Abstract
Internet of Things (IoT) in industrial settings now leads to the development of a new generation of systems designed to improve the operational efficiency of the new paradigm of smart manufacturing plants. Thereby, the current article introduces in detail the definitions, concepts, standards, and other important aspects related to smart manufacturing, cooperative robotics, and machine learning techniques. The paper highlights the opportunities presented by the new paradigm and the challenges faced in effectively implementing it in the industrial context. Especially, the focus is on the challenges associated with the architectures, communications technology, and protocols that enable the integration and deployment of machine learning algorithms to improve the execution of cooperative tasks performed daily by human operators, machines, and robots. The article also provides a systematic review of state-of-the-art research efforts for the fields aforementioned. Finally, an architecture for integrating collaborative robotics and machine learning based on six layers and four hierarchies of the RAMI 4.0 (Reference Architectural Model Industry 4.0) is presented.
Highlights
The challenges faced by industry to maintain or expand their customer base led to the development of smart manufacturing concepts, which, in turn, bring many challenges to traditional manufacturing companies
The platform design introduced overcomes the main shortcomings of applying different technologies to smart manufacturing, which are summarized as follows: 1) it meets data integration requirements by using unified OPen Communication Unified Architecture (OPC UA) information; 2) the proposed architecture allows the acquisition of data from different protocols used in many types of machines as well as legacy systems; 3) the architecture is created in layers of separate elements that can be connected, whose main advantage lies in the fact that the components can be added, substituted, or reused without affecting each other
This paper presented an overview of the definitions, concepts, standards, and other features related to smart manufacturing, cooperative robotics, and machine learning techniques used in the industrial context
Summary
The challenges faced by industry to maintain or expand their customer base led to the development of smart manufacturing concepts, which, in turn, bring many challenges to traditional manufacturing companies. A large amount of devices needs to be connected to the Internet at low costs, with restricted hardware functions and energy sources (such as small batteries), which makes latency, energy efficiency, cost, reliability, and security/privacy the most desirable features [3] Such IIoT deployment enables an astonishing number of applications not even imagined a few years ago. Concepts related to cooperative robotics and machine learning applied to smart manufacturing are clarified and the current trends of their application in manufacturing processes are explored. The focus is on the challenges associated with the need for standards, architectures, network protocols, and efficiency for data exchange, which can boost the use of collaborative robotics for production processes and machine learning techniques applied to industrial applications in the context of IIoT.
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