Qingsong Ai

Recent development on product modelling : a review

Product modelling is a pivotal activity in product development (PD) processes. Well-defined product models organize product data, production information and knowledge to satisfy the requirements of the rapid changes of the PD environment. In this paper, a comprehensive review is carried out on the recent development of product modelling technology. Four types of product modelling methodologies are discussed in detail. Two object-oriented product modelling methods: STEP-based product modelling and UML-based product modelling are reviewed and compared. The research gaps and issues are identified. To implement the product modelling into current integrated manufacturing environment, a generic product modelling framework is proposed. The future research trend of product modelling is also discussed.

A new digital watermarking scheme for 3D triangular mesh models

This paper proposes a new watermarking scheme for three-dimensional (3D) triangular mesh models. In this new scheme, feature points from the rapid changing regions are used to uniquely partition a 3D mesh model into Voronoi patches. Each Voronoi patch is then projected to a reference square plane and the corresponded range image is generated. By employing the existing two-dimensional (2D) image watermarking algorithms, watermark information is embedded into the transform domain of each range image. The watermarked 3D mesh model is obtained by perturbing the vertexes of each patch according to its watermarked range image. In order to make the watermarking scheme robust against various forms of attacks while preserving the perceptibility of the model, the length of the watermark information, the number of the feature points and the embedding strength of the watermark are made adaptive with respect to the geometry complexity of the 3D mesh models. Experimental results show that the proposed watermarking scheme has good imperceptibility and performs well under common 3D watermarking attacks such as mesh simplification, adding noise and cropping.

An analytical approach to customer requirement information processing

‘Customer requirements’ (CRs) management is a key component of customer relationship management (CRM). By processing customer-focused information, CRs management plays an important role in enterprise systems (ESs). Although two main CRs analysis methods, quality function deployment (QFD) and Kano model, have been applied to many fields by many enterprises in the past several decades, the limitations such as complex processes and operations make them unsuitable for online businesses among small- and medium-sized enterprises (SMEs). Currently, most SMEs do not have the resources to implement QFD or Kano model. In this article, we propose a method named customer requirement information (CRI), which provides a simpler and easier way for SMEs to run CRs analysis. The proposed method analyses CRs from the perspective of information and applies mathematical methods to the analysis process. A detailed description of CRIs acquisition, classification and processing is provided.

Practical Velocity Tracking Control of a Parallel Robot Based on Fuzzy Adaptive Algorithm

Due to the advantages of its compact structure and high operation accuracy, the six degrees of freedom (6-DOF) parallel platform has been widely used as a carrier of medical rehabilitation devices. Fuzzy adaptive algorithm does not depend on the mathematical model of controlled object, which possesses good nonlinear characteristics. Those entire features make it an effective method to control such complex and coupling platforms. To facilitate the application of robotics in lower limb rehabilitation fields, a robotic system in practical environment was established based on kinematics modeling of the 6-DOF Stewart-based platform. In order to improve the velocity tracking accuracy, this paper proposed a closed-loop control strategy based on fuzzy adaptive algorithm. The velocity feedback information was utilized to modify the PID parameters adaptively in realtime through fuzzy inference units. Several experiments in practical environment were conducted, and the results demonstrated that the proposed algorithm could effectively reduce the speed jitter, enhance the position and velocity tracking precision of the robot, and the reliability and robustness of the system could also be ensured.

Fuzzy Sliding Mode Control of a Multi-DOF Parallel Robot in Rehabilitation Environment

Multi-degrees of freedom (DOF) parallel robot, due to its compact structure and high operation accuracy, is a promising candidate for medical rehabilitation devices. However, its controllability relating to the nonlinear characteristics challenges its interaction with human subjects during the rehabilitation process. In this paper, we investigated the control of a parallel robot system using fuzzy sliding mode control (FSMC) for constructing a simple controller in practical rehabilitation, where a fuzzy logic system was used as the additional compensator to the sliding mode controller (SMC) for performance enhancement and chattering elimination. The system stability is guaranteed by the Lyapunov stability theorem. Experiments were conducted on a lower limb rehabilitation robot, which was built based on kinematics and dynamics analysis of the 6-DOF Stewart platform. The experimental results showed that the position tracking precision of the proposed FSMC is sufficient in practical applications, while the velocity chattering had been effectively reduced in comparison with the conventional FSMC with parameters tuned by fuzzy systems.

Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model

Purpose – The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach – An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. Findings – Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot ...

A method for determining customer requirement weights based on TFMF and TLR

‘Customer requirements’ (CRs) management plays an important role in enterprise systems (ESs) by processing customer-focused information. Quality function deployment (QFD) is one of the main CRs analysis methods. Because CR weights are crucial for the input of QFD, we developed a method for determining CR weights based on trapezoidal fuzzy membership function (TFMF) and 2-tuple linguistic representation (TLR). To improve the accuracy of CR weights, we propose to apply TFMF to describe CR weights so that they can be appropriately represented. Because the fuzzy logic is not capable of aggregating information without loss, TLR model is adopted as well. We first describe the basic concepts of TFMF and TLR and then introduce an approach to compute CR weights. Finally, an example is provided to explain and verify the proposed method.

A MGrid-based information sharing system for distributed product information sharing and exchange

Grid technology is an emerging technology for the sharing of heterogeneous information globally distributed. This paper presents an information sharing system based on such a technology for a distributed injection moulding manufacturing enterprise. A Manufacturing Grid (MGrid) is proposed for information sharing in distributed manufacturing environment. Based on the MGrid, an information sharing system is developed to support the heterogeneous information sharing across departmental boundaries. By employing OGSA-DAI (Open Grid Services Architecture Data Access and Integration), the heterogeneity and the regional distribution of product information are managed efficiently. Users from different departments of the manufacturing enterprise can utilise the remote information as conveniently as they use local information for their product development tasks. A Grid portal is designed to provide uniform and friendly interfaces for users to interact with the information sharing system in a transparent manner. In order to improve the efficiency of the system, the workflow technology and the concurrently distributed information query process are also investigated in this paper. Case studies are carried out in an international injection moulding enterprise and results show the proposed system provides an efficient mechanism for the enterprise to quickly develop customised products in an economical way.

A STEP-compliant product data model for injection moulding products

In the current rapidly changing manufacturing environment, most manufacturing companies have realised that the ability to quickly develop a customised product at competitive prices is very crucial for them to survive in the keen competitive global market. Product modelling has been recognised as one of the key factors in determining the success of various product development strategies and for industrial competitiveness now and in the future. Standard for Exchange of Product model data (STEP) is an international standard designed to provide a complete, unambiguous, computer-readable definition of the physical and functional characteristic of a product through its lifecycle. This paper proposes a STEP-compliant product data model to fully support the complete product information representation and management for injection moulding product development. The data model is developed based on the structure of an injection moulding company. The entire data model consists of six data models which are defined to express information from various departments of the company. The data model is described in EXPRESS language, which can be integrated with other STEP-based product data models and databases. Consequently, the proposed system can be further extended for helping other injection moulding manufacturers to improve their product development process. An information sharing prototype system is implemented based on the proposed product data model to demonstrate the feasibility of the approach.

A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%.