The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

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Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method.

Using autonomous intelligence to build a smart shop floor

The European Proceedings of Social & Behavioural Sciences

Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

A Model for Predictive Maintenance Based on Asset Administration Shell.

Increased demand for a greater variety of products has forced many companies to rethink their strategies to offer more product variants without sacrificing production efficiency. Consequently, to satisfy this demand for customized products in shorter lead time and lower costs, production systems must be highly reactive and reconfigurable. In this context, the concept of reconfigurable manufacturing systems (RMS) emerged in the late 1990s to overcome the limitations of traditional manufacturing in rapidly and cost-efficiently respond to changing market conditions. However, the traditional development process of special-purpose machines to meet the requirements of change turned into an expensive and time-consuming task, challenging practitioners and scholars for reducing the impact of variety on the manufacturing costs. In order to aid the transition towards the reconfigurability from an engineering design perspective, this article introduces the Module-Based Machinery Design, a method to support the conceptual and system-level design of modular machine families for RMS. The contributions of this research include (i) the organization of existing methods and techniques for designing module-based product families into a coherent framework intended for developing machine families for RMS. (ii) The proposition of a design method that accomplishes the majority of RMS characteristics through the use of modularity. (iii) The introduction of the Adherence Index, a measure to indicate the level of utilization of basic, auxiliary and adaptive modules within a module-based machine variant. (iv) Finally, the analytical evidence of an RMS implementation through the design process of a family of modular floor level palletizers.

Max Birtel

Papers

Intelligent Manual Working Station through Assistive Systems

Interdisciplinary Engineering Methodology for changeable Cyber-Physical Production Systems

FutureFit: a strategy for getting a production asset to an industry 4.0 component – a human-centered approach

Requirements for a Human-Centered Condition Monitoring in Modular Production Environments

Self-description of Cyber-Physical Production Modules for a product-driven manufacturing system