Showing papers in "Robotics and Computer-integrated Manufacturing in 2010"

Low cost integration of additive and subtractive processes for hybrid layered manufacturing

Abstract: While CNC machining (subtractive method) is the only option when it comes to high quality components, it demands greater human intervention to generate the CNC programs, making it a slow and costly route. On the other hand, Rapid Prototyping (additive method) is able to convert the design into the physical objects without any human intervention. But its total automation comes with compromises in the qualities of geometry and material. A hybrid layered manufacturing process presented here combines the best features of both these approaches. In this process the near-net shape of the object is first built using weld-deposition; the near-net shape is then finish machined subsequently. Time and cost saving of this process can be attributed to reduction in NC programming effort and elimination of rough machining. It is envisioned as a low cost retrofitment to any existing CNC machine for making metallic objects without disturbing its original functionalities. Near-net shape building and finish machining happening at the same station is the unique feature of this process. A customized software generates the NC program for near-net shape building. The intricate details of integrating arc welding unit with a CNC milling machine are presented in this paper.

Review: An overview of dynamic parameter identification of robots

Abstract: Due to the importance to model-based control, dynamic parameter identification has attracted much attention. However, until now, there is still much work for the identification of dynamic parameters to be done. In this paper, an overview is given of the existing work on dynamic parameter identification of serial and parallel robots. The methods for estimating the dynamic parameters are summarized, and the advantages and disadvantages of each method are discussed. The model to be identified and the trajectory optimization are reviewed. Further, the methods for validating the estimated model are summarized and the application of dynamic parameter identification is mentioned. The results of this review are useful for manufacturers of robots in selecting proper identification method and also for researchers in determining further research areas.

Review: Advances in 3D data acquisition and processing for industrial applications

Abstract: A critical task of vision-based manufacturing applications is to generate a virtual representation of a physical object from a dataset of point clouds. Its success relies on reliable algorithms and tools. Many effective technologies have been developed to solve various problems involved in data acquisition and processing. Some articles are available on evaluating and reviewing these technologies and underlying methodologies. However, for most practitioners who lack a strong background on mathematics and computer science, it is hard to understand theoretical fundamentals of the methodologies. In this paper, we intend to survey and evaluate recent advances in data acquisition and progressing, and provide an overview from a manufacturing perspective. Some potential manufacturing applications have been introduced, the technical gaps between the practical requirements and existing technologies discussed, and research opportunities identified.

A repair and overhaul methodology for aeroengine components

Abstract: Repair and maintenance of aeroengine components have been one of the main tasks to be overwhelmed by aerospace industries due to continual increase in raw material and manufacturing costs. In this paper, an advanced methodology for the repair of complex geometry and expensive components via reverse engineering, free-form surface modeling and machining is presented. The methodology has been successfully implemented on a critical aeroengine component, i.e. thin-curved compressor blade. The approach involves the integration of 3D non-contact digitization, adaptive free-form surface reconstruction and multi-axis milling operation. Each operation is individually automated and inter-connected each other in order to reduce the total repair time. The workshop results have shown that the proposed repair methodology can be considered a reliable and time-cost effective as compared with the current repair approaches.

Selection of industrial robots using compromise ranking and outranking methods

Abstract: Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is 'VIsekriterijumsko KOmpromisno Rangiranje' (VIKOR), a compromise ranking method and the other one is 'ELimination and Et Choice Translating REality' (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.

Chaotic particle swarm optimization for assembly sequence planning

Abstract: Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first Then, the optimization model of assembly sequences is presented The mapping rules between the optimization model and the traditional PSO model are given The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints

Ontology-based reconfiguration agent for intelligent mechatronic systems in flexible manufacturing

Abstract: This paper proposes a novel approach to achieving fast reconfiguration of modular manufacturing systems, based on an ontology-based reconfiguration agent. The agent uses ontological knowledge of the manufacturing environment for the purpose of reconfiguration without human intervention. The current mass customization era requires increased flexibility and agility in the manufacturing systems to adapt changes in manufacturing requirements and environments. Our configuration agent minimises the overheads of the current reconfiguration process by automating it. It infers facts about the manufacturing environment from the ontological knowledge model and then decides whether the current environment can support the given manufacturing requirements. This paper proposes the agent architecture enabling the integration between the high level planning with the distributed low level control compliant with the upcoming IEC 61499 function blocks standard.

Cost-efficient drilling using industrial robots with high-bandwidth force feedback

Abstract: Here we present a method for high-precision drilling using an industrial robot with high-bandwidth force feedback, which is used for building up pressure to clamp-up an end-effector to the work-piece surface prior to drilling. The focus is to eliminate the sliding movement (skating) of the end-effector during the clamp-up of the end-effector to the work-piece surface, an undesired effect that is due to the comparatively low mechanical stiffness of typical serial industrial robots. This compliance also makes the robot deflect due to the cutting forces, resulting in poor hole position accuracy and to some extent in poor hole quality. Recently, functionality for high-bandwidth force control has found its way into industrial robot control systems. This could potentially open up the possibility for robotic drilling systems with improved performance, using only standard systems without excessive extra hardware and calibration techniques. Instead of automation with expensive fixtures and precise machinery, our approach was to make use of standard low-cost robot equipment in combination with sensor feedback. The resulting sliding suppression control results in greatly improved hole positioning and quality. The conceptual idea behind the force control is useful also in many other robotic applications requiring external sensor feedback control.

Autonomous navigation of an automated guided vehicle in industrial environments

Abstract: The research presented in this paper approaches the issue of navigation using an automated guided vehicle (AGV) in industrial environments. The work describes the navigation system of a flexible AGV intended for operation in partially structured warehouses and with frequent changes in the floor plant layout. This is achieved by incorporating a high degree of on-board autonomy and by decreasing the amount of manual work required by the operator when establishing the a priori knowledge of the environment. The AGV's autonomy consists of the set of automatic tasks, such as planner, perception, path planning and path tracking, that the industrial vehicle must perform to accomplish the task required by the operator. The integration of these techniques has been tested in a real AGV working on an industrial warehouse environment.

Design optimization of a spatial six degree-of-freedom parallel manipulator based on artificial intelligence approaches

Abstract: Optimizing the system stiffness and dexterity of parallel manipulators by adjusting the geometrical parameters can be a difficult and time-consuming endeavor, especially when the variables are diverse and the objective functions are excessively complex. However, optimization techniques that are based on artificial intelligence approaches can be an effective solution for addressing this issue. Accordingly, this paper describes the implementation of genetic algorithms and artificial neural networks as an intelligent optimization tool for the dimensional synthesis of the spatial six degree-of-freedom (DOF) parallel manipulator. The objective functions of system stiffness and dexterity are derived according to kinematic analysis of the parallel mechanism. In particular, the neural network-based standard backpropagation learning algorithm and the Levenberg-Marquardt algorithm are utilized to approximate the analytical solutions of system stiffness and dexterity. Subsequently, genetic algorithms are derived from the objective functions described by the trained neural networks, which model various performance solutions. The multi-objective optimization (MOO) of performance indices is established by searching the Pareto-optimal frontier sets in the solution space. Consequently, the effectiveness of this method is validated by simulation.

Optimal selection of robots by using distance based approach method

Abstract: A deterministic quantitative model based on Distance Based Approach (DBA) method has been developed for evaluation, selection and ranking of robots, which is a concept hitherto not employed in selection problem of this kind. As a significant development over and above past approaches to robot selection, it recognizes the need for, and processes the information about, relative importance of attributes for a given application, without which inter-se-attribute comparison could not be accomplished. It successfully presents the results of this information processing in terms of a merit value which is used to rank the robots. In order to demonstrate the aptness of using DBA method as a decision aid, the results so obtained have been compared with other techniques and methods available in the open literature. Sensitivity analysis test have been performed to analyze the critical and non-critical performance attributes for a robot. The model developed using DBA method is explained and illustrated with an example problem.

Improving feasibility of robotic milling through robot placement optimisation

Abstract: Milling performed with robots is quite demanding, even for low-strength materials, due to the high accuracy requirements, the generally high and periodically varying milling forces and the low stiffness of robots compared to CNC machine tools. In view of the generally improved recently robot stiffness, it is desirable to perform the milling operation in regions of the robot's workspace where manipulability, both kinematic and dynamic, is highest, thereby exhausting the robot's potential to cope with the process. In addition, by selecting the most suitable initial pose of the robot with respect to the workpiece, a reduction in the range of necessary joint torques may be reached, to the extent of alleviating the heavy requirements on the robot. Two genetic algorithms (GAs) are employed to tackle these problems. The values of several robot variables, such as joint positions and torques, which are needed by the genetic algorithms, are calculated using inverse kinematics and inverse dynamics models. In addition, initial positions and poses leading to singularities along the milling path are penalized and, thus, avoided. The first GA deals solely with robot kinematics to maximize manipulability. The second GA takes into account milling forces, which are computed numerically according to the particular milling parameters, to minimise joint torque loads.

Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots

Abstract: Laser interferometry-based sensing and measurement (LISM) technique was originally investigated to perform dynamic measurements of the end effector of a robot manipulator in motion. This technique can provide dynamic position measurements in real time and has high accuracy, large working space, high sampling rate and automatic target tracking. In this paper, a methodology using LISM technique is proposed to perform laser interferometry-based guidance (LIG) for accurate positioning of a robot manipulator in high precision manufacturing operations. The methodology utilizes the LISM apparatus to guide the robot's end effector to a desired location or along a desired path by directing the robot to follow the trajectory mapped by the laser beam. This is accomplished through the establishment of techniques for path generation, sensing and data acquisition and guidance error determination and compensation in the control algorithm. The algorithms for this methodology, together with the measurement and analysis techniques are described. A number of experiments are carried out to examine and validate the proposed LIG technique. Experimental results show that the established technique can effectively improve the positioning accuracy of the robot manipulator.

Conceptual process planning - an improvement approach using QFD, FMEA, and ABC methods

Abstract: Conceptual process planning (CPP) is an important technique for assessing the manufacturability and estimating the cost of conceptual design in the early product design stage. This paper presents an approach to develop a quality/cost-based conceptual process planning (QCCPP). This approach aims to determine key process resources with estimation of manufacturing cost, taking into account the risk cost associated to the process plan. It can serve as a useful methodology to support the decision making during the initial planning stage of the product development cycle. Quality function deployment (QFD) method is used to select the process alternatives by incorporating a capability function for process elements called a composite process capability index (CCP). The quality characteristics and the process elements in QFD method have been taken as input to complete process failure mode and effects analysis (FMEA) table. To estimate manufacturing cost, the proposed approach deploys activity-based costing (ABC) method. Then, an extended technique of classical FMEA method is employed to estimate the cost of risks associated to the studied process plan, this technique is called cost-based FMEA. For each resource combination, the output data is gathered in a selection table that helps for detailed process planning in order to improve product quality/cost ratio. A case study is presented to illustrate this approach.

Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem

Abstract: The paper addresses the problem of multi-depot vehicle routing in order to minimize the delivery time of vehicle objective Three hybrid heuristics are presented to solve the multi-depot vehicle routing problem Each hybrid heuristic combines elements from both constructive heuristic search and improvement techniques The improvement techniques are deterministic, stochastic and simulated annealing (SA) methods Experiments are run on a number of randomly generated test problems of varying depots and customer sizes Our heuristics are shown to outperform one of the best-known existing heuristic Statistical tests of significance are performed to substantiate the claims of improvement

Green product design through product modularization using atomic theory

Abstract: With increasing environmental consciousness and the establishment of environmental protection regulations, green product design not only plays a crucial role in a modern industry but is also becoming the main focus of the future market. In this paper, an innovative method is presented that uses the concepts of atomic theory to solve design modularization problems for green product design. With the developed method, products can be modularized based upon given green constraints, e.g., material compatibility, part recyclability, and part disassemblability. The developed method can help engineers effectively create green designs in the initial design stage, based on product lifecycle requirements. With green considerations incorporated into new modules, a new design can be created that improves upon an original design, with respect to environmental impacts. Product designers can use our method to compare differences between their original designs and the new green modules and then perform necessary design modifications. A table lamp and a motor are used as case study examples to show the effectiveness of the atomic-theory-based green product design method.

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Abstract: Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.

An accurate adaptive NURBS curve interpolator with real-time flexible acceleration/deceleration control

Abstract: Parametric interpolation has been widely used in CNC machining because of its advantages over the traditional linear or circular interpolation. Many researchers focused on this field and have made great progress in the specific one, NURBS curve interpolation. These works greatly improved the CNC machining with constant feedrate, confined chord error and limited acceleration/deceleration. However, during CNC machining process, mechanical shocks to machine tool caused by the undesired acceleration/deceleration profile will dramatically deteriorate the surface accuracy and quality of the machined parts. This is, in most occasions, very harmful to machine tools. In this paper, an accurate adaptive NURBS curve interpolator is proposed with consideration of acceleration-deceleration control. The proposed design effectively reduces the machining shocks by constraining the machine tool jerk dynamically. Meanwhile, the constant feedrate is maintained during most time of machining process, and thus high accuracy is achieved while the feedrate profile is greatly smoothed. In order to deal with the sudden change of the acceleration/deceleration around the corner with large curvature, a real-time flexible acceleration/deceleration control scheme is introduced to adjust the feedrate correspondingly. Case study has been taken to verify the feasibility and advantages of the proposed design.

A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems

Abstract: This paper presents a constraint programming (CP) methodology to deal with the scheduling of flexible manufacturing systems (FMSs). The proposed approach, which consists of both a model and a search strategy, handles several features found in industrial environments, such as limitations on number of tools in the system, lifetime of tools, as well as tool magazine capacity of machines. In addition, it tackles the problem in a integrated way by considering tool planning and allocation, machine assignment, part routing, and task timing decisions altogether in the approach. The formulation, which is able to take into account a variety of objective functions, has been successfully applied to the solution of test problems of various sizes and degrees of difficulty.

Two-wheeled mobile robot motion control in dynamic environments

Abstract: In this paper, a feedback control scheme of a two-wheeled mobile robot is explored in dynamic environments. In the existence of local minima, the design of controller is based on Lyapunov function candidate and considers virtual forces information including detouring force. Simulation results are presented to show the effectiveness of the proposed control scheme.

Motion navigation for arc welding robots based on feature mapping in a simulation environment

Abstract: The efficiency of the off-line programming for the arc welding robots depends on the complexity of the workpieces and the welding experience of the technician. When the workpieces or fixtures are redesigned or the robot workstation layout is changed, the result of prior off-line programming cannot be reused. With the thought of CAD/CAPP/CAM integration, this paper presents a feature mapping algorithm, which converts the design features of the workpieces to the machining features of the seams. Then a motion navigation method based on feature mapping in a simulation environment is proposed. This method comprises initial position guiding and seam tracking. For initial position guiding, a motion path with the least energy consumption is generated based on the weighted optimal control; then the path is revised for avoiding obstacles by setting the path tags dynamically. For seam tracking, the seam is dispersed into discrete welding points; then the welding gun moves through each point according to the welding sequence. The method is implemented in the development of an off-line programming simulation system. Industrial cases of the welding robot workcell layout for automobile door frames show the method can make the off-line programming much more effective.

Incorporating the FCM-BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant

Abstract: To enhance the performance of the internal due date assignment in a wafer fab even further, this study incorporated the fuzzy c-means-back propagation network (FCM-BPN) approach with a nonlinear programming model. In the proposed methodology, the jobs are first classified into several categories by fuzzy c-means. Then, an individual back propagation network is constructed for each category to predict the completion time of the jobs. Subsequently, an individual nonlinear programming model is constructed for each back propagation network to adjust the connection weights in the back propagation network, allowing us to determine the internal due dates of the jobs in the category. The nonlinear programming model is finally converted into a goal programming problem that can be solved with existing optimization software. According to the experimental results, the proposed methodology outperforms the baseline multiple linear regression (MLR) approach by 24% in predicting the job completion/cycle times. In addition, the proposed methodology also guarantees that all jobs can be finished before the established internal due dates, without adding too large a fudge factor, and without sacrificing the accuracy of the completion/cycle time forecasts.

The general architecture of adaptive robotic systems for manufacturing applications

Abstract: Adaptive system often refers to the adaptive control system. In this paper, we discuss the adaptive system concept from an architectural perspective of the system. We use the robotic system as an example to illustrate the adaptive system concept, because the robotic system is a generic system underlying many manufacturing systems. Specifically, the paper presents our work on the development of a general architecture of the adaptive robotic system for manufacturing applications. The contribution of this paper includes: (i) elaboration of the concept of adaptive system and classification of means to make the system adaptive and (ii) formulation of a general architecture of the adaptive robotic system based on the criterion that the more system variants out of the architecture implies the more generality of the architecture. Throughout this paper, we use an industrial robotic system for illustration.

The construction of a collaborative-design platform to support waste electrical and electronic equipment recycling

Abstract: Recycling of waste electrical and electronic equipment (WEEE) is a very important subject not only from the viewpoint of waste treatment but also from the viewpoint of recovery of valuable materials. In the past, some obstacles make recycling challenging for today's manufactured products. First, it is difficult to gain all the information necessary to plan for the recycling evaluation, as most design information is owned and kept by suppliers. Another problem in recycling end-of-life (EOL) products is a lack of technologies to handle the very complex products that are being discarded today, because the knowledge of how to do so is owned by the recycler. This research demonstrates how to support WEEE recycling analysis by environmental information with the part of bill of material. A collaborative-design platform is further constructed and collected all the needed information using computer-aided design (CAD), enterprise resource planning (ERP), and product life-cycle management (PLM) systems. Through this platform, suppliers are required to provide component information to enable the manufacturer's design for disassembly and recycling analysis. The results demonstrate that designers can obtain disassembly and recycling information through the model, so that desirable changes can be made in the early stages of a design. An industrial case study from Taiwan is also provided to demonstrate the use of this model.

Fixture knowledge model development and implementation based on a functional design approach

Abstract: The development of a knowledge model applied to fixture design is a complex task. The main purpose of such model is the development of a knowledge-based application to assist fixture designers. It comprises a detailed specification of the types and structures of data involved in the execution of the inference process needed to create a fixture solution for machining a raw part. A development method together with a knowledge model for automating fixture design is proposed. The development was divided into three parts: Design Process Model, definition of Top-level functional functions and Product Knowledge Model. Adopting a functional design approach, the fixture design solution was created in two levels: functional and detailed. The functional level is based on fixture functional elements and the detailed one is based on fixture commercial elements. The definitions and concepts used in the application are specified in several Units of Knowledge (UoK) that comprises the Fixture Knowledge Model. Common Knowledge Analysis and Design Structuring (CommonKADS), Methodology and software tools Oriented to KBE Applications (MOKA), Integrated DEFinition for Function Modelling (IDEF0) and Unified Modelling Language (UML) are the methodologies and techniques used in the proposed method. Finally, a prototype KBE application for fixture design was developed.

Supplier development practice: Arising the problems of upstream delivery for a food distribution SME in the UK

Abstract: The paper aims to emphasize on the impacts of the supplier development on reducing the defects in supplier quality for a food distribution small-medium sized enterprise (SME). An empirical study was conducted to measure the performance of the suppliers in three different key performance indicators of the outsourcing and supplier's performance to arise the existing problems via information exchange, data collection and data analysis. It was found that supplier development through data and information exchange and better communication by any food distribution SME raises the problems more promptly. This can dramatically change the supplier's behavior to improve the quality of the supplier's service and products. It is suggested that more research is required to raise other key performance indicators and their related problems and to develop more improvement practices. Six sigma methodologies could be the potential good practices to be focused in future research studies. Supplier performance measurement, which encompasses data exchange and data collection, develops the systematic flow of information, which potentially improves the flow of goods and the whole food supply chain to address the final consumer satisfaction. The research took a novel approach in adopting some transport related key performance indicators of the food supply to the food distribution and retailing sector, which is almost a new approach in food industry.

Hybrid plasma deposition and milling for an aeroengine double helix integral impeller made of superalloy

Abstract: Superalloy integral impeller is a vital functional component of an aeroengine, but it is very difficult to manufacture if using the traditional five-axis NC machining method because of its complex free surfaces. Lamination deposition technology using high energy beams can overcome these difficulties and can be used to directly manufacture complex superalloy double helix integral impellers. However, subsequent re-machining should be required to guarantee adequate dimensional precision. In this paper, it is proposed to manufacture the superalloy double helix integral impeller using an approach of HPDM (hybrid plasma deposition and milling) which combines plasma deposition manufacturing as an additive technology and milling as a subtractive technique. Compared with the existing RP methods using high energy beams, HPDM solved the key problems of insufficient dimensional precision and surface quality caused by the step effect and lacking of supporting. To demonstrate the effectiveness of the proposed approach, an aeroengine superalloy double helix integral impeller with good performances of dimensional precision, microstructure and mechanics was rapidly manufactured by HPDM.

Experimental kinematic calibration of parallel manipulators using a relative position error measurement system

Abstract: Because of errors in the geometric parameters of parallel robots, it is necessary to calibrate them to improve the positioning accuracy for accurate task performance. Traditionally, to perform system calibration, one needs to measure a number of robot poses using an external measuring device. However, this process is often time-consuming, expensive and difficult for robot on-line calibration. In this paper, a methodical way of calibration of parallel robots is introduced. This method is performable only by measuring joint variable vector and positioning differences relative to a constant position in some sets of configurations that the desired positions in each set are fixed, but the moving platform orientations are different. In this method, measurements are relative, so it can be performed by using a simple measurement device. Simulations and experimental studies on a Hexaglide parallel robot built in the Sharif University of Technology reveal the convenience and effectiveness of the proposed robot calibration method for parallel robots.

Dynamic formulation of a planar 3-DOF parallel manipulator with actuation redundancy

Abstract: In this paper, based on the conventional Newton-Euler approach, a simplification method is proposed to derive the dynamic formulation of a planar 3-DOF parallel manipulator with actuation redundancy. Closed-form solutions are developed for the inverse kinematics. Based on the kinematics, the Newton-Euler approach in simplification form is used to derive the inverse dynamic model of the redundant parallel manipulator. Then, the driving force optimization is performed by minimizing an objective function which is the square of the sum of four driving forces. The dynamic simulations are done for the parallel manipulator with both the redundant and non-redundant actuations. The result shows that the dynamic characteristics of the manipulator in the redundant case are better than that in the non-redundancy. The redundantly actuated parallel manipulator was incorporated into a 4-DOF hybrid machine tool which includes a feed worktable.

Bionics-An inspiration for intelligent manufacturing and engineering

Abstract: Value creation in all its facets lies at the core of intelligent manufacturing and engineering. In the last 20 years the field of manufacturing has undergone many changes and refinements. Terms such as Integrated Management Systems (IMS), Just in Time (JIT), Toyota Production System (TPS) in the context of Lean Production and 'Flow' were parts of the toolset developed by the Toyota Corporation which pushed them to the forefront of world automotive production. While benchmarking the design production systems and their associated efficiencies is very worthwhile, there are other engineering design, lean production, just in time, and production and supply chain exemplars which are worth investigating. A primary source of best-practice engineering in flexible and intelligent manufacturing is to be found in the study of 'Bionics' (Biomimicry). The intelligence in design and operational efficiency which is brought to Bionics by design in nature was recognised by Leonardo DaVinci when he wrote: ''... in her (design) nothing is lacking and nothing is superfluous'' [1]... This paper examines how design and engineering can learn and apply through the study of bionics/biomimicry, a vast pool of knowledge of design and systems engineering strategies. Such strategies and exemplars will provide benchmarks which will result in inspirational approaches in design, efficiency and sustainable engineering solutions.