with various materials.
The experimental procedure included the design of experiment, materials, and welding parameters.
Journal of Materials Processing Technology. 183(2-3) (2007) 219-225
Journal of Materials Processing Technology. 241 (2017) 103-111

Journal of Anhui Agri Sci, 38 (13):6891-6892.
Journal of Environmental Sciences,23(8):1279-1285
Journal of Hazardous Materials,213-214:123-132
Journal of Agro-Environment Science,29(8):1571-1575.
Journal of Environmental Science and Health, Part A: Toxic/Hazardous Substances and Environmental Engineering,46(7):789-794

The coating was applied using the arc spraying technique, where two materials were melted by an arc and then distributed onto the substrate using compressed air.
Arc spraying constitutes a thermal technique wherein an electric arc is generated between two consumable electrodes while compressed gas serves to atomize and propel the molten electrode materials onto the substrate.
Lin, Preparation, Wear Resistance, and Corrosion Performance of Arc-Sprayed Zn, Al, and Zn-Al Coatings on Carbon Steel Substrates, Journal of Materials Engineering and Performance (2023) 1-14
Lee, Localized corrosion and repassivation behaviors of additively manufactured titanium alloys in simulated biomedical solutions, npj Materials Degradation 7(1) (2023) 44
Lin, Preparation, Wear Resistance, and Corrosion Performance of Arc-Sprayed Zn, Al, and Zn-Al Coatings on Carbon Steel Substrates, Journal of Materials Engineering and Performance 32(23) (2023) 10542-10555.

A Study on the Prediction and Comparison of processing using the Artificial Neural Network in Nitinol Electrochemical Machining WooJae Song1,a, EunSang Lee2,b 1Graduate School of Metal Materials and Machining Process Engineering, Inha University, Incheon 22212, Republic of Korea 2Department of Mechanical Engineering, Inha University, Incheon 22212, Republic of Korea atorsten@inha.edu, bleees@inha.ac.kr Keywords: Nitinol(SMA), Electrochemical Machining, Artificial Neural Network, Machining Prediction Abstract.
In addition, there is a limit to the application of the Design of Experiments(DOE- RSM,Taguchi), which requires the highest precision of machining, to the machining processing of the advanced composite materials that are difficult to machining. [6] Therefore, this study applied Artificial Neural networks to electrochemical machining(ECM), which provide more accurate data than traditional Design of Experiments(DOE).
Therefore applying DNN to advanced materials, such as machineability shape memory alloys(SMA), provides more accurate predictions and results compared to traditional design of experimental(DOE) methods.
Acknowledgments This research was supported by Korea Institute of Science and Technology Job Promotion.
Ugrasen, 2017, “ Estimation of Machining Performances in WEDM of Aluminium based Metal Matrix Composite Material using ANN, “Materials Today: Proceedings, Volume 4, Issue 9, pp. 10035-10038,,