Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Dynamic Multilevel Workflow Management Concept for Industrial IoT Systems

Workflow management is implemented in manufacturing at many levels. The nature of processes variesat each level, hindering the use of a standard modeling orimplementation solution. The creation of a flexible workflow management framework that overarches the heterogeneous business process levels is challenging. Still, one of the promisesof the Industry 4.0 initiative is precisely this: to provideeasy-to-use models and solutions that enable efficient execution of enterprise targets. By addressing this challenge, this articleproposes a workflow execution model that integrates information and control flows of these levels while keeping their hierarchy. The overall model builds on the business process model andnotation (BPMN) for modeling at the enterprise level and recipemodeling based on colored Petri net (CPN) at the production level. Models produced with both alternatives are implemented and executed in a framework supported by an enterprise servicebus (ESB). Loosely coupled, late-bound system elements are connected through the arrowhead framework, which is builtupon the service-oriented architecture (SOA) concept. To proveits feasibility, this article presents the practical application ofthe model via an automotive production scenario

Special Session on Industry 4.0

No abstract available

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows