Procedia manufacturing最新文献

Industrial robots are being used more and more for manufacturing applications that require accuracy beyond what can be obtained from joint measurement. While offline calibration techniques such as volumetric error compensation can be used to correct robot kinematic error, these methods are unable to compensate for robot deformations caused by changing tool loads during the manufacturing operation. This paper explores the use of a real-time robot kinematic error compensation technique where an external high-precision feedback sensor (in this case a laser tracker) directly measures the robot kinematic error and corrections are implemented during processing. A robot kinematic error model is constructed to describe the difference between the programmed trajectory of the robot’s last link and the actual trajectory based on the laser tracker measurement of the 6DoF sensor attached to the last link of the robot. The compensation scheme developed in this paper requires the synchronization of the robot encoder and laser tracker sensor measurements, which is accomplished in the Robot Operating System (ROS). The previously developed kinematic error observer is briefly discussed and a kinematic error compensation that adjusts the robot’s reference path in real time is created in this paper. A discussion of the system (i.e., Yaskawa/Motoman MH180 industrial robot and Automated Precision Inc. laser tracker) hardware components and the software architecture utilized in the experiments conducted in this paper are provided. Initial experimental studies are conducted to determine delays in the feedback measurement, which was 30 ms, explore the effects of controller gain on the system performance, and characterize the noise in the feedback measurement. The controller had an overdamped response with a settling time of 8.758 s. Subsequent experiments investigated the effect of robot velocity on tracking and the ability of the controller to reject a constant force disturbance. It was found that the kinematic error increased linearly as the robot velocity increased, and the controller was able to reject a constant force disturbance of 45 lb within the designed settling time.

An explosion in the volume of Industrial Internet of Things (IIoT) research has failed to fully resonate with industry. Better communication of requirements to researchers will help avoid wasteful IIoT research using attractively priced but functionally dead-end hardware. A robust and systematic evaluation of popular low-cost IIoT devices based on fitment for purpose will increase industry adoption of new technology built on that hardware. Specific hardware examples that have already been adopted by industry are presented. Based on that evaluation, general traits to consider when selecting IIoT hardware for research and development purposes are outlined; we propose that projects built with these criteria in mind are far more likely to be applied by industry and further developed.

Directed Energy Deposition (DED) processes allow additive manufacturing and repair of metallic components with generatively-designed complex geometries, and excellent compositional control. However, its applicability and adoption have been limited when compared to powder bed fusion (PBF) because several issues and anomalies innate to the process are yet to be suitably understood and resolved. This work catalogues and delineates these anomalies in the DED process along with their causes and solutions, based on a state-of-the-art literature review. This work also serves to enumerate and associate the underlying causes to the detrimental effects which manifest as undesirable part/process outcomes. These DED-specific anomalies are categorized under groups related to the part, process, material, productivity, safety, repair, and composition; this paper will specifically report on the first group - part quality and defects, which is further sub-classified into geometrical, morphological and microstructural anomalies. Altogether, this primer acts as a guide to best prepare for and mitigate the problems that are encountered in DED, and also to lay the groundwork to inspire novel solutions to further advance DED into mainstream manufacturing.

A human-driven procedure able to identify cross-sectorial and cross-regional circular economy value-chains was co-created by 17 European regions within the H2020 SCREEN project. This paper explains how such a procedure has been translated into an automated service able to be inserted in a digital platform overcoming current information asymmetry among value-chain stakeholders, currently under development within the H2020 DigiPrime project. The open innovation approach adopted for the translation enables local companies to directly insert their data and to be directly notified about circular business opportunities. The advantage of the automated identification mechanism consists in avoiding the support of a circular economy expert for the first identification of value chains and synergies matching, thus leaving the regional officer more freedom; the lower accuracy of the automated mechanisms will be compensated by the large number of data that will be available once the DigiPrime platform will be fully operating.

Production tends to become decentralized to provide more flexibility and robustness. Hence planning and control is also shifting to decentralized solutions to cope with a changing product mix. We present an intensive simulation study of different types of production and analyze the influence of product structure, product complexity and multiple use of materials. We show for which type of production self-organizing planning and control performs best. For this, we give a brief introduction to our self-organizing production and outline how we generate a wide variety of production master data that are freely customizable in terms of their statistical parameters, like product complexity, multiple use of materials and degree of organization, to reflect different types of productions. The results of our simulation study show the advantages of our self-organizing production.

In the steel industry, there are some parameters that are difficult to measure online due to technical difficulties. In these scenarios, soft-sensors, which are online tools that aim forecasting of certain variables, play an indispensable role for quality control. In this investigation, different soft sensors are developed to address the problem of predicting the slag quantity and composition in an electric arc furnace process. The results provide evidence that the models perform better for simulated data than for real data. They also reveal higher accuracy in predicting the composition of the slag than the measured quantity of the slag.

Recent years, manufacturing aims on increasing flexibility while maintaining productivity for satisfying emerging market needs for higher product customization. Human Robot Collaboration (HRC) is able to bring about this balance by combining the benefits of manual assembly and robotic automation. When introducing a hybrid concept, safety and human acceptance are of vital importance for achieving implementation. Fenceless coexistence may lead to discomfort of operators especially in cases where close Human Robot Interaction (HRI) occurs. This work aims at designing and implementing a natural Human-System and System-Human interaction framework that enables seamless interaction between operators and their “robot colleagues”. This natural interaction will strengthen hybrid implementation through increased: a) operator’s and system’s awareness, b) operator’s trust to the system, and through the decrease of: a) human errors and b) safety incidents. The overall architecture of the proposed system makes it scalable, flexible, and applicable in different collaborative scenarios by enabling the connectivity of multiple interfaces with customizable environments according to operator’s needs. The performance of the system is evaluated on a scenario originating from the automotive industry proving that an intuitive interaction framework can increase acceptance and performance of both robots and operators.

Circular Economy (CE) is an emerging paradigm aiming at establishing a new sustainable development path by decoupling economic growth and resource consumption. The main limitation in current production models is that manufacturing and de-and remanufacturing operations are carried out independently and without sharing information and economic benefits. This can be overcome by rethinking the current European industrial system into new collaborative and sustainable value networks. New business cases can be generated also for individual Small and Medium Enterprises (SME) by taking advantage of the connection with the Industry 4.0 opportunities and by building on cross-sectorial markets and the potential of digital solutions. The operational services (DOS) presented in this paper are the key part of a novel digital platform called DigiPrime, whose aim is to provide SME with a federated service infrastructure that help overcome the current information asymmetry and explore the possibility to access and transit towards a CE cross-sectorial value-chain. The manuscript presents a selection of DOS considered fundamental for an SME in such a process, through a relevant exploitation use-case scenario. The combination of such DOS has been validated within the project for the case of an electric car repair-shop network and the preliminary outcome of their integration process is also outlined.

Flexible robotics will be a major enabling technology for the application of robot-based automation in other than traditionally suitable automotive or electronics production with high volumes. Increased demand for flexibility due to individualized production typical for most SMEs require an increased level of flexibility – also for robots that should be able to learn as well as provide an increased level of autonomy due to improved skills and extended reasoning capabilities. This publication tries to find out if novel ANN methodology that is able to process 3D surface data is applicable to generalize process knowledge in a one shot learning by demonstration situation in order to be able to execute tasks on similar but geometrically unequal objects in future settings. The methodology generalizes not on symbolic or trajectory level but on surface geometry level and was applied to a simple geometric object on lab scale. The algorithms introduced are applicable to more complex objects with practical relevance.

Concurrent with autonomous robots, teleoperation gains importance in industrial applications. This includes remote intervention scenarios and human-robot-cooperation during complex or harmful tasks operated from a distance. For an efficient teleoperation, an interface achieving a good user experience and relieving workload of the operators is decisive. Whereas virtual reality allows a spatial integration, environment reconstruction is often based on point clouds in combination with pre-known object models. Challenges exist in spatial representation, through bandwidth limitations and sensor interferences. Furthermore, the potential of knowledge augmentation for workload improvement is often not fully utilized. To overcome these challenges, we present a hybrid environment reconstruction method, which is adaptive regarding the integration of different data sources as well as their derived semantic information levels. For evaluation, a user study is performed. Thereby known procedures to measure usability, immersion, user experience and operator workload serve for evaluation and comparison.