Robotic Manufacturing Systems Research Articles

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.

Robotic manufacturing systems: A survey on technologies to improve the cognitive level in HRI

Human-robot interaction (HRI) plays an increasingly important role in modern manufacturing processes, as it greatly improves manufacturing efficiency, flexibility, and intelligence. However, some problems related to the cognitive level during the human interaction process still exist, which may hinder the manufacturing accuracy and efficiency, and even present a potential threat to humans who work in the same workspace. Therefore, this study conducts an in-depth survey on current non-exhaustive cognitive technologies such as sensing, motion planning, artificial intelligence, and human-inspired technologies, which are the key aspects for improving the cognitive level of robotic manufacturing systems in HRI. In addition, considering the limitations of current cognitive technologies, the authors present a multidisciplinary cognitive framework to improve the cognitive level of robotic manufacturing systems for intelligent HRI.

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.

Diagnosability of Event Patterns in Safe Labeled Time Petri Nets: A Model-Checking Approach

Checking the diagnosability of a timed discrete-event system usually consists in determining whether a single fault event can always be identified with certainty after a finite amount of time. The aim of this article is to extend this type of analysis to more complex behaviors, called event patterns, and to propose an effective method to check diagnosability with the use of model-checking techniques. To do so, we propose to convert the pattern diagnosability problem into checking a linear-time property over a specific time Petri net. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the problem of improving the monitoring and the supervision of systems, such as automated and robotized manufacturing systems. Based on a model of the system, this article proposes a method to assert with certainty whether the available set of sensors will always provide enough information to ensure that a complex and unexpected behavior has not happened in the system. The proposed method uses a publicly available model-checking tool to perform this analysis.

Heterogeneous requirement gathering for generative design of robotic manufacturing systems

The heterogeneous requirements might lead to time-consuming and iterative design process. This paper intends to discuss the possibility of applying the generative design principle on the design of robotic manufacturing systems. According to the proposed design approach, designers need no longer design a specific robotic manufacturing system based on the clear specification of design requirements; instead, designers can propose the system family from the perspective of domain engineering, and the final architecture of the required robotic manufacturing system can be generated from the system family automatically with the continuous improvement of the heterogeneous requirement gathering result.

Multi-Robot based Control System

One of the most important challenge in Robotic Flexible Manufacturing Systems (RFMS) is how to develop a Multi-Robot based control system in which the robot is able to take intelligent decision to a changing environment. The problematic is how to ensure the flexibility with the proposed multi-robot based control system based on triggering strategies. The flexibility of the whole system is expanded by the capacity of the flexible robots to effectively ensure tasks assigned to it. Through this paper, three contributions can be presented: (i) the RFMS based Control Architecture by presenting in details the main components and methods, (ii) the planning model, and (iii) the different levels of flexibility in RFMS.

Scheduling Robotic Cellular Manufacturing Systems With Timed Petri Net, A* Search, and Admissible Heuristic Function

System scheduling is a decision-making process that plays an important role in improving the performance of robotic cellular manufacturing (RCM) systems. Timed Petri nets (PNs) are a formalism suitable for graphically and concisely modeling such systems and obtaining their reachable state graphs. Within their reachability graphs, timed PNs’ evolution and intelligent search algorithms can be combined to find an efficient operation sequence from an initial state to a goal one for the underlying systems of the nets. To schedule RCM systems, this work proposes an A* search with a new heuristic function based on timed PNs. When compared with related approaches, the proposed one can deal with token remaining time, weighted arcs, and multiple resource copies commonly seen in the PN models of RCM systems. The admissibility of the proposed heuristic function is proved. Finally, experimental results are given to show the effectiveness and efficiency of the proposed method and heuristic function. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Robotic cellular manufacturing (RCM) systems are among the most common and complicated discrete-event dynamic systems, which provide a great number of choices of resources and processing routes to allow high system productivity. Timed Petri nets (PNs) and intelligent search algorithms on their reachability graphs are ideal tools to handle the RCM scheduling problem. This work proposes an A* search method based on the evolutions of timed PNs to optimally schedule RCM systems. The proposed method can deal with token remaining time, weighted arcs, and multiple resource copies often encountered in the PN models of RCM systems.

Reachability Tree-Based Optimization Algorithm for Cyclic Scheduling of Timed Petri Nets

Timed Petri nets (TPNs) have been widely used for modeling discrete-event systems of diverse manufacturing and service industries. In this article, we introduce a reachability tree-based optimization algorithm to optimize cyclic schedules of TPNs. In particular, we focus on a special class of cyclic schedules that are referred to as one-cyclic schedules, i.e., the algorithm efficiently finds the optimal one-cyclic transition firing schedule of a TPN. The proposed scheduling method can be robustly applied and extended to a number of different scheduling models since the methodology is not bounded to a specific domain. To enhance the computational performance, we establish a set of transition ordering constraints that can reduce the tree size during the search procedure. We evaluate the computational efficiency of the suggested algorithm by examining robotized manufacturing systems where one-cyclic schedules are popularly being used. It is numerically shown that the proposed algorithm is computationally more efficient than the previously studied Petri net-based optimization methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Resource scheduling is one of the most important managerial issues in diverse industrial systems. An optimal scheduling method for a certain industrial system is often locally developed by utilizing domain-specific operational properties. Although such domain-dependent knowledge can contribute to enhancing the computational efficiency of an optimization method, such an approach has a weak point that the method might not be applicable to scheduling problems of different industrial fields. Our motivation is to develop an algorithm for optimizing steady-state schedules that can be robustly applied for various types of discrete-event systems. The algorithm is developed on the basis of the Petri net modeling framework as it is widely being used for describing cyclic behaviors of diverse manufacturing systems, service systems, and social systems. It is experimentally shown that the proposed algorithm is computationally efficient compared with the existing cyclic scheduling methods.

Digital twin-based industrial cloud robotics: Framework, control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.

Hybrid calibration and detection approach for mobile robotic manufacturing systems

PurposeThe purpose of this paper is to present a visual detection approach to predict the poses of target objects placed in arbitrary positions before completing the corresponding tasks in mobile robotic manufacturing systems.Design/methodology/approachA hybrid visual detection approach that combines monocular vision and laser ranging is proposed based on an eye-in-hand vision system. The laser displacement sensor is adopted to achieve normal alignment for an arbitrary plane and obtain depth information. The monocular camera measures the two-dimensional image information. In addition, a robot hand-eye relationship calibration method is presented in this paper.FindingsFirst, a hybrid visual detection approach for mobile robotic manufacturing systems is proposed. This detection approach is based on an eye-in-hand vision system consisting of one monocular camera and three laser displacement sensors and it can achieve normal alignment for an arbitrary plane and spatial positioning of the workpiece. Second, based on this vision system, a robot hand-eye relationship calibration method is presented and it was successfully applied to a mobile robotic manufacturing system designed by the authors’ team. As a result, the relationship between the workpiece coordinate system and the end-effector coordinate system could be established accurately.Practical implicationsThis approach can quickly and accurately establish the relationship between the coordinate system of the workpiece and that of the end-effector. The normal alignment accuracy of the hand-eye vision system was less than 0.5° and the spatial positioning accuracy could reach 0.5 mm.Originality/valueThis approach can achieve normal alignment for arbitrary planes and spatial positioning of the workpiece and it can quickly establish the pose relationship between the workpiece and end-effector coordinate systems. Moreover, the proposed approach can significantly improve the work efficiency, flexibility and intelligence of mobile robotic manufacturing systems.

Robotic additive manufacturing system for dynamic build orientations

PurposeThis paper aims to present a multi-axis additive robot manufacturing system (ARMS) and demonstrate its beneficial capabilities.Design/methodology/approachARMS was constructed around two robot arms and a fused filament fabrication (FFF) extruder. Quantitative experiments are conducted to investigate the effect of printing at different orientations with respect to gravity, the effect of dynamically changing build orientation with respect to the build tray when printing overhanging features, the effect of printing curved parts using curved, conformal layers. These capabilities are combined to print an integrated demonstrator showing potential practical benefits of the system.FindingsOrientation with respect to gravity has no effect on print quality; dynamically changing build orientation allows overhangs up to 90° to be cleanly printed without support structures; printing an arch with conformal layers significantly increases its strength compared to conventional printing.Research limitations/implicationsThe challenge of automatic slicing algorithms has not been addressed for multi-axis printing. It is shown that ARMS could eventually enable printing of fully-functional prototypes with embedded components.Originality/valueThis work is the first to prove that the surface roughness of an FFF part is independent of print orientation with respect to gravity. The use of two arms creates a novel system with more degrees of freedom than existing multi-axis printers, enabling studies on printing orientation relationships and printing around inserts. It also adds to the emerging body of multi-axis literature by verifying that curved layers improve the strength of an arch which is steeply curved and printed with the nozzle remaining normal to the curvature.

Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.

SME-oriented flexible design approach for robotic manufacturing systems

Continuously growing pressures due to the shorter development lead-time and the uncertainty of increasingly complex market require a high flexibility from production companies and their manufacturing systems. Comparing with large enterprises, small and medium-sized enterprises (SMEs) which lack concentrated market power and powerful original equipment manufacturers (OEMs) have difficulty and risk to develop flexible and customised robotic manufacturing systems. Nowadays, although various design methods for robotic manufacturing systems have been made to fix this problem, however, there still exist two main challenges: flexibility in design and high degree of customisation.To deal with the existing challenges, this paper proposes an SME-oriented design approach based on a configuration design paradigm. This method can offer decision support to designers to form flexible architecture for robotic manufacturing systems and, at the same time, give more interactive configuration freedom to customers so as to achieve a high productivity and flexibility for customisation but with less risk and cost of product development. To validate the proposed method, two design cases of robotic manufacturing systems are presented for demonstration.

Effect of depositing torch angle on the first layer of wire arc additive manufacture using cold metal transfer (CMT)

Purpose This paper aims to mainly report the impact of torch angle on the dynamic behavior of the weld pool which is recorded and monitored in real time with the aid of a high-speed camera system. The influence of depositing torch angle on the fluctuation behavior of weld pool and the quality of weld formation are compared and analyzed. Design/methodology/approach The FANUC controlled robotic manufacturing system comprised a Fronius cold metal transfer (CMT) Advanced 4000R power source, FANUC robot, water cooling system, wire feeding system and a gas shielding system. An infrared laser was used to illuminate the weld pool for high-speed imaging at 1,000 frames per second with CR600X2 high-speed camera. The high-speed camera was set up a 35 ° angle with the deposition direction to investigate the weld pool flow patterns derived from high-speed video and the effect of torch angles on the first layer of wire additive manufacture-CMT. Findings The experimental results demonstrated that different torch angles significantly influence on the deposited morphology, porosity formation rate and weld pool flow. Originality/value With regard to the first layer of wire arc additive manufacture of aluminum alloys, the change of torch angle is critical. It is clear that different torch angles significantly influence on the weld morphology, porosity formation and weld pool flow. Furthermore, under different torch angles, the deposited beads will produce different defects. To get well deposited beads, 0-10° torch could be made away from the vertical position of the deposition direction, in which the formation of deposited beads were well and less porosity and other defects.

Survey on Design Approaches for Robotic Manufacturing Systems in SMEs

Continuously growing pressures from competitive market require manufacturing enterprises to produce more quickly and cost-effectively. However, most of small and medium-sized enterprises (SMEs) still adopt handwork manufacturing process due to the diversity of products and their small batch sizes. Robotic flexible manufacturing systems can be considered as an effective support to aid SMEs in solving the aforementioned conflict between the product diversity and production efficiency. Nowadays, although various design methods for robotic manufacturing systems have been made, however, there still exists challenges to implement flexible robotic manufacturing systems in small and medium-sized enterprises because of the lack concentrated market power and powerful original equipment manufacturers (OEMs) in SMEs.The paper firstly analyses the challenges relating to the implementation of robotic flexible manufacturing systems in SMEs. Then, considering the proposed challenges, the authors review current design approaches for flexible manufacturing systems. Finally, the paper evaluates each design approaches according to the challenges proposed previously and points out the future research directions.

Automated Food Manufacturing System and Robotic Flexible Food Manufacturing System - Innovate UK/EPSRC

By developing food manufacturing robotic technologies, the Automated Robotic Food Manufacturing System and Robotic Flexible Food Manufacturing System projects work to meet both commercial and societal needs.

Scheduling Cluster Tools in Semiconductor Manufacturing: Recent Advances and Challenges

Cluster tools are automated robotic manufacturing systems containing multiple computer-controlled process modules. They have been increasingly used for wafer fabrication. This paper reviews the modeling and scheduling methods for cluster tools with both nonrevisiting and revisiting processes. For nonrevisiting processes, we focus on the modeling and scheduling problems of cluster tools with different constraints. Then, their solution methods are reviewed and compared. For revisiting processes, this paper first discusses the scheduling problem of some general manufacturing systems with revisiting. Then, the modeling and scheduling methodologies used to solve the scheduling problems of cluster tools with revisiting processes are reviewed. Future research directions and conclusions are finally discussed. Note to Practitioners —Semiconductor manufacturing systems are among the most advanced and complicated manufacturing systems. Their key equipment is highly automated robot-based cluster tools. With wafer residency time constraints, wafer revisiting, activity time variation, chamber cleaning requirements, and failure-prone process modules (PMs), it is very challenging to schedule and control them. This paper surveys their modeling and scheduling methods. Scheduling them requires one to schedule their robot tasks and processing activities simultaneously. Owing to wafer residency time constraints and the lack of buffers among PMs, it is difficult to conduct their optimal scheduling. This paper presents a thorough review of the state-of-the-art research results about modeling and optimal scheduling of clusters tools and indicates the future research directions.

Feature-based five-axis path planning method for robotic additive manufacturing

Additive manufacturing has been developed for decades and attracts significant research interests in recent years. Usually, the stereolithography tessellation language format is employed in additive manufacturing to represent the geometric data. However, people gradually realize the inevitable drawbacks of the stereolithography tessellation language file format, such as redundancy, inaccuracy, missing of feature definitions, and lack of integrity. In addition, it is almost impossible to apply the simple polygonal facet representation to the five-axis manufacturing strategy. Hence, there are quite few researches and applications on the five-axis additive manufacturing, in spite of its common applications in the subtractive machining. This article proposes a feature-based five-axis additive manufacturing methodology to enhance and extend the additive manufacturing method. The additive manufacturing features are defined and categorized into two5D_AM_feature and freeform_AM_feature. A feature extraction method is proposed that can automatically recognize the additive manufacturing features from the input model. Specially for the freeform_AM_feature, a five-axis path planning method is proposed and split into three stages: (1) offset the reference surface, (2) spatially slice the freeform layers, and (3) generate the toolpaths for each freeform layer. Real additive manufacturing five-axis toolpaths can be obtained using the proposed algorithm that performs as a secondary developed plug-in in the CATIA® environment. A robotic additive manufacturing system is constructed for the implementation of the five-axis additive manufacturing tasks, which are generated by the proposed algorithms and post-processed after simulation and off-line programming. Some examples are printed to validate the feasibility and efficiency of the proposed method.

Cross-Layer Optimization Model Toward Service-Oriented Robotic Manufacturing Systems

Service-oriented robotic manufacturing system (SORMS) is an integrated system, in which the industrial robots (IRs) operate within a service-oriented manufacturing model, and can be virtualized and servitized as services, so as to provide on-demand, agile, configurable, and sustainable manufacturing capability services to users in workshop environment. Manufacturing capability of such systems can be divided into three layers, including manufacturing cell layer, production process layer, and workshop layer. However, currently most of the existing works carried out the optimization on each layer individually. Manufacturing cells are the component parts of a production process, and there are close relationships between them and can affect the operation and performance for each other; therefore, it is essential to jointly consider the manufacturing capability service optimization on both layers. In this context, a cross-layer optimization model is proposed to conquer the existing limitation and provide a comprehensive performance assurance to SORMSs. The proposed model has different decision-making mechanisms on each layer, and the communications and interaction between the two layers can further coordinate the optimizations. A case study based on robotic assembly is implemented to demonstrate the availability and effectiveness of the proposed model.