Robotic Manufacturing Systems Research Articles

Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems

Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems

High-Throughput Additive Manufacturing of Continuous Carbon Fiber-Reinforced Plastic by Multifilament.

Additive manufacturing (or 3D printing) of continuous carbon fiber-reinforced plastics with fused deposition modeling is a burgeoning manufacturing method because of its potential as a powerful approach to produce lightweight, high strength and complex parts without the need for a mold. Nevertheless, it cannot manufacture parts rapidly due to low throughput. This paper proposes a high-throughput additive manufacturing of continuous carbon fiber-reinforced plastics by multifilament with reference to fiber tape placement. Three filaments were fed and compaction printed simultaneously by a robotic manufacturing system. The coupled thermal-mechanical model of the filament deformation during printing was developed to eliminate the initial interval between the filaments and improved mechanical properties. Furthermore, the mathematical relationship between filament deformation and printing parameters consisting of printing temperature, printing speed and roller pressure was proposed using response surface methodology with the line width as the response. The tensile tests demonstrate that the tensile properties of printed parts are positively correlated with the line width, but not infinitely improved. The maximum tensile strength and tensile modulus are 503.4 MPa and 83.11 Gpa, respectively, which are better than those obtained by traditional methods. Void fraction and scanning electron microscope images also reveal that the appropriate line width achieved by the reasonable printing parameters contributes to the high-throughput multifilament additive manufacturing of continuous carbon fiber-reinforced plastics. The comparison results indicate that the high-throughput multifilament additive manufacturing proposed in this paper can effectively improve the speed of continuous carbon fiber-reinforced plastics additive manufacturing without degrading the mechanical performance.

Multi-agent collaborative conceptual design method for robotic manufacturing systems in small- and mid-sized enterprises

Robotic manufacturing systems are essential in the post-pandemic world owing to their high level of flexibility and automation during a labor crisis. However, designing a suitable robotic manufacturing system for small- and mid-sized enterprises (SMEs) is challenging, considering both their financial burdens and the current supply chain disruptions of component suppliers. To address these challenges, this study proposes a distributed multi-agent collaborative conceptual design method involving designers and suppliers to assist SMEs in implementing robotic manufacturing systems. First, we propose a common data model that enables knowledge interaction between different agents during collaborative conceptual design. Subsequently, based on the proposed data model, the agent-based collaborative conceptual design process is developed, which enables different agents to communicate, interact, and negotiate with each other according to their experiences and knowledge. Third, an integrative algorithm based on 2-additive fuzzy measures, Choquet integral, and stochastic multi-criteria acceptability analysis is implemented to support the multi-agent decision-making process to robustly select architecture alternatives. As a case study, a real industrial design project of a robotic manufacturing system required by our industrial partner, is adopted to demonstrate the effectiveness of the proposed method.

Energy-efficient and quality-aware part placement in robotic additive manufacturing

The advancements in autonomous robots for additive manufacturing (AM) are opening new horizons in the manufacturing industry, especially in aerospace and construction applications. The use of multiple robots and collaborative work in AM has rapidly gained attention in the industry and research community. Addressing the process planning challenges for single-robotic AM is foundational in addressing more advanced challenges at the collaborative multi-robotic level for AM. Among these challenges include the part placement problem which explores the optimal positioning of the part within the robot’s reach volume. The majority of the existing part placement algorithms take into account the part accuracy and manufacturing time for decision-making, while neglecting the implications of such decisions on energy efficiency and environmental sustainability. To address this gap, this paper presents a methodology for energy-efficient, high-quality part placement (EEHQPP) in robotic additive manufacturing. An energy-quality map is formulated and established to characterize the energy and quality variations across the robot’s workspace to inform the decision-making process. Two case studies (a container and a spur gear) are considered, and the performance of the proposed approach compared to the benchmark (i.e., default part placement by the 3D printing software) are evaluated. The proposed algorithm reduces both the energy consumption and maximum deviation error of the container (6.5% and 19.4%, respectively) and spur gear (1.4% and 32.7%, respectively) geometries manufactured by the robotic additive manufacturing system.

Robot-aided manufacturing and its impact on the future of the furniture industry in Egypt

Smart robots are one of the most important outputs of the digital revolution; many of them have taken part in industrial fields, including the furniture industry, It is now the technology of the present, and it will have a significant impact on the industry in the future. Robot-aided manufacturing has now become the criterion for measuring the progress of countries, especially the major industrial countries such as the United States of America and Japan, and reports have showed that we estimate the global robotics industry in 2020 at one hundred billion dollars. In addition, that after nearly 45 years, artificial intelligence will outperform humans in performing many tasks, like the field of industries that require humans to deal directly with cutting and sawing machines, such as the furniture industry, in order to reduce the risks of accidents to which workers are exposed. Research problem: The absence of the culture of robot-aided manufacturing in the furniture industry in Egypt, especially considering the technological development that we are experiencing now, which leads us to the importance of testing the possibility of applying some robotic control systems in the furniture industry in Egypt. Research Queries: - What are the most important features of robotic manufacturing that can affect factories' decision to move towards robotic manufacturing? - What are the robotic manufacturing techniques that can be used in the furniture industry in Egypt? - Is there an impact of the application of robotic manufacturing in the furniture industry? - Is Egypt ready to implement robotic manufacturing systems in furniture factories in Egypt?

Multi-Agent Collaborative Conceptual Design Method for Robotic Manufacturing Systems in SMEs

Multi-Agent Collaborative Conceptual Design Method for Robotic Manufacturing Systems in SMEs

AUTOMATED MANUFACTURING SYSTEMS, MODELLED BY MEANS OF COLOURED PETRI NETS WITH COMPLEX COLOURS AND FUNCTIONS

The automated manufacturing systems are frequent within the modern manufacturing systems. This paper shows how the coloured Petri nets are used to assess a robotic manufacturing system, considering several versions of it. The system is made of three machines, and the workpieces are moved through the system by means of a conveyer-type transferring device. Three typed of workpieces are manufactured in this system. the modelling and simulation are made by means of coloured Petri nets. Within the first version, each workpiece type is manufactured on one machine. The loading and evacuation of the workpieces is made by means of an industrial robot for each machine. Within another version, a gantry-type robot serves all the three machines. Also, this paper analyses the case when the manufacturing of one workpiece is made on two machines. These versions were modelled by means of complex coloured Petri nets. The simulations were meant to point out any bottlenecks that may occur within the operation of the system. Using the time factor allowed to assess the system versions also for efficiency. Finally, the paper confirms that modelling and simulation with coloured Petri nets are very useful to assess the performances of the robot manufacturing systems.

An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios.

Elastomer-Based Visuotactile Sensor for Normality of Robotic Manufacturing Systems.

Modern aircrafts require the assembly of thousands of components with high accuracy and reliability. The normality of drilled holes is a critical geometrical tolerance that is required to be achieved in order to realize an efficient assembly process. Failure to achieve the required tolerance leads to structures prone to fatigue problems and assembly errors. Elastomer-based tactile sensors have been used to support robots in acquiring useful physical interaction information with the environments. However, current tactile sensors have not yet been developed to support robotic machining in achieving the tight tolerances of aerospace structures. In this paper, a novel elastomer-based tactile sensor was developed for cobot machining. Three commercial silicon-based elastomer materials were characterised using mechanical testing in order to select a material with the best deformability. A Finite element model was developed to simulate the deformation of the tactile sensor upon interacting with surfaces with different normalities. Additive manufacturing was employed to fabricate the tactile sensor mould, which was chemically etched to improve the surface quality. The tactile sensor was obtained by directly casting and curing the optimum elastomer material onto the additively manufactured mould. A machine learning approach was used to train the simulated and experimental data obtained from the sensor. The capability of the developed vision tactile sensor was evaluated using real-world experiments with various inclination angles, and achieved a mean perpendicularity tolerance of 0.34°. The developed sensor opens a new perspective on low-cost precision cobot machining.

Online Coordinated Motion Control of a Redundant Robotic Wire Arc Additive Manufacturing System

This paper considers the trajectory tracking control for wire arc additive manufacturing (WAAM). Some aspects of the WAAM process, such as the angles between welding torch direction, deposition surface normal and gravity are very important to ensure the quality of the manufactured parts. A task-priority based kinematic control scheme is proposed to control a manipulator and a positioning table coordinately. The manipulator and the positioning table are considered as a single robotic chain, and the primary task is defined to make the welding torch follow a desired trajectory defined in the positioning table deposition frame, while the secondary task is to align the welding torch in a desired direction defined in the inertial frame, e.g., gravity direction. A complete Lyapunov stability analysis is performed considering unmodeled dynamics in the kinematic control loop. The effectiveness of the proposed method is shown experimentally on a WAAM robotic system composed of a 6-axis industrial manipulator, a 2-axis positioning table and a Cold Metal Transfer (CMT) power source.

Symbolic Scheduling of Robotic Cellular Manufacturing Systems With Timed Petri Nets

To reduce the computational burden in the scheduling of robotic cellular manufacturing (RCM) systems based on Petri nets’ (PNs) reachability graphs, existing methods mainly focus on the relaxation of a graph search algorithm at the cost of schedule optimality. Different from that, this article presents a method that accelerates the search process by using symbolic representations and manipulations. The proposed method uses binary decision diagrams (BDDs) to functionally represent and evolve discrete and place-timed PNs of RCM systems and then schedule them with a symbolic A* search to find an optimal solution with minimal makespan. It uses compact BDD structures to represent sets of states, instead of individual states, and then performs efficient Boolean manipulations to evolve the net and search for a system schedule. A heuristic function and its Boolean implementations for the symbolic A* search are developed. The admissibility of the proposed heuristic function is proven, which guarantees the optimality of the obtained schedule. The computational time is thus reduced without sacrificing the schedule optimality. Finally, experiments are carried out to show the effectiveness and efficiency of the presented method.

Robotic Manufacturing System for Unattended Machining and Inspection of Graphite Bipolar Flow Field Plates for Proton Exchange Membrane Fuel Cells

A single robot-based manufacturing system for unattended machining and inspection of graphite bipolar flow field plates for proton exchange membrane fuel cells is designed and integrated for demonstration and validation. Unlike most robotic manufacturing systems where an industrial robot is used for tending an automated tool such as a computer numerical control machine, in the present system the industrial robot performs all manufacturing operations, including machining the flow fields on both sides of the plates, changing the tools, handling the plates, vacuuming the plates and the workholding device of graphite dust, flipping the plates, air blowing them and performing machine vision inspection for quality control. The toolpath for robotic machining the flow fields and the manifolds are generated offline using Roboguide simulation software. The manufacturing system uses an integrated machine vision inspection process as a diagnostic tool for in-line checking the presence of machined features and in-line verification of feature dimensions. Besides the considerably lower capital cost compared to other automated manufacturing systems resulted from the elimination of the automated machine tool, the proposed robotic cell has the advantage of better managing the abrasive graphite dust resulted in the manufacturing process. The limitations of the proposed robotic cell are assessed and recommendations for further development are considered. The manufacturing system is demonstrated as part of a larger endeavour of bringing to readiness advanced manufacturing technologies for renewable energy devices and responds the high priority needs identified by the U.S. Department of Energy for fuel cells manufacturing research and development.

Defects Reduction in the Robotic Layup Process

Robotic layup is a novel process developed to face the increasing demand for automation, flexibility, repeatability, and achieving high-quality composite materials in relevant industry fields, such as aerospace and automotive. This process is based on laying prepreg tissues on a mold using the action of a robotic arm equipped with a specific end-effector, which is usually composed of rollers and punches. The main drawback of the robotic layup is the occurrence of wrinkles and defects while moving, placing, and processing the pre-impregnated tissues. This issue is particularly evident in the processing of complex-shaped surfaces. The robotic arm cannot replicate exactly the movement of a human operator, following the geometry of the surfaces with a proper angulation like a human wrist. Moreover, operator hands can be set in a different shape just changing the configuration of the fingers, adapting themself in different curvatures. The demand of the industry to improve automation requires that the robotic manufacturing systems replicate as much as possible the gesture of the operator. In this study, an end-effector capable to replicate the human movements during the hand layup of fibrous tissues or fabrics has been conceptualized.

The Role of a Behavioural Model for the Virtual Commissioning of Robotic Manufacturing Systems

Abstract This paper focuses on virtual commissioning of a robotic manufacturing system, its theoretical background, and an analysis of its importance in the design of automated and robotic systems. Virtual commissioning allows the PLC control program to be tested and debugged on a created digital model of the machine, called a digital twin of the machine. Just this solution can simulate and optimize the automated system design itself without the need to have real hardware (PLC, automation elements, or the whole machine/equipment). Virtual commissioning offers an effective alternative to reduce the risks and effort of real commissioning of the machine. This makes it possible to reduce the time to market and increase flexibility, efficiency, and quality. Virtual commissioning in a single-control system is a simple solution; we can either use the Software-in-the-Loop method with emulated PLC control, simulated behavioural model, and co-simulation model or Hardware-in-the-Loop method with real controller. The problem appears when we have various types of controller, including an industrial robot controller. The paper deals with the theoretical background and partial outlines of solutions in the use of different types of the control system, if interconnections or emulators for different control systems are available. The recommended solution is to use a separate solution for the 3D simulation of the robotic system and an independent solution for the behavioural model, which will be tested in the future.

Modelling and simulation of robotic production systems

Modern manufacturing systems consist of a sequence of robotic systems, such as robotic cells, which are widely used in the industry. As they become commonplace and play important roles in modern industrial production, efficient modelling and use of robotic cells become a necessity. Robotization brings many measurable benefits. It enables effective utilization of both human resources and the possessed machine park and thus contributing to the improvement of product quality and efficiency. Industrial robots not only increase the growth of productivity, by replacing man in monotonous works, requiring enormous, not achievable for human precision but above all ensure the accuracy and repeatability of the product quality. Appropriate design and modelling of robotic manufacturing systems are crucial in the efficient integration of work cell components and proper maintenance of robotic production systems. Therefore, new solutions for programming environments in industrial applications are sought which not only allow reprogramming of industrial robots but also enable the virtual design of new production lines, which in turn allows the faster introduction of higher quality products. This article presents the modelling and programming of robotic work cells in the Siemens Process Simulate environment.

Approach of resource expenditure estimation toward mechanization in the manufacturing of cell-based products.

Recent developments for the manufacturing of cell-based products have focused on the advancement of products to clinical trials or commercialization, with awareness of the importance of cost-based effectiveness in cell manufacturing. The mechanization of cell-processing operations is advantageous for the reproducibility and stability of product quality and is thought to reduce the cost-of-goods through the life cycle of the product in a scale-up system; however, few cases of the implementation exist. This study developed an estimation method for the resource expenditure of cell-processing operations in the manufacturing of cell-based products. To estimate resource expenditures, we evaluated the manufacturing processes by operations involving entering into the surrounding area of cell processing zone, materials loading, cell-processing operation, cleaning, and leaving from the surrounding area. The cell-processing operation is applicable to manual or robotic cell manufacturing system in a biosafety cabinet or an isolator system. In cases of low annual batch numbers of manufacturing (batch number <33), the resource expenditure of cell-processing operations in a robotic operation system installed in the isolator system is estimated to be higher compared with a manual operation system in the isolator system due to additional initial costs for design and fabrication of the robotic operation system containing robot arms. With increasing numbers of annual batches, the resource expenditure decreases for robotic operating system, leading to an advantageous juncture where the resource expenditure of a robotic operation system is equivalent to that of a manually operated system, whereby the labor cost for cell-processing operations rises. In addition, the expertise of operations required for cell manufacturing is suggested to foster potential risks associated with the operation skills or turnover of operators, and the cost of education and training increases due to the necessity of persistent human resource development. Collectively, revealing the approach for installation of robotic operation system in cell manufacturing.

Knowledge-based engineering approach for defining robotic manufacturing system architectures

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time. A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users’ unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution.

Knowledge-based program generation approach for robotic manufacturing systems

In recent decades, robotic manufacturing systems have been considered as effective solutions for providing more productive manufacturing processes, but with less cost and risk. However, the programming for robotic manufacturing systems is a time-consuming task, and hinders the implementation of robotic manufacturing systems in today's industry. This paper proposes a knowledge-based program-generation approach for robotic manufacturing systems. The proposed approach provides effective support for the standardization of the rules and knowledge related to manufacturing programs that have proven successful in previous manufacturing cases; this can not only increase the programming efficiency, but can also improve the manufacturing stability and production quality. First, an ontological knowledge model is developed to provide an explicit semantic description of the relevant concepts for the robotic manufacturing system, basic instruction units for the program, and product models of the workpieces. Second, a rule-based reasoning mechanism is established to infer the implicit relationships between the basic instruction units of the manufacturing program. Finally, based on the semantic descriptions and reasoning mechanism of the proposed knowledge model, the basic instruction units of the manufacturing program are instantiated based on data extracted from the product models and integrated according to the relationships inferred by the reasoning mechanism, thereby generating the robotic manufacturing program.

A general take on a Tecnomatix Process Simulate´s Digital Twin creation and its exchange of information with the TIA Portal and PLC SIM Advanced

Digital twins are rapidly establishing themselves as a necessary step in digital change also in the design of automated and robotic manufacturing systems. The digital twin serves as an interface between physical industrial products and digital worlds. This allows you to map the continuous availability of data throughout the lifecycle from product planning and development, production and commissioning to use and recycling. An important step towards this goal is to create digital twins of the components in order to create a database usable for the design of new production systems in a virtual environment. One of the possibilities of using the digital twins is testing and virtual commissioning of production systems. Instead of the classic CAD model, the digital twin replaces a detailed digital image of individual components, including their full functionality. The Digital Twin comprises the CAD model, kinematic behavior model a most important is the logical behavior model. Our goal is to create a digital twin component of the production system, integrate a CAD model, create a kinematic model and a logical behavior model. This digital twin will have signals assigned to it for logical behavior. The result is the verification and testing of the information exchange between the virtual control system and the virtual model, using the Software-in-the-Loop method and the Tecnomatix Process Simulate software environment on the digital twin side and the Siemens TIA Portal with PLC SIM Advanced on the controller side. The result of the knowledge is that the exchange of information is functional and usable for virtual commissioning.

Hybrid offline programming method for robotic welding systems

• Current automatic programming approaches for robotic manufacturing systems have been reviewed. • A hybrid offline programming method that combines CAD-based and vision-based offline approaches is proposed. • The vision-based and CAD-based activities of the proposed programming method with the support techniques are presented. • Vision & CAD interactive activities are proposed to identify the workplace, detect the deviation, and compensated the path. Offline programming is an intuitive and automatic programming generation technique that does not use real robotic systems, thus greatly decreasing the downtime required for system programming, and resulting in enormous savings in terms of labor costs. Currently, offline programming can be generally categorized into computer-aided-design-based (CAD-based) and vision-based approaches; these two types of offline programming approaches have been widely applied in robotic welding systems. However, owing to the highly complex and diverse workpieces needed in the shipbuilding industry, neither of the aforementioned offline programming approaches can fully support the automatic generation of welding programs. In this paper, a hybrid offline programming method systematically combining CAD-based, vision-based, and vision & CAD interactive activities is proposed to overcome the limitations of current automatic program generation methods for robotic welding systems. In the vision-based activities, the positions of the workpieces are obtained by using geometrical features gathered from the workpieces’ images, whereas in the CAD-based activities, the welding tasks are assigned to different mobile components of the welding torch; then, their welding paths are planned according to the workpieces’ CAD models. The vision & CAD interactive activities enable the mapping between the point cloud of a workpiece and its CAD model, so that the deviations caused by assembly errors can be detected and path compensation data can be determined. The effectiveness of the proposed hybrid offline programming method is demonstrated by integrating it into a subassembly welding robotic system. The experimental results indicate that this method can significantly improve the efficiency, accuracy, and flexibility of the robotic welding system.