Background, Systematic Review, Challenges and Outlook

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe

Enabling Technologies, Frameworks and Applications

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Agent-based manufacturing — review and expert evaluation

The advent of smart manufacturing and the exposure to a new generation of technological enablers have revolutionized the way manufacturing process is carried out. Cyber-Physical Production Systems (CPPS) are introduced as main actors of this manufacturing shift. They are characterized for having high levels of communication, integration and computational capabilities that led them to a certain level of autonomy. Despite the high expectations and vision of CPPS, it still remains an exploratory topic. Multi-Agent Systems (MAS) have been widely used by software engineers to solve traditional computing problems, e.g., banking transactions. Because of their high levels of distribution and autonomous capabilities, MAS have been considered by the research community as a good solution to design and implement CPPS. This work first introduces a collection of requirements and characteristics of smart manufacturing. A comprehensive review of various research applications is presented to understand the current state of the art and the application of agent technology in manufacturing. Considering the smart manufacturing requirements and current research application, a SWOT analysis was formulated which identifies pros and cons of the implementation of agents in industry. The SWOT analysis was further validated by an industrial expert evaluation and the main findings and discussion of the results are presented

Integration of cutting-edge interoperability approaches in cyber-physical production systems and industry 4.0

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.publishersversionpublishe

Big Data Life Cycle in Shop-floor – Trends and Challenges

Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components - Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented

Tactile sensing with gesture-controlled collaborative robot

Sensors and human machine interfaces for collaborative robotics will allow smooth interaction in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user's hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath