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This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

The purpose of this study is to obtain a titanium mandibular implant that possesses a personalized external shape for appearance recovery, a supporting structure for physiological loading and numerous micro-pores for accelerating osseointegration.

A three-dimensional intact mandibular model of a beagle dog was created from cone-beam computerized tomography scans. A segment of the lower jaw bone was resected and replaced by a personalized implant with comprehensive structures including a customized external shape, supporting structures and micro-pores, which were designed by topology optimization. Then with FEM analysis, the stress, displacement distribution and compliance of the designed implant were compared with the non-optimized model. The weight of the optimized implant that was fabricated by SLM with titanium alloy powder was measured and contrasted with the predicted non-optimized model for evaluating the viability of the design.

The FEM results showed the peaks of von Mises stress and displacement on the optimized implant were much lower than those of the implant without optimization. With topology optimization, the compliance of the implant decreased significantly by 53.3 per cent, and a weight reduction of 37.2 per cent could be noticed.

A design strategy for personalized implant, with comprehensive structures and SLM as the fabrication method, has been developed and validated by taking a canine mandible as the case study. With comprehensive structures, the implant presented good biomechanical behaviors thanks to the most appropriate supporting structures obtained by optimal design. The topological optimal design combined with SLM printing proved to be an effective method for the design and fabrication of personalized implant with complex structures.

Lack of trust in construction projects will lead to poor project performance or project failure, indicating the importance of trust-building. Existing studies have developed various trust models, while most studies covered limited trust factors, failed to clarify their meanings and relationships or lacked qualitative or quantitative evidence. Thus, this study aims to develop a measurement model of trust in construction projects with theoretical justification as well as qualitative and quantitative data.

A literature review was conducted to identify conceptual types, factors and indicators of trust. Individual interviews and focus groups were performed to test the proposed framework with qualitative data. A survey and confirmatory factor analysis (CFA) method were utilized to build the measurement model of trust using quantitative data in BIM-assisted projects.

The proposed trust framework covered the four conceptual types, four factors (integrity, competency, benevolence and commitment) and 13 indicators, supported by the results of interviews and focus groups. The measurement model of trust from CFA results supported the significant, positive, and one-to-one relationships between 13 indicators and four factors of trust in BIM-assisted projects.

Theoretically, the study provides new insights into the multi-dimensional nature of trust. In practice, the findings could facilitate trustors and trustees to better understand, build, measure and enhance trust in construction projects.

The purpose of this paper is to propose a defect detection method of bare printed circuit boards (PCB) with high accuracy.

First, bilateral filtering of the PCB image was performed in the uniform color space, and the copper-clad areas were segmented according to the color difference among different areas. Then, according to the chaotic characteristics of the spatial distribution and the gradient direction of the edge pixels on the boundary of the defective areas, the feature vector, which evaluates quantitatively the significant degree of the defect characteristics by using the gradient direction information entropy and the uniform local binary patterns, was constructed. Finally, support vector machine classifier was used for the identification and localization of the PCB defects.

Experimental results show that the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids.

Considering the limitations of describing all kinds of defects on bare PCB by using single kind of feature, the gradient direction information entropy and the local binary patterns were fused to build a feature vector, which evaluates quantitatively the significant degree of the defect features.

This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy.

A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method.

Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively.

A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

The paper aims to review the fundamental concept of quality function deployment (QFD) and discusses the fact that a road to success for a new product development is the identification of customers' requirements and their conversion into engineering design requirements. Thereafter, it seeks to review the subject and to study four new cases on the topic of QFD.

The paper discusses key elements of QFD and the fact that the vision for the development of a comprehensive quality system can be built upon the principles of QFD taking customer requirements into consideration and relating that to the design requirements.

To make product development task successful and bringing competitive advantages to the core business, management must be committed to the needs of the customers through marketing surveys and implementing that into the process of product development by converting them into engineering design requirements.

The method has been used successfully in practice in areas such as: facility locations, marketing strategies, robot selection, ERP selection, software development, and sports.

The paper reviews QFDs, and its extensions such as fuzzy QFD, analytic hierarchy process, analytic network process and QFD, statistically extended QFD, dynamic QFD, and other extensions of that. In addition to that, cases covering topics of: ship of quality, cost‐design parameter modeling, enhanced version of the quality function development, financial factors and uncertainties in the product design process with fuzzy formulation, and a model for prioritizing and designing rule changes for the game of soccer, are also reviewed.

The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

Quality function deployment (QFD) has been used to translate customer requirements into engineering characteristics of a product, while benchmarking was developed to search for the best industry practices, which will lead to exceptional performance through the implementation of these best practices. However, no attempt has been made to integrate QFD with benchmarking to identify the best practices of QFD model. This paper aims to classify the QFD models and thereby applying benchmarking process to propose the best practices of QFD model.

The fundamental benchmarking model developed by Camp has been used to benchmark the existing QFD models available in the literature.

Benchmarking the QFD models revealed about 36 QFD steps in the first phase of the house of quality. The tools used in solving for each practice are also reported.

The proposed model is conceptual and it requires validation by implementing the same in an organization to understand its effectiveness.

Utilizing the benchmarking process to develop the best practices of QFD model is an original concept.

An investigation was conducted into the impact of various process parameters on the surface and subsurface quality of glass-ceramic materials, as well as the mechanism of material removal and crack formation, through the use of ultrasonic-assisted grinding.

A mathematical model of crack propagation in ultrasonic-assisted grinding was established, and the mechanism of crack formation was described through the model. A series of simulations and experiments were conducted to investigate the impact of process parameters on crack depth, surface roughness, and surface topography during ultrasonic-assisted surface and axial grinding. Additionally, the mechanism of crack formation was explored.

During ultrasonic-assisted grinding, the average grinding forces are between 0.4–1.0 N, which is much smaller than that of ordinary grinding (1.0–3.5 N). In surface grinding, the maximum surface stresses between the workpiece and the tool gradually decrease with the tool speed. The surface stresses of the workpiece increase with the grinding depth, and the depth of subsurface cracks increases with the grinding depth. With the increase of the axial grinding speed, the subsurface damage depth increases. The roughness increases from 0.780um/1.433um.

A mathematical model of crack propagation in ultrasonic-assisted grinding was established, and the mechanism of crack formation was described through the model. The deformation involved in the grinding process is large, and the FEM-SPH modeling method is used to solve the problem that the results of the traditional finite element method are not convergent and the calculation efficiency is low.