Chonghai Xu

Design of Nano-Micro-Composite Ceramic Tool and Die Material with Back Propagation Neural Network and Genetic Algorithm

An algorithm combined with back propagation neural network (BPNN) and genetic algorithm (GA) was used in the optimum design of the compositions of an advanced ZrO2/TiB2/Al2O3 nano-micro-composite ceramic tool and die materials. GA was used to fully optimize the network topology, thresholds, and initial connection weights of BPNN. The input parameters are the contents of each compositions of ceramic tool and die materials and the output parameters are mechanical properties including hardness, flexural strength, and fracture toughness. The compositions with optimum mechanical properties can be chosen for materials preparation with less error and the result can be used to guide the experimental process. As a result, the nano-micro-composite ceramic tool and die material with good mechanical properties was then fabricated. It indicated that the algorithm can offer a robust and efficient way for the compositional design of ceramic tool and die materials.

Prediction of the mechanical properties of ceramic die material with artificial neural network and genetic algorithm

This paper presents a connectionist approach using back-propagation artificial neural network (BP) and genetic algorithm (GA) to predict the mechanical properties of ceramic die material. This method is using GA to optimize BP, including how to train the initial connection weights of network and to determine the threshold values. In the analysis, mechanical properties of ceramic die material are optimized while its composition is assumed to be predefined. Various output parameters are considered in the optimization analysis, including hardness, flexural strength and fracture toughness in the optimization analysis. And the input parameters are composition of a kind of material. Results from experiment simulation tool are utilized to train and test the BP-GA optimization approach. The analysis indicates that the BP-GA approach offers a robust and efficient method of optimizing the mechanical properties of ceramic die material when improved neural networks are utilized instead of neural networks.

The influence of process parameters on the preparation of CaF2@Al(OH)3 composite powder via heterogeneous nucleation

Abstract The use of self-lubricating materials not only reduces friction coefficients and alleviates effects of wear and tear, but also helps to maintain the mechanical properties of materials. By using coating technology in the application of self-lubricating materials, it is possible to effectively improve the bonding strength between a solid lubricant and substrate material and additionally to protect the solid lubricant against oxidation and decomposition during the sintering process. This study uses the heterogeneous nucleation method, where aluminum hydroxide (Al(OH)3) is coated on the surface of calcium fluoride (CaF2) to prepare a CaF2@Al(OH)3 composite powder. It was observed, through analysis of the preparation process parameters, that the Al3+ concentration, pH value, and reaction temperature were the factors which most influence coating quality. Optimum process parameters were found to be an Al3+ concentration of 0.15 mol · L−1, pH value of 7.5, and reaction temperature of 75 °C. Comparing and analyzing the micro-morphology of sample composite powders proved the effectiveness and favorable performance of the CaF2@Al(OH)3 composite we produced under optimized process parameters.

Investigation of Al2O3/TiC ceramic cutting tool materials with the addition of SiC-coated h-BN: preparation, mechanical properties, microstructure and wear resistance

Abstract SiC-coated h-BN powders ((h-BN)/SiC) were used to substitute h-BN as solid lubricant to prepare Al2O3/TiC/(h-BN)/SiC self-lubricating ceramic cutting tool materials using hot pressing. Microstructure and mechanical properties of the material were studied. Overall improvements in Vickers hardness and fracture toughness were obtained compared with that of Al2O3/TiC/h-BN. The coating of SiC blocks direct contact between h-BN and Al2O3/TiC and hence enables h-BN particles to bond more closely with Al2O3/TiC. The density of the material was dramatically improved because of the decrement of voids around h-BN particles. Dry turning tests were carried out to investigate the wear resistance of the materials when cutting 40Cr quenched and tempered steel. Enhancement in wear resistance, which may result from the improvement in mechanical properties, was observed.

Recent Progress in Self-Lubricating Ceramic Composites

Structural ceramic composites have received increasing attention over the past few decades for their potential applications in various fields. Lubrication is usually required for moving ceramic parts because of their high coefficient of friction under dry sliding conditions. Self-lubricating ceramic composites have been applied in severe operating conditions where conventional lubrication method, such as liquid lubrication, is unavailable. The solid lubricants added in self-lubricating ceramic composites can reduce the coefficient of friction. However, they decrease mechanical properties and then weaken antiwear property of the ceramic composites, which consequently restricts self-lubricating ceramic composites’ application scope. Therefore, there is a contradiction between the antifriction and antiwear properties of self-lubricating ceramic composites and many efforts from researchers have been devoted to resolve it. In this chapter, two new types of self-lubricating ceramic composites were elaborated. Graded self-lubricating ceramic composites were developed by adopting the design concept of functionally graded materials (FGMs). Their characteristics are that the solid lubricant content decreases with a gradient from the surface to the center and thermal residual compressive stresses exist in the surface after the sintering process. The gradient distribution of solid lubricant and the thermal residual compressive stresses are used to improve the mechanical properties of the ceramic composites. Another new type of self-lubricating ceramic composites is those with the addition of coated solid lubricants. The solid lubricant powders are firstly coated by metal or metallic oxide, etc., to form core-shell structured composite powders and then mixed with the ceramic matrix powders to prepare self-lubricating ceramic composites by sintering. The shell substance is used to protect the solid lubricant core from reacting with the ceramic matrix during the sintering process and promote the relative density of the ceramic composites. The two new types of self-lubricating ceramic composites showed superior mechanical properties and tribological properties to the traditional self-lubricating ceramic composites.

Lubrication Performance of Graphene as Lubricant Additive in 4-n-pentyl-4′-cyanobiphyl Liquid Crystal (5CB) for Steel/Steel Contacts

The lubrication performance of graphene used as additive in 4-n-pentyl-4′-cyanobiphyl liquid crystal (5CB) for steel/steel contacts was studied on a ball-on-plate tribotester. The friction test results show that when the graphene content in the 5CB was 0.15 wt.%, and the lubricant and friction pairs were heated to 44–46 °C before friction tests, the lubrication performance of the 5CB was most improved. Compared with pure 5CB, 5CB+0.15 wt.% graphene suspension reduced the friction coefficient and wear scar diameter by up to 70.6% and 41.3%, respectively. The lubrication mechanisms have been tentatively proposed according to the test results. We speculate that the excellent lubrication performance of graphene/5CB suspensions may be attributed to the low shear resistance adsorption layer formed by graphene and 5CB molecules on the sliding surfaces. As the protective layer, it not only prevents direct contact between the rough sliding surfaces but also is easy to slide.

Effect of h-BN@Al2O3 on the microstructure and mechanical properties of Si3N4/TiC ceramic composite

Abstract In this study, a new type of (Si3N4/TiC/(h-BN@Al2O3)) ceramic material containing different amounts of coated h-BN were prepared by vacuum hot-pressing sintering. Microstructural analysis showed that the h-BN@Al2O3 particles were dispersed homogeneously and that the particles were tightly bound in the matrix, as determined by scanning electron microscopy. The effects of h-BN@Al2O3 content on the mechanical properties of the composites were also investigated. The optimal content of h-BN@Al2O3 was 10 vol.%. The flexural strength, hardness, and fracture toughness of the material were increased by 22.2 %, 27.0 %, and 5.9 %, respectively, relative to the material to which uncoated h-BN was added.

Development of Zirconia Nanocomposite Ceramic Tool and Die Material Based on Tribological Design

With the development of modern manufacturing technology, die is more and more need in high temperature, high pressure, special working conditions or complex working condition [Liu & Zhou, 2003]. The requirement in mechanical properties of die material becomes higher and higher. It is necessary to improve the new die material [Kar et al, 2004]. Structure ceramics, because of its high hardness, high temperature mechanical property, wear resistance and corrosion resistance, have been widely used [Basu et al, 2004]. However, lower fracture toughness has limited its wide applications. Moreover, the tribological characteristics also need further study [Hirvonen et al, 2006].

Numerical Simulation of Fabrication for Ceramic Tool Materials

Ceramic materials have good mechanical properties, such as high hardness, good wear resistance and elevated-temperature anti-oxidation. So, Ceramic materials are widely applied not only in the field of aeronautics and astronautics, building and mechanics in modern technology, but also in the field of the cutting tool materials. Ceramic tool materials are widely used in the dry cutting and high speed cutting. The preparation of ceramic tool materials includes powdering, forming, hot-press fabrication and machining process. A green compact before sintering is a porous packing of loose powder that is held together by weak surface bonds. The individual particles fabricated together to form a dense, strong monolithic part by sintering. The driving force for the sintering is the reduction in surface free energy of the particle. This reduction is performed by diffusion transport of material. Many factors affected the process of material transport and the exhalation of pores. Therefore, the hot-press fabrication is a very complicated process in which the compact formed of fine powder materials fabricated at the temperature below the melting point of the main constituent for the purpose of gaining the enough strength of the compact by bonding particles together. The hot-press fabrication is a key process, which governs the mechanical properties of the ceramic tool materials as well as the components and content.

Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network

At present, the main method of the ceramic tool and die materials research is still the traditional ‘trial-error’ method which needs a large number of experiments to determine the optimum material compositions. This traditional method requires researchers to repeat experiments and to face to the complex preparation processes as well as the high cost of the experiments, and so on. Therefore, the utilization of advanced and even intelligent design technologies for ceramic material design is extremely necessary. The computational intelligence (CI) technique, as an offshoot of artificial intelligence (AI), is a kind of heuristic algorithm including three categories: neural network, fuzzy system and evolutionary computation. Genetic algorithm (GA) and artificial neural network (ANN) are the two important computational intelligence techniques. In recent, the two techniques especially the ANN have got successful application in the material design of ceramics and metal matrix composites, etc. For instance, some researchers used ANN to predict the functional properties of ceramic materials from compositions (Scott et al, 2007) or the bending strength and hardness of particulate reinforced Al-Si-Mg aluminum matrix composites (Altinkok & Korker, 2004) or the mechanical properties of ceramic tool (Huang et al, 2002) or the percentage of alumina in Al2O3/SiC ceramic cakes and the pore volume fraction (Altinkok & Korker, 2005), etc. ANN is a kind of self-learning technology and back propagation (BP) neural network is one of the simply and commonly used network architectures. BP is based on the gradient descent method where connection weights and thresholds are modified in a direction corresponding to the negative gradient of a backward-propagated error measure (Jiang & Adeli, 2004). Although BP neural network has an advantage of high accuracy, it is often plagued by the local minimum point, low convergence or oscillation effects. In order to overcome the disadvantage of BP neural network, GA is usually used to improve the BP neural network. GA has a strong searching capability and high probability in finding the global optimum solution which is suitable for the early stage of data searching. Although these two techniques seem quite different in the number of involved individuals and the process scheme, they can provide more power of problem solving than either alone (Yen &