Showing papers in "Robotics and Computer-integrated Manufacturing in 2018"
TL;DR: The results indicate a high fragmentation among hardware, software and AR solutions which lead to a high complexity for selecting and developing AR systems.
Abstract: Augmented Reality (AR) technologies for supporting maintenance operations have been an academic research topic for around 50 years now. In the last decade, major progresses have been made and the AR technology is getting closer to being implemented in industry. In this paper, the advantages and disadvantages of AR have been explored and quantified in terms of Key Performance Indicators (KPI) for industrial maintenance. Unfortunately, some technical issues still prevent AR from being suitable for industrial applications. This paper aims to show, through the results of a systematic literature review, the current state of the art of AR in maintenance and the most relevant technical limitations. The analysis included filtering from a large number of publications to 30 primary studies published between 1997 and 2017. The results indicate a high fragmentation among hardware, software and AR solutions which lead to a high complexity for selecting and developing AR systems. The results of the study show the areas where AR technology still lacks maturity. Future research directions are also proposed encompassing hardware, tracking and user-AR interaction in industrial maintenance is proposed.
479 citations
TL;DR: Results show that the proposed methodology can bring more advantages to CMfg than the security and scalability, as well as the qualitative and quantitative methods are utilized.
Abstract: New emerging manufacturing paradigms such as cloud manufacturing, IoT enabled manufacturing and service-oriented manufacturing, have brought many advantages to the manufacturing industry and metamorphosis the industrial IT infrastructure. However, all existing paradigms still suffer from the main problem related to centralized industrial network and third part trust operation. In a nutshell, centralized networking has had issues with flexibility, efficiency, availability, and security. Therefore, the main aim of this paper is to present a distributed peer to peer network architecture that improves the security and scalability of the CMfg. The proposed architecture was developed based on blockchain technology, this facilitated the development of a distributed peer to peer network with high security, scalability and a well-structured cloud system. The proposed architecture which was named as the “BCmfg” is made up of five layers namely; resource layer, perception layer, manufacturing layer, infrastructure layer and application layer. In this paper, the concept of its architecture, secure data sharing, and typical characteristic are discussed and investigated as well as the key technologies required for the implementation of this proposed architecture is explained based on demonstrative case study. The proposed architecture is explained based on a case study which contains five service providers and 15 end users with considering 32 OnCloud services. For evaluation purpose, the qualitative and quantitative methods are utilized and the results show that the proposed methodology can bring more advantages to CMfg than the security and scalability.
221 citations
TL;DR: In this paper, the effect of processing parameters on the dimensional accuracy and mechanical properties of cellular lattice structures fabricated by additive manufacturing, also known as 3D printing, was investigated using regression method and analysis of variance (ANOVA).
Abstract: This paper investigates the effect of processing parameters on the dimensional accuracy and mechanical properties of cellular lattice structures fabricated by additive manufacturing, also known as 3D printing. The samples are fabricated by selective laser melting (SLM) using novel titanium-tantalum alloy. The titanium-tantalum alloy has the potential to replace commercially pure titanium and Ti6Al4V as biomedical material. In this study, the unit cell used is specially designed to carry out the analysis using regression method and analysis of variance (ANOVA). Due to the effect of the SLM process parameters, the elastic constant of the cellular lattice structures ranged from 1.36 ± 0.11 to 6.82 ± 0.15 GPa using the same unit cell design. The elastic constant range, while showing the versatility of titanium-tantalum as biomedical material, is rather wide despite using the same lattice structure designed. This shows that there is a need to carefully control the processing parameters during the lattice structures fabrication so as to obtain the desired mechanical properties. Based on the statistical analysis, it is found that the dimensional accuracy and mechanical properties such as elastic constant and yield strength of the cellular lattice structures are most sensitive to laser power as compared to other parameters such as laser scanning speed and powder layer thickness.
220 citations
TL;DR: In this article, the authors present an in situ monitoring method that integrates the acquisition of infrared images with a data mining approach for feature extraction and a statistical process monitoring technique to design a data-driven and automated alarm rule.
Abstract: Despite continuous technological improvements in metal additive manufacturing (AM) systems, process stability is still affected by several possible sources of defects especially in the presence of challenging materials. Thus, both the research community and the major AM system developers have focused an increasing attention on in situ sensing and monitoring tools in the last years. However, there is still a lack of statistical methods to automatically detect the onset of a defect and signal an alarm during the part's layer-wise production. This study contributes to this framework with two levels of novelty. First, it presents an in situ monitoring method that integrates the acquisition of infrared images with a data mining approach for feature extraction and a statistical process monitoring technique to design a data-driven and automated alarm rule. Second, the method is aimed at monitoring powder bed fusion processes for difficult-to-process materials like zinc and its alloys, which impose several challenges to the process stability and quality because of their low melting and boiling points. To this aim, the proposed approach analyzes the byproducts generated by the interaction between the energy source and the material. In particular, it detects unstable behaviors by analyzing the salient properties of the process plume to detect unstable melting conditions. This case study entails an SLM process on zinc powder, where different sets of process parameters were tested leading either to in-control or out-of-control quality conditions. A comparison analysis highlights the effectiveness of plume-based stability monitoring.
156 citations
TL;DR: In this article, a force-controlled end-effector for automated polishing processes is presented, which is integrated into a macro-mini robot polishing cell to reduce the inertial effects that may result in undesired vibrations.
Abstract: In this paper, the novel design of a force-controlled end-effector for automated polishing processes is presented The proposed end-effector is to be integrated into a macro-mini robot polishing cell The macro robot (in this study, it is a six-axis industrial robot) is used to position the mini robot (the proposed end-effector) according to the workpiece profile while the mini robot controls the polishing force Th end-effector has a polishing head that can be extended and retracted by a linear hollow voice coil actuator to provide tool compliance The main advantage of the proposed design is that it allows this motion without extending or retracting the polishing motor nor spindle, which reduces the inertial effects that may results in undesired vibrations By integrating a force sensor, the polishing force is measured and fed back to the controller to regulate it according to the polishing pre-planned requirements The effectiveness of the proposed device to track a certain desired force with step changes under different feed rates has been examined through polishing experiments The results demonstrate the effectiveness of the presented device to reduce the vibration and achieve remarkable force tracking
110 citations
TL;DR: In this article, a vibration sensor was used for measuring the vibrations of a bar mount during extrusion of polylactic acid, acrylonitrile butadiene styrene, and SemiFlex filaments via Direct and Bowden types of fused filament fabrication extruders.
Abstract: 3D printing and particularly fused filament fabrication is widely used for prototyping and fabricating low-cost customized parts. However, current fused filament fabrication 3D printers have limited nozzle condition monitoring techniques to minimize nozzle clogging errors. Nozzle clogging is one of the most significant process errors in current fused filament fabrication 3D printers, and it affects the quality of the prototyped parts in terms of geometry tolerance, surface roughness, and mechanical properties. This paper proposes a nozzle condition monitoring technique in fused filament fabrication 3D printing using a vibration sensor, which is briefly described as follows. First, a bar mount that supports the liquefier in fused filament fabrication extruder was modeled as a beam excited by a system of process forces. The boundary conditions were identified, and the applied forces were analyzed for Direct and Bowden types of fused filament fabrication extruders. Second, a new 3D printer with a fixed extruder and a moving platform was designed and built for conducting nozzle condition monitoring experiments. Third, nozzle clogging was simulated by reducing the nozzle extrusion temperature, which caused partial solidification of the filament around inner walls of the nozzle. Fourth, sets of experiments were performed by measuring the vibrations of a bar mount during extrusion of polylactic acid, acrylonitrile butadiene styrene, and SemiFlex filaments via Direct and Bowden types of fused filament fabrication extruders. Findings of the current study show that nozzle clogging in fused filament fabrication 3D printers can be monitored using an accelerometer sensor by measuring extruder’s bar mount vibrations. The proposed technique can be used efficiently for monitoring nozzle clogging in fused filament fabrication 3D printers as it is based on the fundamental process modeling.
108 citations
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TL;DR: In this paper, the authors measured the warpage on block-shaped parts in ABS thermoplastic resin as a function of some geometric variables related to the process: the size of the part in the three directions, and the thickness of deposited layers.
Abstract: Analysis of thermal distortions on parts built by extrusion-based additive manufacturing.Measurement of geometric deviations on block-shaped specimens in ABS thermoplastic.Main original finding: maximum warpage occurs at intermediate values of part thickness.Suggested explanations: shrinkage on multiple layers, occurrence of plastic deformation. Similarly to most additive manufacturing processes, Fused Deposition Modeling involves the processing of material by thermal cycles which can create distortions (warpage) in the built parts. The paper aims to characterize this defect on block-shaped parts in ABS thermoplastic resin as a function of some geometric variables related to the process: the size of the part in the three directions, and the thickness of deposited layers. For this purpose, the geometric deviations on parts manufactured with different combinations of the above variables have been measured and statistically analyzed in order to identify the influence factors and to estimate their individual and interaction effects on warpage. The results have given one main further insight compared to previous studies, namely the occurrence of a maximum distortion at intermediate values of part height. The attempt to explain it has suggested two additional hypotheses for the physical explanation of distortions: the extension of thermal stresses to multiple layers due to heat conduction from the last deposited layer, and the occurrence of bending stresses beyond the yield point of the material. These effects have been modeled by analytic equations in order to verify whether they can help improve the accuracy of warpage estimation.
99 citations
TL;DR: An integrated decision model is presented that coordinates predictive maintenance decisions based on prognostics information with a single-machine scheduling decisions so that the total expected cost is minimized.
Abstract: Maintenance planning and production scheduling are two activities that are inter-dependent but most often performed independently in manufacturing. The maintenance planning affects both available production time and failure probability. However, in previous research, the maintenance planning only considers preventive maintenance and may result in maintenance shortage or overage. And the deterioration and health status of machines from prognostics are often ignored. The paper presents an integrated decision model that coordinates predictive maintenance decisions based on prognostics information with a single-machine scheduling decisions so that the total expected cost is minimized. In the integrated model, the health status and dummy age subjected to machine degradation is considered. Finally, a case study is used to demonstrate the value of the proposed methods. And the performance of the integrated solution is compared with solutions obtained from solving the predictive maintenance planning and production scheduling problems independently. The results prove its efficiency.
99 citations
TL;DR: By analyzing hundreds of specifications it appears that, indeed, the range of C-factors of the grippers built by one company can be often consistently different from these of competitors, which seems at odd with the requirement of modern robotic systems.
Abstract: With the recent introduction of ambitious industrial strategies such as Horizon 2020 and Industry 4.0, a massive focus has been placed on the development of an efficient robotic workforce. Amongst all the operations robotic systems can take care of, handling remains a preferred choice due to a combination of factors including its often repetitive nature and low skill requirement. The associated demand for grasping tools has led to an ever increasing market for manipulation end-of-arm tooling from which a handful of industry giants have emerged. Based on data publicly accessible from the catalogs of several well-known companies, this paper aims at presenting a review on the characteristics of pneumatic, parallel, two-finger, industrial grippers. Included in the specifications under scrutiny in this paper are: stroke, force, weight, as well as a performance index referred to as the C-factor. This last index is a combination of three of the aforementioned characteristics and has been proposed in the literature as a measure of the efficiency that a gripper is capable of reaching. As will be shown, by analyzing hundreds of specifications it appears that, indeed, the range of C-factors of the grippers built by one company can be often consistently different from these of competitors. Furthermore, an important bias for certain typical specifications can be observed in most of the grippers which seems at odd with the requirement of modern robotic systems. This latter remark will open up a closing discussion proposed in the last part of this paper on the future evolution of grippers based on emerging new products.
98 citations
TL;DR: A generic decision methodology is introduced that will not only provide a set of compromised AM materials, processes and machines but will also act as a guideline for designers to achieve a strong foothold in the AM industry by providing practical solutions containing design oriented and feasible material-machine combinations from a current database of 38 renowned AM vendors.
Abstract: Market dynamics of today are constantly evolving in the presence of emerging technologies such as Additive Manufacturing (AM). Drivers such as mass customization strategies, high part-complexity needs, shorter product development cycles, a large pool of materials to choose from, abundant manufacturing processes, diverse streams of applications (e.g. aerospace, motor vehicles, and health care) and high cost incurred due to manufacturability of the part have made it essential to choose the right compromise of materials, manufacturing processes and associated machines in early stages of design considering the Design for Additive Manufacturing guidelines. There exists a complex relationship between AM products and their process data. However, the literature to-date shows very less studies targeting this integration. As several criteria, material attributes and process functionality requirements are involved for decision making in the industries, this paper introduces a generic decision methodology, based on multi-criteria decision-making tools, that will not only provide a set of compromised AM materials, processes and machines but will also act as a guideline for designers to achieve a strong foothold in the AM industry by providing practical solutions containing design oriented and feasible material-machine combinations from a current database of 38 renowned AM vendors in the world. An industrial case study, related to aerospace, has also been tested in detail via the proposed methodology.
95 citations
TL;DR: In this article, an efficient technique for producing the thin-walled metallic structures, including objects with undercut features, is presented. But this technique requires additional degrees of freedom or higher order kinematics to the work piece and/or the deposition head by suitably aligning the overhanging feature in-line to the deposition direction.
Abstract: Gas Metal Arc Welding (GMAW) based weld-deposition process is one of the deposition-based Additive Manufacturing (AM) processes with the ability to produce fully dense complex functional metallic objects. Due to its high deposition rates, high material and power efficiency, lower investment costs, simpler setup and work environment requirements it is slowly becoming a viable metallic AM method. Amongst various geometrical features that can be realized in weld-deposition based AM, the thin-walled features (i.e., features with one single deposition pass) are the toughest as the process has to overcome the bead-over-bead complexity. Based on geometric modelling and experimentation, this paper presents an efficient technique for producing the thin-walled metallic structures, including objects with undercut features. This is possible by adding extra degrees of freedom or by using higher order kinematics to the work piece and/or to the deposition head by suitably aligning the overhanging feature in-line to the deposition direction. An in-house MATLAB code was developed to slice the CAD model and generate the tool path for inclined deposition of a given layer of a thin-walled model. A geometrical model proposed to predict the layer thickness of a given layer during such bead-on-bead deposition showed good correlation with experimental data. Some illustrative complex thin-walled components successfully fabricated using this model have also been presented.
TL;DR: It is shown that RFID-SMS can improve the inter-enterprise production transparency under mass individualization and real-time monitoring and dynamic dispatching of inter-Enterprise production and transportation tasks.
Abstract: In today's manufacturing environment, manufacturers need to handle unprecedented and diverse customer requirements swiftly. An efficient way is to collaborate with various stakeholders such as micro-/small-/medium-sized enterprises, factories, workshops, logistics service providers, public warehouse providers, and even individuals, forming a social media-based community. The vital factor to enable this way is the efficient real-time monitoring and dynamic dispatching of inter-enterprise production and transportation tasks. This work deals with the social manufacturing trend and proposes an radio frequency identification-enabled social manufacturing system (RFID-SMS) to realize the real-time monitoring and dispatching of inter-enterprise production and transportation tasks. Firstly, RFID devices are systematically deployed in each enterprise's job-shops and transportation vehicles to collect real-time production and transportation data. Then, these data is processed to monitor task progress and states, which is helpful to manage the inter-enterprise production processes. To deal with the unexpected disturbances, dynamic dispatching decisions for inter-enterprise production and transportation tasks are made by the manufacturer to win high flexibility and efficiency. Furthermore, a prototype is developed and a case is implemented in a printing machinery company, and the feasibility of the proposed system and models are evaluated by the practical industrial data from the company. It shows that RFID-SMS can improve the inter-enterprise production transparency under mass individualization.
TL;DR: A task-level programming software tool allowing robotic novices to program industrial tasks on a collaborative robot called Skill Based System (SBS), founded on the concept of robot skills, which are parameterizable and task-related actions of the robot.
Abstract: During the past decades, the increasing need for more flexible and agile manufacturing equipment has spawned a growing interest in collaborative robots. Contrary to traditional industrial robots, collaborative robots are intended for operating alongside the production personnel in dynamic or semi-structured human environments. To cope with the environment and workflow of humans, new programming and control methods are needed compared to those of traditional industrial robots. This paper presents a task-level programming software tool allowing robotic novices to program industrial tasks on a collaborative robot. The tool called Skill Based System (SBS) is founded on the concept of robot skills, which are parameterizable and task-related actions of the robot. Task programming is conducted by first sequencing skills followed by an online parameterization performed using kinesthetic teaching. Through several user studies, SBS is found to enable robotic novices to program industrial tasks. SBS has further been deployed and tested in two manufacturing settings demonstrating its applicability in real industrial scenarios.
TL;DR: A comprehensive dynamic model of an industrial robot in both dynamic mode and quasi-static mode is obtained to calculate the external force produced by human operator in pHRI and enables sensorless pHRI for industrial robots even in the environment with ambient vibration.
Abstract: As industrial robots are applied in manufacturing industry on a large-scale and human intelligence is regarded as an important part in manufacturing, physical human−robot interaction (pHRI) which integrates the strength and accuracy of robot with human operator's ability of task cognition has drawn the attention of both academia and industry. However, an industrial robot without extra force/torque sensor for interacting force monitoring cannot be used directly in pHRI, and research on pHRI of industrial robots remains a challenge. In this research, a comprehensive dynamic model of an industrial robot in both dynamic mode and quasi-static mode is obtained to calculate the external force produced by human operator in pHRI and enables sensorless pHRI for industrial robots even in the environment with ambient vibration. Particularly, the dynamics in the process of mode switching which has not been investigated by researchers is studied and compensated by an empirical but effective method. Admittance control is used to transfer the detected force into reference position and velocity of the robot. RBF (Radial Basis Function) network is used to update the damping parameter online in order to reduce the contact force change and the contact force which makes pHRI more natural and easier. The stability of the controller is also discussed. The proposed methods of external force detection and adaptive admittance control show satisfactory behaviour in the experiments.
TL;DR: This paper focuses on the optimization of the kinematic and hydrodynamic model of the amphibious spherical robot, so as to improve the control accuracy and stability of the robot.
Abstract: This paper describes an improved three-dimensional (3D)-printed, low-cost, multi-functional, high-maneuverability, high-concealment, turtle-inspired mobile amphibious spherical robot for environmental monitoring and data collection. The major challenge in developing such a robot lies in its limited physical size and compact structure that allows for only one type of propulsion system to be used both on land and in water. This paper focuses on the optimization of the kinematic and hydrodynamic model of the amphibious spherical robot, so as to improve the control accuracy and stability of the robot. In order to optimize some kinematic and dynamic modeling parameters of the robot, such as the drag coefficient of robot, the angular velocity and swing angle of each joint, a solid model of the 3D-printed robot was built by SolidWorks. Our simulation results and theoretical calculations confirmed the validity of the virtual model and facilitated identification of key parameters in the design. The correctness of the modeling was demonstrated by the stability of consecutive crawling and underwater movements, providing a basis for driving and controlling methods for this amphibious robot, as well as guidance for the robot's gait trajectory. Combining the robot's crawling mechanism with related simulation results, an optimized prototype of the 3D-printed amphibious spherical robot was constructed. A series of crawling experiments on a common floor were performed with the improved robot prototype, which was also done using the previous robot. The results were evaluated by a novel optical positioning system, NDI Polaris. Moreover, several experiments were carried on land crawling and underwater swimming to verify the performance of the improved amphibious spherical robot. Comparison of experimental and simulation results demonstrated the improved robot had better amphibious motion performance, as well as more potentiality and applicability to the real structures.
TL;DR: This work examines two types of techniques of safe collaboration that do not interrupt the flow of collaboration as far as possible, namely proactive and adaptive, which were implemented in a prototype highly interactive and immersive Virtual Environment.
Abstract: Human-Robot Interaction (HRI) has emerged in recent years as a need for common collaborative execution of manufacturing tasks. This work examines two types of techniques of safe collaboration that do not interrupt the flow of collaboration as far as possible, namely proactive and adaptive. The former are materialised using audio and visual cognitive aids, which the user receives as dynamic stimuli in real time during collaboration, and are aimed at information enrichment of the latter. Adaptive techniques investigated refer to the robot; according to the first one of them the robot decelerates when a forthcoming contact with the user is traced, whilst according to the second one the robot retracts and moves to the final destination via a modified, safe trajectory, so as to avoid the human. The effectiveness as well as the activation criteria of the above techniques are investigated in order to avoid possible pointless or premature activation. Such investigation was implemented in a prototype highly interactive and immersive Virtual Environment (VE), in the framework of H-R collaborative hand lay-up process of carbon fabric in an industrial workcell. User tests were conducted, in which both subjective metrics of user satisfaction and performance metrics of the collaboration (task completion duration, robot mean velocity, number of detected human-robot collisions etc.) After statistical processing, results do verify the effectiveness of safe collaboration techniques as well as their acceptability by the user, showing that collaboration performance is affected to a different extent.
TL;DR: In this article, the authors investigated the potential use of a commercially available portable photogrammetry system (the MaxSHOT 3D) in industrial robot calibration and compared it with a laser tracker (the FARO laser tracker) by calibrating an industrial robot, with each device in turn, then comparing the obtained robot position accuracy after calibration.
Abstract: This work investigates the potential use of a commercially-available portable photogrammetry system (the MaxSHOT 3D) in industrial robot calibration To demonstrate the effectiveness of this system, we take the approach of comparing the device with a laser tracker (the FARO laser tracker) by calibrating an industrial robot, with each device in turn, then comparing the obtained robot position accuracy after calibration As the use of a portable photogrammetry system in robot calibration is uncommon, this paper presents how to proceed It will cover the theory of robot calibration: the robot's forward and inverse kinematics, the elasto-geometrical model of the robot, the generation and ultimate selection of robot configurations to be measured, and the parameter identification Furthermore, an experimental comparison of the laser tracker and the MaxSHOT 3D is described The obtained results show that the FARO laser tracker ION performs slightly better: The absolute positional accuracy obtained with the laser tracker is 0365 mm and 0147 mm for the maximum and the mean position errors, respectively Nevertheless, the results obtained by using the MaxSHOT 3D are almost as good as those obtained by using the laser tracker: 0469 mm and 0197 mm for the maximum and the mean position errors, respectively Performances in distance accuracy, after calibration (ie maximum errors), are respectively 0329 mm and 0352 mm, for the laser tracker and the MaxSHOT 3D However, as the validation measurements were acquired with the laser tracker, bias favors this device Thus, we may conclude that the calibration performances of the two measurement devices are very similar
TL;DR: In this paper, a geometric model between the actuated force and the contact force for an under-actuated tendon-driven robotic gripper based on the geometric analysis is constructed to obtain the transmission efficiency of the tension force when a tendon wraps a joint mandrel by the geometric relations.
Abstract: We build the mathematical model between the actuated force and the contact force for an under-actuated tendon-driven robotic gripper based on the geometric analysis.A mathematical model is constructed to obtain the transmission efficiency of the tension force when a tendon wraps a joint mandrel by the geometric relations.The geometric model of transmission characteristics determined by the tendon routes for reducing the resistance is generated.Genetic Algorithm is applied to optimizing the dimensions of the gripper and the tendon routes.The geometrically optimal approach provided by us has the characteristics of the versatility and can also be referred to optimizing most of the under-actuated robotic gripper with tendon-driven mechanisms. The design optimization of a robotic gripper is of utmost importance for achieving a stable grasp behaviour. This work focuses on analysing the optimal design of an under-actuated tendon-driven robotic gripper with two 3-phalange fingers and a geometric design optimization method is proposed to achieve a stable grasp performance. The problem has twenty-two design variables, including three phalange lengths, three phalange widths, three radii of joint mandrels, a palm width and twelve route variables for allocation of six pulleys. First, the mathematical model between the active and contact forces is expressed in relation to the geometric dimensions of the robotic gripper. Second, the geometric model of transmission characteristics determined by the tendon routes for reducing the resistance is generated. Next, three objective functions and multiple geometric constraints are derived and integrated into two fitness models. Finally, the genetic algorithm is applied to addressing the optimization problem. Practical experiments are performed as well to validate the proposed approach. The approach is universal for optimizing any conventional under-actuated tendon-driven gripper.
TL;DR: It is demonstrated that nearly 20% of the kinematic error in this study can be attributed to complex, joint-dependent error sources, and the proposed modeling framework, constructed from measurements of 250 poses, describes 97.0%" of the measured error.
Abstract: Robot positioning accuracy is critically important in many manufacturing applications. While geometric errors such as imprecise link length and assembly misalignment dominate positioning errors in industrial robots, significant errors also arise from non-uniformities in bearing systems and strain wave gearings. These errors are characteristically more complicated than the fixed geometric errors in link lengths and assembly. Typical robot calibration methods only consider constant kinematic errors, thus, neglecting complex kinematic errors and limiting the accuracy to which robots can be calibrated. In contrast to typical calibration methods, this paper considers models containing both constant and joint-dependent kinematic errors. Constituent robot kinematic error sources are identified and kinematic error models are classified for each error source. The constituent models are generalized into a single robot kinematic error model with both constant and high-order joint-dependent error terms. Maximum likelihood estimation is utilized to identify error model parameters using measurements obtained over the measurable joint space by a laser tracker. Experiments comparing the proposed and traditional calibration methods implemented on a FANUC LR Mate 200i robot are presented and analyzed. While the traditional constant kinematic error model describes 79.4% of the measured error, the proposed modeling framework, constructed from measurements of 250 poses, describes 97.0% of the measured error. The results demonstrate that nearly 20% of the kinematic error in this study can be attributed to complex, joint-dependent error sources.
TL;DR: The proposed method can perform safe and timely dynamic avoidance for redundant manipulators in human-robot interaction and is implemented in Robot Operating System (ROS) using C++.
Abstract: In order to avoid dynamic obstacle timely during manufacturing tasks performed by manipulators, a novel method based on distance calculation and discrete detection is proposed. The nearest distances between the links of a manipulator and the convex hull of an arbitrarily-shaped dynamic obstacle obtained from Kinect-V2 camera in real-time are calculated by Gilbert–Johnson–Keerthi algorithm, and the minimum one is defined as the closest distance between the manipulator and the obstacle. When the closest distance is less than a safe value, whether the dynamic obstacle is located in the global path of the manipulator is determined by improved discrete collision detection, which can adjust detection step-size adaptively for accuracy and efficiency. If the obstacle will collide with the manipulator, set a local goal and re-plan a local path for the manipulator. The proposed method is implemented in Robot Operating System (ROS) using C++. The experiments indicate that the proposed method can perform safe and timely dynamic avoidance for redundant manipulators in human-robot interaction.
TL;DR: In this article, a multi-body dynamic model of an ABB IRB6660 industrial robot is elaborated using beam elements which can easily be integrated into the machining trajectory planning.
Abstract: Productivity in robotic machining processes can be limited by the low rigidity of the overall structure and vibration instability (chatter). The robot's dynamic behavior, due to changes in its posture along a machining trajectory, varies within its workspace. Chatter in robotic machining therefore depends not only on the cutting parameters but also on the robot configuration. Moreover, the robot can follow a machining trajectory in the operational space with an infinite number of possible trajectories in its configuration space. It is due to the redundancies offered by its kinematic chain. This paper deals with the optimization of robotic machining stability by controlling the robot configurations and the functional redundancies of its kinematic chain. It is shown that stability in robotic machining along a given trajectory can be ensured through the optimization of the robot configurations, without changing the cutting parameters, in order to maintain productivity performance. A multi-body dynamic model of an ABB IRB6660 industrial robot is elaborated using beam elements which can easily be integrated into the machining trajectory planning. The beam element geometry, elasticity and damping parameters are adjusted on the basis of experimental modal identifications. The present study is focused on the dynamic model-based predictions of stable and unstable zones along a robotic machining trajectory with one degree of functional redundancy. Experimental machining tests confirm the stability predictions from the numerical model.
TL;DR: In this article, an Elman-based Layer Recurrent Neural Network (LRNN) was used to predict the accuracy of components produced by WEMD by using an ELMAN-based layer recurrent neural network, and the results reveal that the average deviation between network predictions and actual components is below 6μm.
Abstract: For many industrial sectors, high-added value components are related to high accuracy manufacturing technologies. Wire Electrical Discharge Machining (WEDM) is an advanced non-conventional machining method commonly used in the production of precision components in extremely hard materials. The precision of the process depends largely on the deformation of the wire tool. Whilst theoretical models allow a scientific understanding of the causes of a lack of accuracy, they still lack the level of precision required to predict actual deviations in industrial products. In this work, we propose a way to predict the accuracy of components produced by WEMD by using an Elman-based Layer Recurrent Neural Network (LRNN). The results reveal that the average deviation between network predictions and actual components is below 6μm, which implies extremely good performance of the net. In a further step, an algorithm was proposed for designing wire paths of variable radius, so that the deviations in the machined parts can be corrected via software. By combining the predictions of the developed LRNN with the Simulated Annealing (SA) optimization technique, wire paths of variable radius can be designed, so that radial deviations due to wire deformations can be minimized. The results show that the new proposal is very efficient in those situations in which wire deformation is greatest. In other words, when the part radius is low and part height is large, the stiffness of the wire is reduced and the error of the part sharply increases. In these cases, the average deviation was reduced by as much as 80%, and the Coefficient of Variation (CV) was decreased by 43%. The solution can be readily implemented on any existing WEDM machine.
TL;DR: The mechanical and control design of a magnetic tracked mobile robot designed to move on vertical steel ship hulls and to be able to carry 100 kg payload, including its own weight is presented.
Abstract: In this paper we present the mechanical and control design of a magnetic tracked mobile robot. The robot is designed to move on vertical steel ship hulls and to be able to carry 100 kg payload, including its own weight. The mechanical components are presented and the sizing of the magnetic tracks is detailed. All computation is embedded in order to reduce time delays between processes and to keep the robot functional even in case of signal loss with the ground station. The main sensor of the robot is a 2D laser scanner, that gives information on the hull surface and is used for several tasks. We focus on the welding task and expose the control algorithm that allows the robot to follow a straight line for the welding process.
TL;DR: Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions.
Abstract: Currently, a key industrial challenge in friction processes is the prediction of surface roughness and loss of mass under different machining processes, such as Electro-Discharge Machining (EDM), and turning and grinding processes. Under industrial conditions, only the sliding distance is easily evaluated in friction processes, while the acquisition of other variables usually implies expensive costs for production centres, such as the integration of sensors in functioning machine-tools. Besides, appropriate datasets are usually very small, because the testing of different friction conditions is also expensive. These two restrictions, small datasets and very few inputs, make it very difficult to use Artificial Intelligence (AI) techniques to model the industrial problem. So, the use of the isotropy level of the surface structure is proposed, as another input that is easily evaluated prior to the friction process. In this example, the friction processes of a cubic sample of 102Cr6 (40 HRC) steel and a further element made of X210Cr12 (60 HRC) steel are considered. Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions. The results obtained with real datasets showed that RBFs and MLPs provided the most accurate models for loss of mass and surface roughness prediction, respectively. MLPs have slightly higher surface prediction accuracy than Random Forest, although MLP models are very sensitive to the tuning of their parameters (a small mismatch between the learning rate and the momentum in the MLP will drastically reduce the accuracy of the model). In contrast, Random Forest has no parameter to be tuned and its prediction is almost as good as MLPs for surface roughness, so Random Forest will be more suitable for industrial use where no expert in AI model tuning is available. Moreover, the inclusion of the isotropy level in the dataset, especially as a numeric attribute, greatly improved the accuracy of the models, in some cases, by up to 52% for MLPs, and by a smaller proportion of 16% in the Random Forest models in terms of Root Mean Square Error. Finally, Random Forest ensembles only trained with low and very high isotropy level experimental datasets generated reliable models for medium levels of isotropy, thereby offering a solution to reduce the size of training datasets.
TL;DR: In this article, an innovative disassembly approach was proposed to identify the profitability of recycling such electronic components. But the authors did not consider the management of electronic components that has been rarely considered in the scientific literature.
Abstract: The rapid growth of market share of Electrical Vehicles (EVs) and their increasing amount of electric and electronic components have introduced difficult challenges for future recycling of such vehicles. End of Life Vehicles (ELVs), together with Waste Electric and Electronic Equipment (WEEE), are renowned as an important source of secondary raw materials. In addition, a significant proportion of the hidden value at the End-of-Life (EoL) of the EVs is embedded in the light fractions containing complex material mixtures, i.e. the management of electronic components that has been rarely considered in the scientific literature. The purpose of this paper is to fill this gap through the use of an innovative disassembly approach to identify the profitability of recycling such electronic components. The novel approach, based on the utilisation of a robotic system, disassembles and extracts Strategically Important Materials (SIMs) from EV components, thereby improving the concentration of these materials prior to final recycling and refining processes. This paper presents the challenges in the robotic disassembly of Electrical and Electronic (E&E) components. A case study has also been included to demonstrate that an average 95% of the materials and their associated recovery value could be achieved.
TL;DR: A double-curved shell model of hypoid gear finite element is used to perform the numerical loaded tooth contact analysis (NLTCA), as well as to establish the data-driven relationships between machine settings and physical performance evaluations.
Abstract: A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances is proposed to improve competitiveness of hypoid gear product development facing with economic globalization. Firstly, to deal with the vagueness or impreciseness of the voice of customer (VOC), the fuzzy quality function deployment (fuzzy-QFD) using fuzzy weighted average method in the fuzzy expected value operator is introduced into hypoid gear manufacturing. It can convert them into the critical to qualities (CTQs), and the technical geometric and physical performance requirements. And then, the multi-objective optimization (MOO) modification of machine settings is proposed to establish a basic data-driven model for collaborative system. Different with the conventional modification only considering geometric performance, it provides an improved modification also considering the physical performances. Finally, a double-curved shell model of hypoid gear finite element is used to perform the numerical loaded tooth contact analysis (NLTCA), as well as to establish the data-driven relationships between machine settings and physical performance evaluations. Immediately, whole development is divided into three sub-problems: i) optimal operations of the noise factors by measurement and numerical control (NC) compensation, ii) identification of the prescribed ease-off topography by multi-objective optimization using iterative reference point approach and iii) modification considering geometric performance by a trust region algorithm with step strategy. The numerical instance in practical applications is given to verify the proposed methodology.
TL;DR: Experimental results showed that the local thresholding approach has managed to fulfill the research objective in detecting, identifying and locating the butt welding joint position in the three different scenarios.
Abstract: Manual detection and identification of butt weld joints through welding image in real-time by human observation is subjective in nature, requires experience and can be biased at times. Furthermore, since that most of the welding robots application are programmed by teach and play means that they need to be reprogrammed each time they deal with new task. This is time consuming, plus welding parameters also need to be refined for every new program. Hence, this research aims to tackle the aforementioned issues by suggesting an alternative method that can automatically recognize and locate the butt welding position at starting, middle, auxiliary and end point under three scenarios which are; (1) straight, (2) saw tooth and (3) curve joint. This is done without any prior knowledge of shapes involved. A new approach known as local thresholding is proposed in the segmentation process which consists of image pre-processing, noise reduction and edge region points generation for butt welding joint identification. Region points for butt welding seam path are selected by shape selection generated from contour points according to the three scenarios. Each point is located by 2D coordinates that is usually used by robot controller for path planning. Experimental results showed that the local thresholding approach has managed to fulfill the research objective in detecting, identifying and locating the butt welding joint position in the three different scenarios. When compared with other methods such as background subtraction, local thresholding narrowly loses out in terms of less mismatch error produced. However, it gives the best results in the detection of the butt welding joint edges. Besides having the advantage of the ability to perform identification without prior knowledge from an image, local thresholding also showed that it can work just fine even in the presence of imperfections such as scratches on the surface of mild steel.
TL;DR: With increasing use of fiber reinforced polymer composites follows a natural pursuit for more rational and effective manufacturing, Robotic pick-and-place systems can be used to automate handling.
Abstract: With increasing use of fiber reinforced polymer composites follows a natural pursuit for more rational and effective manufacturing. Robotic pick-and-place systems can be used to automate handling o ...
TL;DR: An improved method is proposed in this paper to calibrate the tool (grinding machine) frame and workpiece (aero-engine blade) frame by holding the ruby probe as the main calibration tool to enhance the accuracy of robotic calibration system.
Abstract: Calibration in the robotic belt grinding of complex blades is considered as one of the key bottlenecks of measurement accuracy. To enhance the accuracy of robotic calibration system, an improved method is proposed in this paper to calibrate the tool (grinding machine) frame and workpiece (aero-engine blade) frame by holding the ruby probe as the main calibration tool. Firstly, the sphere-to-sphere method replacing the traditional point-to-point method is put forward to calibrate the flexible and fixed probe frame. Secondly, the calibrated flexible and fixed probe frame is employed to precisely seek the origin point of tool frame and then to calibrate it accurately. Thirdly, both the rough calibration (manual calibration) and fine calibration (auto calibration) are adopted to calibrate the workpiece frame, the resulting translation and rotation errors are controlled at small values to improve the calibration accuracy. Finally, a typical case on robotic belt grinding of aero-engine blade is conducted to validate the calibration results of the robotic belt grinding system (RBGS).
TL;DR: A novel approach is proposed for time-optimal trajectory planning of a hyper-redundant manipulator which is requested to move from an initial configuration to a final configuration in 3D workspaces using a Genetic Algorithm with multiple population.
Abstract: A novel approach is proposed for time-optimal trajectory planning of a hyper-redundant manipulator which is requested to move from an initial configuration to a final configuration in 3D workspaces. The 3D workspace is cluttered with static objects which have known geometry and position. The proposed approach generates a trajectory for the manipulator's end-effector considering simultaneously the kinematical constraints of the manipulator (specifically velocity and acceleration) and the presence of obstacles. The method solves an optimization problem to find the minimum time trajectory to perform the requested tasks. The optimization problem is solved by using a Genetic Algorithm with multiple population. The efficiency of the developed method is investigated and discussed through characteristic simulated experiments concerning a variety of operating environments.