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The function of injection molding process is described in the quality, function and costs of injection products from the definition of function. Detailed analysis and evaluation are taken to investigate these three aspects, and the aspect of injection products quality is principally discussed. The analysis and evaluation of injection molding process function will be kept as the judgment of quality of the process.

This paper briefly introduced the principle of Rapid Heat Cycle Molding. Researches and applications of Rapid Heat Cycle Molding were reviewed, and heating method and mold structure were emphatically studied. At last the exiting problems and trend were proposed.

It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.

It is proposed in this paper a novel damage identification method of concrete-filled steel tubular arch bridge using data fusion based on information allocation theory. This method is actually a data fusion process, in which the statistic characteristics of the measured data, such as mean, standard deviation are calculated firstly, and then the bad points are excluded by the Grubbs Guidelines from the data set. After that, the measured data satisfied to certain precision are sent to a data fusion centre where the fusion computation is performed by employing a weighted average algorithm, and the damage location of a CFST arch bridge is determined by the peak value method finally. The efficiency and feasibility of the method proposed is validated by detecting both single- and multi-damage patterns of a two-span CFST arch bridge, and the effect of environmental on identification results is studied simultaneously. The results show that the proposed method is capable of detecting damage and has a good robustness.

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

This paper present a cooling simulation method for External Gas-assisted Injection Molding (EGAIM). To simulate the cooling process of EGAIM, a Finite Element Method model with heat transfer processing with isotropic materials containing inner heat source was established, and pressure was loaded on the interface of mold and part as gas packing pressure. The simulation was used to explore the effect of process parameters on the quality of an example with different thickness ribs with ANSYS. The results showed the method is feasible and reliable.

The forklift design is a creative activity based on the complexity of knowledge and experience. To fully use the successful experience and knowledge of forklift’s design which have been in using and shorten the period of forklift’s development, to enhance the rapid response ability of enterprises to the markets, we put forward the method of forklift’s design reuse based on CBR, at the same time; develop the overall design system of forklift----HCDesign. Furthermore, we analyze the functions, structures and the implementation module of the system to study some of the key technique, using the case to verify the system’s validity.

To extract effectively features and improve damage identification precision, this study proposed an intelligent data-fusion model by integrating fractal theory, probabilistic neural network (PNN) and data fusion. A two-span concrete-filled steel tubular arch bridge in service was used to validate the intelligent model by identifying both single- and multi-damage patterns. The results show that the intelligent model proposed can not only reliably identify damage with different noise levels, but also have an excellent anti-noise capability and robustness.

Most of the input of parametric design system is the specific structure parameter, which is in a detailed design stage. To promote it into conceptual stage, the function-structure mapping problem is proposed. To solve the problem, the function -structure mapping mechanism which is based on logical view and in the framework of axiomatic design is established, the mapping model is solved by optimization method and the mapping procedure is integrated with parametric design system. Function requirements are mapped into a parametric model directly. Finally, a forklift design process validates the method and shows its advantage.

This paper provides new test data and changing regularity of behavior of rectangular concrete-filled steel tubular columns that have been exposed to high temperature and subsequently are subjected to bi-axial force and bending. Fourteen specimens exposed to constant high temperatures were tested to investigate the residual behavior of them. The investigation emphasized the effect of experimental parameters, such as temperature, slenderness ratio, eccentric angle, height-to-width ratio and confining coefficient, on residual bearing capacity and ductility of members. It is found that the bearing capacity and ductility are mainly influenced by the testing parameters. After being heated to a high temperature lower than 600 °C, the columns still have good carrying capacity. The ductility coefficients of all the specimens in the test are not less than 5.6, which indicates that CFST columns subjected to bi-axial compression and bending have good ductility even after high temperature exposure. The experimental results provide the foundation for assessing, repairing and strengthening the fire-damaged structures.

The Fiber Bragg Grating (FBG) sensing technique is suitable for a wide variety of measurements, including temperature, pressure, acceleration, liquid level, etc., and has been applied to many bridges and buildings in the past two decades. The fact that the FBG technique can only monitor and measure strain data for most cases when it is used for deformation measurements impedes application of the FBG sensing technique in civil infrastructures. This paper proposes FBG sensing-based deformation monitoring methods that are applicable to monitoring beam deflection, column inclination angle and mortise-tenon joint dislocation for Chinese traditional timber structures. On the basis of improved conjugated beam theory and geometrical trigonometric function relationship, the relationships between the FBG sensing strain values and the deflection of beam, inclination angle of column, as well as the amount of dislocation of mortise-tenon joint are deducted for Chinese traditional buildings. A series of experiments were conducted to verify the applicability and effectiveness of the proposed deformation monitoring methods. The results show that a good agreement is obtained between the values given by the methods proposed in this paper and other methods. This implies that the proposed deformation monitoring methods are applicable and effective in the health monitoring of Chinese traditional timber structures.

In this paper, the main molding process parameters which are relevant to warpage were optimized by orthogonal experiment and finite element software MPI. The result shows that we can get a series of optimized parameters and know how much each process parameter influences the warpage by combining orthogonal experiments with simulation experiments using MPI, while fewer experiments are needed. Verified by MPI, such series of optimized parameters can keep the warpage in a low level and completely meet the standard of the product.

Product cost estimation in early design stage puzzles researchers for long-time. A new product cost estimation model is proposed, which is applicable for few estimating samples and product information in early design stage. Similarity between the estimating product and the samples are calculated by model of Euclidean distance. Both of utilization ratio of estimating samples and reliability of estimation result are improved based on samples’ sequencing for effect of product cost. The cost estimation model is applied in design of claw of brake disc. The estimation result indicates that the presented model can be used for condition of few estimating samples and product information in early design phase.

The forming mechanism and optimization of the gas-assisted injection molding (GAIM) process have been studied for several years, both at home and abroad. Some researches and experiments about GAIM is summarized in this paper. The GAIM's forming mechanism focuses on research of the filling, gas-assisted filling and gas-assisted packing and cooling stages. While the optimization of the GAIM process pays much more attention to one of the parameters affecting the molding quality.

For the successful realization of product configuration design (PCD) in the condition of mass customization mode, the module encoding methods are studied. By analyzing the product structure model and the process of computer-aided PCD, all related information which is required for the module is decided. This paper proposes a scientific module encoding method suitable for PCD. It describes the geometric features of the modules, the interface relationships and the affiliations between modules. Further more, it introduces additional attribute codes used to evaluate product configuration. By this way the product information can be fully expressed in order to improve the efficiency of PCD.

A small and medium enterprise using ERP system as an example, this paper analyzes its business processes to reengine critical business processes more likely to have errors for the necessity and urgency of process reengineering. The use of AHP identifies weights of evaluation indexes determining whether the critical business processes are error-prone and using the improved flow prioritization matrix method prioritizes business processes. Select the first five business processes as the critical business processes, and choose the first two critical business processes as examples to reengine with the cause-effect analysis method.

Process planning system for hydraulic disk brake for motorcycle based on Microsoft .Net platform is presented. Two modes of process planning which called retrieved process and generated process are studied for assembly process planning and machining process planning. Then process planning system for hydraulic disk brake for motorcycle is developed base on Browse/Sever mode. An example of hydraulic disk brake shows validity of the system.

This paper reports the work of web-based auto-design machining process of special bearing. A template-based process planning method is proposed for auto generating machining process of special bearing, including turning process and grinding process of inner ring or outer ring. Process templates are customized to generate process, procedures size and size tolerance are calculated according to procedure size database. A web-based process planning system is developed based on the proposed method. The use of the system is illustrated with an example.

The material removal mechanisms of sapphire substrate in double-side lapping process were studied. The concepts of brittle vs. ductile machining and two-body vs. three-body abrasion were used to classify the processing mechanisms. The material removal models of double-side lapping process in different mechanisms were analyzed and researched. The experiment showed that the material removal model can describe double-side lapping process of sapphire qualitatively.

In this paper, a new analytical method is proposed to generate concrete random aggregate structure (RAS) in meso-scopic level. This method is particular useful for the progressive failure of concrete under various external loads. In the meso-mechanics level, the concrete is taken as the composite material consisting of aggregate, mortar and interface between them. As a result, it is generated a random aggregate structure in which the shape, size and distribution of the aggregate particles resemble real concrete in the statistical sense using the Monte Carlo random sampling principle in this paper, and the method proposed can generate both regular aggregate particles and irregular ones in 2-Dimmension. It is should be noted that vector concept and some controlling measures are also proposed to avoid generating aggregate particles not consistent with real ones. Numerical examples are simulated to validate the proposed method finally.

This paper presents an innovative product design system based on Quality Function Development (QFD) and Theory of Inventive Problem Solving (TRIZ in Russian abbreviation). House of Quality (HOQ) is the core of QFD. To make QFD more practical and operable for small and medium-sized companies, a simplified model of HOQ is proposed. A set of reasoning scheme for inferring requirements relationships is adopted to facilitate constructing the HOQ. The contradiction in the process of mechanical design is defined by TRIZ as physical contradiction and technical contradiction. These two contradictions are solved through contradiction matrix and inventive principles of TRIZ. The innovative design system mainly uses the tool of contradiction matrix to solve the bottle techniques attained from QFD. By taking the HOQ as its interface the integration between QFD and TRIZ is realized. After conducting quality function development, two outputs can be obtained. They are the technical measure needing improvement and the technical measure that is getting worse while improving the former one. These two measures are taken as inputs of TRIZ module to analyze and solve the problem of product design.

Over the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integrating them into a consistent, accurate and intelligible data set. Meanwhile, since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it has been successfully applied to structural damage detection (Jiang et al. 2004; Klenke et al. 1996; Ko et al. 1999; Ni et al. 2001). In order to make full use of multi-sensor data (or information) from multi-resource and to improve the diagnosis accuracy of the health conditions for complex structures, it is advisable to combine these methods and exploit their individual advantages. In this paper, a 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented. The proposed method is then applied to damage detection and identification of two simulation examples. The result shows that the proposed method is feasible and effective for damage identification.

To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed.

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

This paper proposes a revised counter-propagation network (CPN) model by integrating rough set in structural damage detection, applicable for processing redundant and uncertain information as well as assessing structural health states. Firstly, rough set is used in the model to deal with a large volume of data; secondly, a revised training algorithm is developed to improve the capabilities of the CPN model; and lastly, the least input vectors are input to the revised CPN (RCPN) model, hence the rough set-based RCPN is proposed in the paper. To validate the model proposed, numerical experiments are conducted, and, as a result, six damage patterns have been successfully identified in a steel frame. The influence of measurement noise, the network models adopted, and the data preprocessing methods on damage identification is also discussed in the paper. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also significantly reduces the data storage requirement and saves computing time.

Bridge construction quality inspection and evaluation systems previously developed have been no more than just conventional management information systems, in which no mapping or sophisticated visual analysis capabilities are supported or even considered, not to mention network connectivity, especially Internet access. To address the problem, it is herein presented a WEBGIS-based quality inspection and evaluation system for bridge construction, applicable to not only simply-supported, old-fashioned bridges like arches, girders, etc., but also complex bridges like suspension, cable stayed, and other bridges with unique characteristics. Great efforts have been made to incorporate into the system the features like: real-time visualization, online spatial queries, comprehensive analysis, damage pattern recognition, decision making, and so on. The objective of this study is to demonstrate various new possibilities that Internet and GIS technology could bring to bridge construction quality inspection and evaluation, through the design and implementation of the Shenyang WEBGIS-based quality inspection and evaluation system for bridge construction.

Publisher Summary Concrete-filled rectangular steel tubes (CFRST) are widely applied in many engineering structures as they have many superior behaviors, such as high bearing capacity, good fire resistance, and advanced economic indices. Research on the behavior of CFST at ambient temperature is relatively mature, but less is reported on the behavior of CFST after elevated temperature. Especially, there is no existing report on biaxial eccentric compression behavior of CFRSTs after constant high temperature. To assess the residual behavior of CFRSTs after constant high temperature, the principles of calculation for load vs. mid-span deflection curves of them subjected to biaxial eccentric load is formulated, a calculation procedure is made using MATLAB, and its effectiveness is validated by the test results.

This paper presents a novel two-stage damage detection method by integrating modal strain energy and revised particle swarm optimization (RPSO). In the first stage, the modal strain energy change ratio (MSECR) is used to roughly identify the locations of damaged elements via an appropriate MSECR threshold which is determined through parameter estimation. In the second stage, RPSO that integrates evolutionary theory with general PSO is used to precisely locate and quantify the damage with the gravity position of the selected excellent particles in the current entire population taken into consideration. Two numerical simulations and a seven-story steel frame experiment in laboratory conditions are performed to validate the proposed method, and a comparison is made between the proposed approach and existing methods. The results show that: (1) the proposed method can not only effectively locate damage, but also accurately evaluate the extent of damage. Meanwhile, it also enjoys good noise-tolerance and adaptability; (2) the damage threshold of the MSECR presented in this paper can be determined by the parameter estimation and reliability index, and then used to reduce the number of elements to be analyzed and to improve the computation efficiency in the second stage; and (3) compared with general PSO algorithm, RPSO is more efficient and robust for damage detection with a better noise-tolerance. This study shows that the proposed method can provide a reliable and fast tool to accurately identify, locate and quantify single- and multi-damage of complex engineering structures.

Since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it shows great promise for structural damage detection in noisy conditions. In the traditional PNN, the smoothing parameter is unique to all pattern classes and is specified artificially, which may result in inaccuracy of identification results and computational inefficiency. In this study, we explore the damage localization of the Tsing Ma suspension bridge by use of an adaptive PNN that optimally determines different smoothing parameters for different pattern classes through an iteration scheme. A series of pattern classes are defined for the Tsing Ma Bridge to depict different damage locations.