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
SES 9.4: Sustainable Manufacturing
Thursday, 29/Jun/2017:
11:20am - 1:00pm

Session Chair: Yi-Chi Wang
Location: Aula P (first floor)

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373. Simulating a Semiconductor Packaging Facility: Sustainable Strategies and Short-time Evidences

Yi-Chi Wang1, Tin-Chih Chen1, Li-Chih Wang2

1Feng Chia University, Taiwan; 2Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan

Semiconductor packaging plays a crucial role in semiconductor manufacturing because it is among the closest steps to the end customers. However, the complexity of production conditions makes controlling a semiconductor packaging facility a challenging task, and dynamic factory simulation has been considered as an effective means to fulfill this task. The large amounts of money, time, efforts, and expertise required to conduct a factory simulation study force a semiconductor packaging firm to pursue the persistent application of the factory simulation model (i.e., the sustainability of the factory simulation model). This challenge has rarely been discussed in previous studies. This study proposed several strategies to enhance the sustainability of a factory simulation model. In addition, the effectiveness of these strategies were examined by identifying short-time evidence rather than observing for a long duration to enhance efficiency. The proposed methodology was applied to the simulation of a real semiconductor packaging facility.

277. Optimization of energy efficiency of a production site: a tool for a fast data acquisition

Ivan Meo, Alessandra Papetti, Fabio Gregori, Michele Germani

Università Politecnica delle Marche, Italy

Nowadays the efficient use of energy has acquired a significant importance in different sectors, particularly in the industrial one. In the latter, many companies adopt policies aimed at sustainable manufacturing, reducing production costs to be more competitive on the market. Even the increasingly stringent regulations on environmental impact lead companies to tread a path towards energy efficiency in short terms to avoid penalties.

Hence the need for a tool that favors the audit of plant energy flows and the identification of existing criticalities in a fast and effective manner. This permits to evaluate the plant energy efficiency. Moreover, tools to monitor processes are increasing in terms of technologies. Digitalization of data is an opportunity to acquire real-time data for deep analysis and optimization. Data without a correct organization are useless for a correct plant management.

The goal of this work is to propose a structured data framework to perform fast and intuitive energy monitoring analysis. A method to effectively acquire plant data will be provided. The method starts from a classification of necessary data, such as the amount of energy or the number of hours worked, then propose an acquisition process that adapts to each energy carrier and to every production process. A resulting tool is proposed for a proper data collection. The tool collects data from different fields of the plant, arranging inputs for deeper energy analysis. The tool permits to identify in a clear manner plant limits in terms of energy use. An energy manager through the tool can propose fast solutions to overcome plant criticalities.

The tool is based on the data needed to characterize the flow of energy of various processes and services of the plant; a proper organization of data will be provided. The work will be validated exploiting the method than the proposed tool in a case study concerning a manufacturer of heat exchangers which decided to embark on a path toward energy efficiency without any supporting tool. A rapid and automatized implementation of the instrument will be the next step of the present research.

4. Smart Life Cycle Monitoring for Sustainable Maintenance and Production – an example for Selective Laser Melting machine

Eckart Uhlmann1,2, Rodrigo Pastl Pontes1, Abdelhakim Laghmouchi1, Claudio Geisert1, Eckhard Hohwieler1

1Fraunhofer Institute for Production Systems and Design Technology, Germany; 2nstitute for Machine Tools and Factory Management IWF - Technische Universität Berlin, Germany

Smart linking, evaluation and provision of information over the life cycle of a product are becoming growingly important. The use of information extracted from the combination of condition monitoring data, product data from the design and development phase, and from the product utilization phase, such as documented observations during the maintenance events will increase the availability of production machines and reduce the costs and resources caused by machine downtime. This knowledge and the correlations identified from the intelligent linking of the information over the life cycle of a machine can be provided to different stakeholders (e.g. service technician, maintenance planner, product developer, etc.) depending on their needs and requirements. This enables a sustainable maintenance, development and operation of the machines in the production environment. Moreover, the analysis of condition data and energy consumption of a production system and their linking with information from different phases of the life cycle of the machine, using intelligent approaches for data measurement for data acquisition, IT-infrastructure for data transmission, storage and provision, diagnostics and prognosis algorithms for fault detection and forecasting of the remaining useful life, will optimize the operation of the machine and increase availability and reduce costs for maintenance, repair and overhaul. The aim of this paper is to present a concept of a smart linking, evaluation and provision of the information over the life cycle of a production system to increase the performance and the efficiency for maintenance events.

370. The effect of forklift driver behavior on energy consumption and productivity

Abdulhameed Al-Shaebi1, Nourma Khader1, Husam Dauod1, Joseph Weiss2, Sang Won Yoon1

1State University of New York at Binghamton, United States of America; 2The Raymond Corporation, Greene, NY 13778, U.S.A.

This research investigates the impact of forklift driver behavior on energy consumption and productivity (i.e., the average number of pallet movements per operating hour) by conducting various statistical and regression analyses. Forklift driver behavior data are collected from actual warehouse setting to evaluate the relationships between different driving behaviors (i.e., concurrent travel and lift, travel speed, acceleration, and braking) and driver performance. The research results show that 1) drivers perform similar tasks have different driving behaviors; 2) driver productivity increases with longer durations of concurrent travel and lift; and 3) average speed is the most significant variable that affects the energy consumption.

122. Energy Usage Analysis of Carbide End Mills on AISI 1045 Steel

Oscar Velásquez Arriaza, Besmir Cuka, Jong-Young Lee, Dong-Won Kim

Chonbuk National University, Korea, Republic of (South Korea)

Machining and manufacturing processes have a strong influence on industry development and economy growth. Many important factors have been studied in order to reduce the waste in these processes, as well as to optimize them. The energy and power is a good parameter to verify the efficiency of the processes, and it can be used for process monitoring. Monitoring the state of energy being used may serve as an indicator on the manufacturing process behavior and efficiency. The main issue is to translate the energy usage into a practical indicator since the energy indicator is a sort of tough challenge to approach due to the dynamic characteristics of the machines and the diversity of associated factors interacting one another during the machining processes.

Another important issue that has a deep impact on both cost and efficiency, especially in computer assisted processes, is tool wear and tool life expectancy. Although it has been ceaselessly studied over many years, it is a complex task to solve still to these days. Thus, in this study a specific type of carbide end mill is analyzed through a potential energy approach over the conventional end milling. Energy limit for a cutting tool with specific physical and chemical features will be estimated since it can simplify the tool life estimation and the tool change timing prediction. A series of experiments are performed with carbide end mills against AISI 1045 steel, periodically measuring the tool wear and the required energy till the end of tool life.

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