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 1.1: Data science in manufacturing
Time:
Tuesday, 27/Jun/2017:
11:20am - 1:00pm

Session Chair: Chia-Yen Lee
Location: Aula Convegni (first floor)

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Presentations

261. A conceptual framework for “Industry 3.5” to empower intelligent manufacturing in emerging countries and case studies

Chen-Fu Chien

National Tsing Hua University, Taiwan

Leading nations have reemphasized manufacturing with national competitive strategies such as Industry 4.0. The paradigm of production and manufacturing system is shifting, in which the increasing adoption of intelligent equipment and robotics, Internet of Things (IOT), and big data analytics have empowered manufacturing intelligence. Leading companies are battling for dominant positions in this newly created arena via providing novel value-proposition solutions and/or employing new technologies to enhance smart production. However, most of emerging countries may not ready for the migration of Industry 4.0. This study aims to propose a conceptual framework of “Industry 3.5” as a hybrid strategy between Industry 3.0 and to-be Industry 4.0, to address some of the needs for flexible decisions and smart production in Industry 4.0. Empirical studies in high-tech manufacturing and other industries are used for illustration. Future research directions are discussed to implement the proposed Industry 3.5 to facilitate the migration of Industry 4.0.


51. Equipment Health Monitoring in the Semiconductor Assembly Process

Zhao-Hong Dong, Bo-Kai Jang, Chia-Yen Lee

National Cheng Kung University, Taiwan

Due to the semiconductor assembly process is capital-intensive for mass production, the industry faces a major challenge of management issue─ monitoring the thousands of equipment. In general, overall equipment effectiveness (OEE) has been widely used to measure the productivity and assess the status of equipment. However, there are some other indices not identified according to the OEE, for example, the usage of consumables and spare parts. This study proposed an equipment health monitoring (EHM) framework to improve the OEE, drive the productivity, and support preventive maintenance. The EHM framework has several modules including the data preprocessing, statistical process control (SPC), analytic hierarchical process (AHP), and finally provides an equipment health index (EHI) of the equipment. According to different types of the status variable identifications (SVIDs), several SPC control charts are developed to monitor each SVID individually and the weight of each SVID is extracted to build the EHI via AHP method. An empirical study of the Taiwan leading semiconductor assembly manufacturer is conducted to validate the proposed models. The result shows that the proposed framework supports the real-time monitoring of equipment health in the thousands of equipment. When EHI decreases and equipment alarms, the firm can trace the root cause by the decomposition of EHI for trouble-shooting.


61. Work Study and Simulation Optimization of Supply-Demand Balancing in the Moth Orchid Plant Factory

Jia-Ying Cai, Chin-Yi Tseng, Ting-Syun Huang

National Cheg Kung University, Taiwan, Taiwan

During the four-year production lead-time, some inevitable factors (e.g., insect pest, plant disease) might have a negative impact on the quality of Phalaenopsis (i.e., moth orchids); besides, the market fluctuation results in difficulties of decision-making and unstable income of the company. This study develops a two-stage framework which investigates the production process in the 1st stage and balance the supply and demand in the 2nd stage. An empirical study of a moth orchid plant factory is conducted. The first part we use time motion study to build up operator process chart and process sequence. Based on the lean production management, we identify seven muda (e.g., transportation waste, inventory waste, etc.) and eliminate them. Thus, the standard operating procedures (SOP) can be developed for the plant factory. In the second part, we build up a simulation model of the production process via the SOP. We use the production input and output collected data to figure out the variable parameters (e.g., yield). We also collect the global moth orchid market supply distribution and decompose the market share to the case factory to know the supply distribution of the case factory (i.e., global market share of the case factory). According to the decomposed supply distribution as our output distribution into the model, we can obtain the input portfolio with minimal cost by simulation optimization technique to address the demand-supply mismatching problem.


117. Development of a process data-based strategy for conditioning position-controlled ID cut-off grinding wheels in silicon wafer manufacturing

Uwe Teicher, Wolfgang Dietz, Andreas Nestler, Alexander Brosius

Technische Universität Dresden, Germany

Manufacturing technologies in the semiconductor industry put high demands on accuracy and process reliability, which is reflected in high manufacturing costs. Particular attention has to be paid to the initial steps of wafering, as these processes can significantly help in determining important quality parameters and can have a strong economic influence on subsequent processes.

ID grinding has established itself as a cost-effective manufacturing method for the production of wafers with a diameter of up to 150 mm. Further developments in mechanical engineering aimed at improving the quality parameters TTV and Bow, resulted in the integration of a magnetic position control, which specifically influences the axial position of the grinding wheel and thus also the position of the abrasive layer in the grinding gap. However, applying the position control results in a modified scenario for the conditioning of the grinding wheel, since control signals for activating a conditioning measure can no longer be used.

The approach to solving this problem, is to develop a conditioning strategy which is, on the one hand, based on data provided by the grinding machine. On the other hand, it also describes which signals - generated by a process computer for position control - are being processed, evaluated and made available to the grinding machine.

As a result, an operational manufacturing solution is presented, which can help to improve the performance of position control in connection with a modified strategy for the conditioning of the abrasive layer in order to improve the quality parameters of IC wafers.



 
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