Procedia manufacturing最新文献

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.

This work presents a novel software tool to design, analyze and simulate parallel robots. It considers any desired constraints, requirements and load scenarios, while being able to be used by non-experts. This is comprehensively shown for one kind of the Gough/Stewart-platform kinematics class. First, the inverse kinematics is derived, which allows for the interactive exploration of the workspace, as well as other properties, prior to building the machine. In addition, the forward kinematics are implemented iteratively. The validation of the simulation is done by presenting a low cost, six degree of freedom robot for laboratory applications, being designed using this framework. Here, both the inverse and forward kinematics are used in real time to allow for precise and correct movement, detailed in-process measurements, such as process loads, along with overload protection. The approach fosters the efficient selection of existing as well as the design of custom robots of this kind.

Despite the widespread use of electronic devices and consumer products integrating flexible electronics, in industrial practice, the assembly process of film-based coverlays is still performed manually, and this, in turn, affects process accuracy in first place. Additionally, the related manual tasks can be troublesome for workers, causing stress and fatigue. As a result, such a process can represent a bottleneck in the fabrication of Flexible Printed Circuit Boards (FPCBs).

In the present paper, the architecture of a gripper is proposed as the core device of a completely automated film-coverlay assembly process. The architecture of the gripper is vacuum-based and exploits the action of several independent valves, enabling the grip configuration according to the geometry of the component. The gripper operation is shown by implementing specific solutions related to the removal of the coverlay protective film and partial coverlay thermal bonding, thus providing also a detailed description of the phases concerning a feasible automated assembly process. The results of the tests concerning the evaluation of both solutions are reported and discussed in the paper. Finally, a simplified gripper version is prototyped to perform some of the tests, and it is also used to preliminarily assess the performance of the gripping strategy, with good results.

In studying manufacturing processes it is often necessary to compare nominal or reference product shapes that are expected to be ideally achieved after applying a nominal set of process parameters and the actual product shape achieved. The latter is usually obtained by metrological equipment such as profilometers, tomographs and 3D scanners, whilst the former may also result from manufacturing process numerical simulation software. In this work, comparison of 2D contours is studied by adopting the well-established Procrustes method. The method relies on minimizing deviation of landmark points belonging to the shapes under scrutiny. Landmarks that need to be compared pairwise should be chosen in an optimal way, especially when the contours contain different numbers of points, when they differ locally etc. Optimal determination of landmarks and as a result implementation of contour comparison is performed through ant colony optimization implemented on Matlab^TM platform. Indicative results are shown where deviations discovered signify shrinkage of a die-cast clay part.