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 4.6: Manufacturing Process and Technology
Wednesday, 28/Jun/2017:
9:00am - 10:00am

Session Chair: Ronny Miguel Gouveia
Location: Aula R (first floor)

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175. Throughput Rate Improvement in Multiproduct Assembly Line Using Lean and Simulation Modeling and Analysis

Mahmoud Nagi, F. Frank Chen, Hung-da Wan

The University of Texas at San Antonio, United States of America

Throughput Rate Improvement in Multiproduct Assembly Line Using Lean and Simulation Modeling and Analysis

Mahmoud M Nagi, F. Frank Chen and HungDa Wan

Department of Mechanical Engineering & Center for Advanced Manufacturing and Lean Systems

The University of Texas at San Antonio, San Antonio, Texas, USA


Variation in customer requirements is increasing day after another. It requires assembly plants to be more flexible to adopt multiple products in each assembly line. Thus, it has been a challenge to improve the throughput of multiproduct assembly lines in most cost effective manner due to the enormous complexity of the multiproduct assembly processes. Products mix and line balancing are usually the main factors contributing to the assembly line performances. Modeling and simulation of assembly lines and implementation of Lean Manufacturing and Six Sigma tools can be effective in finding insights and solutions. In this paper, we used pull simulation module to mimic an engine assembly line where 114 different products are being assembled. Line balancing, leveling and controlling the work in process (WIP) were found to be the driving elements to improve the Throughput. Developing effective design of experiments for the simulation modeling and analysis helped in validating the impact of the changes. Recommended solutions have helped the engine assembly line to increase its throughput rate by 14%.

71. The architecture of system for CNC machine tool programming

Jan Duda, Janusz Pobozniak

Cracow University of Technolgy, Poland

The goal of the paper is to present the architecture of CNC machine tool programming system based on the meta-knowledge and recognition of intermediate workpiece states. This is a Computer Aided Process Planning System (CAPP) with the functionality limited to the machining. Additionally, both the manufacturing knowledge as well ad feature recognition and processing algorithms were developed specially for the CNC machining. It was necessary to develop the module for G-code generation .

The shell expert system Exsys Professional is the main element of the proposed system. This development tool has some solutions facilitating the integration with other software packages. The knowledge is represented in the form of production rules IF... THEN...ELSE. The important function is the possibility to modify the reasoning process though the program written in the special Command Language (CL).

The manufacturing knowledge is very extensive. It is not possible to represent it using only simple rules due to the high number of such rules. The creation of consistent database will be nearly impossible. Additionally, the manufacturing knowledge has some area, which can be represented in the form of procedures. Very often they represent the basic manufacturing principles. To simplify the development of the manufacturing knowledge base and allow for the storage of procedural knowledge, the meta-knowledge in the form of hierarchical decision nets is used.

The workpiece model is created in CAD system. Manufacturing feature recognition and transformation to create the intermediate workpiece states reflecting the progress of manufacturing process were implemented in the software based on ACIS graphic kernel by Spatial Corp. The output of the manufacturing feature recognition is the feature workpiece model. This model can store the data about the manufacturing features in relational database. The data about features used in IF... THEN rules are delivered through the small programs communicating with the workpiece database and reading the output data from relational database. During process planning, the workpiece is transformed from its final state (finished workpiece) to its initial state (raw material) through the series of transformations. The transformation can result in the removal of some features (by adding material) or creation of new features, which must be recognised. The manufacturing feature recognition process is done several times. Apart from the recognition before process planning, also the recognition of workpiece intermediate states must be carried out. Such approach allows to solve the problems caused by interacting manufacturing features. According to many Authors, this is the main problem limiting the wide use of manufacturing feature technology.

The outcome of the system is G-code control program. The THEN part of some rules contains the software commands responsible for the transformation of manufacturing features. The reasoning control program written in CL language adds the beginning and end parts of the G-code program.

The goal of the proposed structure of the system is to verify the approach for CNC machine tool programming based on meta-knowledge and intermediate state recognition.

276. Manufacturing parameters optimization in a textile dyeing process

I-Hsuan Hong1, Zixin Shen1, Sheng-Chieh Chen1, Argon Chen1, Kun-Cheng Tsai2, Yu-Tong Li3

1National Taiwan University, Taiwan; 2Institute for Information Industry,11F, No. 106, Section 2, Heping E. Road, Taipei 106, Taiwan; 3Taiwan Textile Research Institute, No. 20, Kejia Rd., Hsijou Li, Douliou City, Yunlin County 64057, Taiwan

This research is to develop the dyeing parameter optimization model for functional textiles based on the analysis of relationship between the manufacturing parameters in the dyeing process and the dyeing performance. The aim of this research is to minimize the total dyeing cost including the production and energy costs with the consideration of robustness measure and dyeing performance. The first task of this research is to analyze the relationship between the dyeing parameters and dyeing performance by the Central Composite Design (CCD). The result of the CCD is the estimated response surface for the use of our second task of the dyeing parameter optimization to search for the optimal combination of dyeing parameters with a robust performance against the manufacturing variability.

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