Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
SES 2.2: Smart Factories and Industrial IoT
Time:
Tuesday, 27/Jun/2017:
1:50pm - 3:10pm

Session Chair: Michele Ciavotta
Location: Aula N (first floor)

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Presentations

232. Internet-of-Things paradigm in food supply chains control and management

Riccardo Accorsi1, Marco Bortolini1, Giulia Baruffaldi2, Francesco Pilati1, Emilio Ferrari1

1University of Bologna, Italy; 2Department of Management and Engineering, Padova University, Stradella San Nicola, 3 – 36100 Vicenza (Italy)

Starting from the definition of the Internet-of-Things (IoT) paradigm, the aim of this paper is to discuss goals and expected strategies for the design and building of a IoT architecture aiding the planning, management and control of the Food Supply Chain (FSC) operations. A comprehensive architecture of the entities, the physical-objects, the physical and informative flows, the stages and the processes to be sensed, tracked, controlled and interconnected is given to illustrate the interdependencies between the observed supply chain and the exogenous environment in terms of physical and information inputs, outputs and mutual impacts. A simulation gaming tool embedding and implementing the IoT paradigm for the FSC management is also proposed and illustrated to showcase the potential benefits and opportunities for more direct integration of the physical food ecosystems into virtual computer-aided control systems.


219. A Wireless Intelligent Network for Industrial Control

Mohammad Gholami, Mohammed Salem Taboun, Robert Brennan

University of Calgary, Canada

This paper reports on an ad hoc wireless sensor network architecture for industrial sensing and control applications. This approach is tested using a large-scale (400-676 node) agent-based simulation of a factory environment that is subject to noise and blockage. The basic problem tackled by the distributed system is mobile node tracking. To support this work, we introduce two classes of metrics: (1) a set of tracking perfor-mance metrics, and (2) a set of network architecture efficiency metrics. The results of our experiments show that the proposed distributed system adapts readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.


43. Enabling Connectivity of Cyber Physical Production Systems: A Conceptual Framework

Rafael A. Rojas C.1, Erwin Rauch1, Renato Vidoni1, Dominik T. Matt1,2

1Free University of Bolzano, Italy; 2Fraunhofer Italia Research s.c.a.r.l., Innovation Engineering Center (IEC), via Macello 57, Bolzano, 39100 Italy

In the Fourth Industrial Revolution, the Internet of Things will allow integrating all instances of the value chain of products, enabling communication and cooperation between them. This requires a high level of integration and interoperability of a wide spectrum of communication technologies in all layers of a network system. Inside a Smart Factory, the same problem takes a more concise form, and Cyber-Physical Systems (CPS) such as humans, robots, smart machines, products and sensors, have to be synchronized one with another and with the external world to share information and trigger actions to make possible both flexibility and high level of customization, which characterizes the Industry 4.0 revolution. Moreover, all data generated at the Smart Factory should be collected, stored, and shared to further Big Data Analysis and decision making. This must combine machine-to-machine communication, machine-human interaction and the Internet. In this work, the conceptual development of such a network to implement a Smart Factory at the Mini-Factory Laboratory of the Free University of Bolzano is presented. The objective is to set up an Industrial Internet System (IIS), first by homogenizing and integrating the communication systems of the end-nodes of the Mini-Factory, i.e., sensors, robots, etc., through the necessary hardware and middle-ware, and second, by constructing a centralized backbone network where all valuable information is collected for further data analysis. This will make possible the implementation of an application layer oriented to Industrial Internet of Things, and the decentralization of decision making allowed by the cyber-physical

capabilities together with a centralized high-level optimization interface.


337. Process monitoring technology based on virtual machining

Eun-Young Heo2, Hikoan Lee2, Cheol-Soo Lee3, Dong-Won Kim1, Dong Yoon Lee4

1Chonbuk National University, Korea, Republic of (South Korea); 2CAMTIC, Jeonju-si, Jeollabuk-do, 54852, Korea; 3Sogang University, Seoul, 04107, Korea; 4Korea Institute of Industrial Technology, Ansan-si, Gyeonggi-do, 15588, Korea

This paper presents a process monitoring system which is integrated with virtual machining for a more accurate diagnosis of machining operation without the need for test machining. Virtual machining simulates the material removal process along the NC tool path and predicts the static and dynamic characteristics of the operation. The status data of a machine tool from the CNC and sensor signals are integrated with pre-estimated virtual machining data. The proposed monitoring system analyzes the integrated data to diagnose the machining operation in (near) real-time. The application of the monitoring system to 3 axis machining is demonstrated with experimental results.



 
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