실험계획법을 이용한 원격 레이저용접 프로세스의 주요 파라미터 도출

Kezia Amanda Kurniadi(K. A. Kurniadi),류광열(K. Ryu),김덕영(D. Kim) 한국정밀공학회 2012 한국정밀공학회 학술발표대회 논문집 Vol.2012 No.5월

Development of Mathematical Models for Facility Planning in Reconfigurable Manufacturing Systems

Kezia Amanda Kurniadi,류광열 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11

Reconfigurable Manufacturing System (RMS) appeared as a solution to high variation in customer demands allowing manufacturers to satisfy different amount of demands in each single period. RMS arranges system configuration or relocates facilities to match the capacity exactly when and where needed; adding and removing machines whose number depends on the amount of demand. Relocating machines often requires highly expensive cost and well-planned factory floor space with high utilization. And often, companies provide limited budget and factory floor space. In this study, RMS facility is arranged and RMS configuration is designed to optimize the available factory floor space, and then penalty is applied when there is tardiness in satisfying customer demands. Multi-objective mathematical models with scoring method are developed to support facility planning in RMS by optimizing factory floor/space as well as minimizing costs at relocating machines and penalty of tardiness. An exemplary case study is also provided.

Identification of remote laser welding parameters for the gap control based on Taguchi method

Kezia Amanda Kurniadi,류광열,이상일,이화섭,김덕영 한국경영과학회 2012 한국경영과학회 학술대회논문집 Vol.2012 No.5

Remote Laser Welding (RLW) is emerging as a powerful and promising joining technology for sheet metal assembly. RLW can easily create joints through robot repositioning and laser beam redirection from a remote distance. To keep or increase RLW quality, tight dimensional control of a part-to-part gap is essential, which can be obtained from correct combination of welding parameters. In this paper, the concept of Taguchi method is used for identifying welding parameters. The signal to noise (S/N) ratio and analysis of variance (ANOVA) are carried out in order to discover the main function and the influence of factors . Therefore, significant parameters affecting the quality of RLW can be clearly identified by using the proposed methods.

Identification of remote laser welding parameters for the gap control based on Taguchi method

Kezia Amanda Kurniadi,류광열,이상일,이화섭,김덕영 대한산업공학회 2012 대한산업공학회 춘계학술대회논문집 Vol.2012 No.5

Remote Laser Welding (RLW) is emerging as a powerful and promising joining technology for sheet metal assembly. RLW can easily create joints through robot repositioning and laser beam redirection from a remote distance. To keep or increase RLW quality, tight dimensional control of a part-to-part gap is essential, which can be obtained from correct combination of welding parameters. In this paper, the concept of Taguchi method is used for identifying welding parameters. The signal to noise (S/N) ratio and analysis of variance (ANOVA) are carried out in order to discover the main function and the influence of factors . Therefore, significant parameters affecting the quality of RLW can be clearly identified by using the proposed methods.

Green Product Design based on PDM, Green-BOM, and QFD

Kezia Amanda Kurniadi(케지아 아만다 쿠르니아디),Kwangyeol Ryu(류광열),Xuehao Feng(풍설호) (사)한국CDE학회 2013 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2013 No.1

The changes in the environment and several environmental regulations force industry to develop products which preserve environmental issues as well as allow them to maintain their market share and competitive advantage. Important factors that have to be considered at the design stage include the material selection for components, product structure, as well as the customer requirements. To design such effective system, a Green Bill of Material (Green-BOM) combined with correct knowledge of product structure is required as critical factors. In addition, by applying Quality Function Deployment (QFD), the technical requirements of a product are transformed into customer requirements. This study integrates QFD with Product Data Management (PDM) for effective management of green product data with Green-BOM representation while complying with specified eco-friendly attributes from Green-BOM and QFD to clarify whether the product meets customer needs.

A Framework of Reconfigurable Machining Modules for Reconfigurable Manufacturing Systems

Kezia Amanda Kurniadi,류광열 한국경영과학회 2019 한국경영과학회 학술대회논문집 Vol.2019 No.4

A reconfigurable manufacturing system (RMS) was firstly introduced due to the problem coming from manufacturing companies in meeting with frequently changing demands in a fast pace. The crucial issue in achieving optimal RMS is the reconfiguration process which requires adding and removing machines when a new demand emerges. This reconfiguration process is strictly related to the company budget, time, and space. In order to physically implement a fully working RMS in the real manufacturing world, manufacturers need to consider an applicable integrated system to optimize the reconfiguration planning process. However, the real time operational decision-making for RMS is limited in literature compared to the production planning problem. This study copes with reconfiguration planning problem, by utilizing cyber physical system (CPS) so that such problem can be solved, automated, and controlled. This paper introduces reconfigurable machining modules (r-modules) as representation of CPS components, where each includes hardware, software module, and sensor module. Each sensor module would be able to measure most of geometric errors of the machine tool. The result of these is sent to machine controller to be stored and analyzed, and lastly used to prevent errors in the future. Finally, the result of this paper is a framework defining the connection and controller of r-modules, as well as how decision-making takes place considering a specific reconfiguration parameters and goal, which differs for each company.

The Development of Reconfiguration Cost Minimization Model for Reconfigurable, Self-Adapting and Transformable Manufacturing System

Kezia Amanda Kurniadi,류광열 대한산업공학회 2019 대한산업공학회 추계학술대회논문집 Vol.2019 No.11

In recent manufacturing system’s environment, reconfiguration process is one of the key issues in achieving and satisfying a fast-changing, fully-customized, high demand production requirement. Reconfiguration process involves not only reconfiguration of physical equipments, but also software or computer applications, even company’s database or system’s calculations. In reconfiguration manufacturing system, reconfiguration process happens when the new demand is received, then the production floor will calculate how many machines exactly needed to satisfy the new demand. Reconfiguration process has a strong relationship with cost, time, and energy. When it happens, the most crucial things to take on consideration is reconfiguration cost and how the company can satisfy the new demand within the customer’s duedate. Therefore, in order to minimize the reconfiguration cost, not only a thorough objective function and constraints are needed, but we also need to examine the complexity and various attributes of reconfiguration process. This paper addresses the various in-depth order of reconfiguration’s step-by-step process followed by each required formulation to finally accomplish a reconfigurable, self-adapting, and transformable manufacturing system.

Design of a System Layout for Reconfigurable Manufacturing System with Theory of Constraints

Kezia Amanda Kurniadi(쿠르니아디 케지아 아만다),Kwangyeol Ryu(류광열) (사)한국CDE학회 2017 한국CDE학회 논문집 Vol.22 No.2

This paper presents a systematic approach for design of timely and proper layouts of a manufacturing system facilitating reconfigurability, referred to as a reconfigurable manufacturing system. A proper methodology for design of a system layout is required for reconfiguration planning – adding or removing machines for supplying the exact capacity needed to fulfill market demands, as well as minimizing the cost of adding new machines. In this paper, theory of constraints is used to make reconfiguration manufacturing systems more cost-effective and efficiency. The proposed approach is validated by using a real industrial case. This paper suggests that the proposed study should be performed concurrently with the design of a new manufacturing system.

Real-Time Parameter Adjustment and Fault Detection of Remote Laser Welding by using ANN

Kezia Amanda Kurniadi,류광열,김덕영 한국정밀공학회 2014 International Journal of Precision Engineering and Vol. No.

Remote Laser Welding (RLW) has been considered as a new and promising green technology for sheet metal assembly in automotiveindustry because of several benefits, such as reduced processing time, decreased factory floor footprint, flexible process base for futuremodel introduction or product change, as well as reduced environmental impact through reduction in energy consumption. However,the recent RLW systems are limited in their applicability due to lack of systematic control methodologies. Therefore, this study aimsto develop a control module to obtain good quality joints of RLW by using Artificial Neural Network (ANN) model consisting of twostages, fault detections and parameter adjustments. A certain combination of parameters value, such as melting temperature, part typeand thickness, laser power, and welding speed, is used as an input for the network in the first stage. The first network can recognizethe fault patterns and gives an estimated faults type as an output. Then, the second stage performs sensitivity analysis of output faultsand generation of adjustment rules, resulting in parameters adjustment rules as the final output. The proposed module will providea systematic control of RLW joints during the production and facilitate acceptable faults detection to reduce defectives.

Real-Time Parameter Adjustment and Fault Detection of Remote Laser Welding by using ANN

Kurniadi, Kezia Amanda,Ryu, Kwangyeol,Kim, Duckyoung 한국정밀공학회 2014 International Journal of Precision Engineering and Vol.15 No.6

Remote Laser Welding (RLW) has been considered as a new and promising green technology for sheet metal assembly in automotive industry because of several benefits, such as reduced processing time, decreased factory floor footprint, flexible process base for future model introduction or product change, as well as reduced environmental impact through reduction in energy consumption. However, the recent RLW systems are limited in their applicability due to lack of systematic control methodologies. Therefore, this study aims to develop a control module to obtain good quality joints of RLW by using Artificial Neural Network (ANN) model consisting of two stages, fault detections and parameter adjustments. A certain combination of parameters value, such as melting temperature, part type and thickness, laser power, and welding speed, is used as an input for the network in the first stage. The first network can recognize the fault patterns and gives an estimated faults type as an output. Then, the second stage performs sensitivity analysis of output faults and generation of adjustment rules, resulting in parameters adjustment rules as the final output. The proposed module will provide a systematic control of RLW joints during the production and facilitate acceptable faults detection to reduce defectives.