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Global Engineers and Technologists Review

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Page 1: GETview Vol3 No2 March 2013
Page 2: GETview Vol3 No2 March 2013

Committee of the Global Engineers & Technologists Review

Chief Editor Ahmad Mujahid Ahmad Zaidi, MALAYSIA

Managing Editor

Mohd Zulkifli Ibrahim, MALAYSIA

Editorial Board

Dr. Arsen Adamyan Yerevan State University

ARMENIA

Assoc. Prof. Dr. Gasham Zeynalov Khazar University

AZERBAIJAN

Assistant Prof. Dr. Tatjana Konjić University of Tuzla Bosnia and Herzegovina

BOSNIA and HERZEGOVINA

Assistant Prof. Dr. Muriel de Oliveira Gavira State University of Campinas (UNICAMP)

BRAZIL

Assoc. Prof. Dr. Plamen Mateev Sofia University of St. Kliment Ohridsky

BULGARIA

Dr. Zainab Fatimah Syed The University of Calgary

CANADA

Assistant Prof. Dr. Jennifer Percival University of Ontario Institute of Technology

CANADA

Prof. Dr. Sc. Igor Kuzle University of Zagreb

CROATIA

Assoc. Prof. Dr. Milan Hutyra VŠB - Technical University of Ostrava

CZECH

Prof. Dr. Mohamed Abas Kotb Arab Academy for Science, Technology

and Maritime Transport EGYPT

Prof. Dr. Laurent Vercouter INSA de Rouen

FRANCE

Prof. Dr. Ravindra S. Goonetilleke The Hong Kong University of Science and Technology

HONG KONG

Assoc. Prof. Dr. Youngwon Park Waseda University

JAPAN

Prof. Dr. Qeethara Kadhim Abdulrahman Al-Shayea Al-Zaytoonah University of Jordan

JORDAN

Prof. Yousef S.H. Najjar Jordan University of Science and Technology

JORDAN

Assoc. Prof. Dr. Al-Tahat D. Mohammad University of Jordan

JORDAN

Assoc. Prof. Dr. John Ndichu Nder Jomo Kenyatta University of Agriculture and Technology-

(JKUAT) KENYA

Prof. Dr. Megat Mohamad Hamdan Megat Ahmad The National Defence University of Malaysia

MALAYSIA

Prof. Dr. Rachid Touzani Université Mohammed 1er

MOROCCO

Prof. Dr. José Luis López-Bonilla Instituto Politécnico Nacional

MEXICO

Assoc. Prof. Dr. Ramsés Rodríguez-Rocha IPN Avenida Juan de Dios Batiz

MEXICO

Dr. Bharat Raj Pahari Tribhuvan University

NEPAL

Page 3: GETview Vol3 No2 March 2013

Prof. Dr. Abdullah Saand Quaid-e-Awam University College of Eng. Sc. & Tech.

PAKISTAN

Prof. Dr. Naji Qatanani An-Najah National University

PALESTINE

Prof. Dr. Anita Grozdanov University Ss Cyril and Methodius

REPUBLIC OF MACEDONIA

Prof. Dr. Vladimir A. Katić University of Novi Sad

SERBIA

Prof. Dr. Aleksandar M. Jovović Belgrade University

SERBIA

Prof. Dr. A.K.W. Jayawardane University of Moratuwa

SRI LANKA

Prof. Dr. Gunnar Bolmsjö University West

SWEDEN

Prof. Dr. Peng S. Wei National Sun Yat-sen University at Kaohsiung.

TAIWAN

Prof. Dr. Ing. Alfonse M. Dubi The Nelson Mandela African

Institute of Science and Technology TANZANIA

Assoc. Prof. Chotchai Charoenngam Asian Institute of Technology

THAILAND

Prof. Dr. Hüseyin Çimenoğlu Instanbul Technical University (İTÜ)

TURKEY

Assistant Prof. Dr. Zeynep Eren Ataturk University

TURKEY

Dr. Mahmoud Chizari The University of Manchester

UNITED KINGDOM

Prof. Dr. David Hui University of New Orleans

USA

Prof. Dr. Pham Hung Viet Hanoi University of Science

VIETNAM

Prof. Dr. Raphael Muzondiwa Jingura Chinhoyi University of Technology

ZIMBABWE

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Dear the Seeker of Truth and Knowledge

To bring a new journal into the world class literature is a great challenge, especially when the aim of the journal is to publish the high quality manuscripts. This is as shown in the right-path progress of The Getview to going to the excellent position. Certainly, the relentless work and vision of editorial board inspires The Getview track, beside their helpful reviews given to assist authors in improving the manuscripts. The mission of the journal will not change: We seek to publish the best work that bridges the interests of two or more communities in engineering and technology. Due to become a great journal recognized is not only where the authors choose to send their most exciting findings, but also on the application and practicable approaches by many ways in which a study can fulfill this criterion, then some work bridges different literatures to transform a question and its importance to the field related with value interdisciplinary research constructed is also the reasons to value the best research of any kind. Hence, by emphasizing on the developing of knowledge, The Getview would like to invite you to participate in the next volume publication by submitting your most important research and encouraging your colleagues to submit the quality manuscripts to us. Regardless the manuscript is accepted or not, one of the great benefits The Getview can provide to the prospective author(s) is mentoring nature of our review process. Prof. Ahmad Mujahid Ahmad Zaidi, PhD. Chief Editor The Global Engineers and Technologists Review

Page 5: GETview Vol3 No2 March 2013

©PUBLISHED 2013

Global Engineers and Technologists Review

GETview

ISSN: 2231-9700 (ONLINE)

Volume 3 Number 2

February 2013

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, electronic, mechanical photocopying, recording or otherwise,

without the prior permission of the Publisher.

Printed and Published in Malaysia

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Vol.3, No.2, 2013 1. LEAN WASTE ANALYSIS AND IMPROVEMENT USING DYNAMIC VALUE STREAM

MAPPING ROHANA, A., NOORIRINAH, O., ISA, H., KAMAT, S.R. and MEHAD, M.F.

9. COMPARISON OF AGGREGATION OF SMALL AND LARGE INDUCTION MOTORS

FOR POWER SYSTEM STABILITY STUDY MURIUKI, J.K. and MURIITHI, C.M.

14. A REVIEW ON SUPERSONIC PARTICLE DEPOSITION AS POTENTIAL SOLUTION

FOR AIRCRAFT METAL MOLD REPAIR NOORIRINAH, O., ROHANA, A., JEEFFERIE, A.R. and NUZAIMAH, M.

© 2013 GETview Limited. All right reserved

CONTENTS

ISSN 2231-9700 (online)

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GLOBAL ENGINEERS & TECHNOLOGISTS REVIEW www.getview.org

G.L.O.B.A.L E.N.G.I.N.E.E.R.S. .& .-.T.E.C.H.N.O.L.O.G.I.S.T.S R.E.V.I.E.W 1

ROHANA1, A., NOORIRINAH2, O., ISA3, H., KAMAT4, S.R. and MEHAD5, M.F.

1, 2 Faculty of Engineering Technology 3, 4, 5 Faculty of Manufacturing Engineering

Universiti Teknikal Malaysia Melaka Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, MALAYSIA

[email protected] [email protected]

[email protected] [email protected]

1.0 INTRODUCTION Nowadays, businesses have grown to be more global and competitions are fierce. Thus, companies must strive to improve the effectiveness and efficiency of their operations in order to achieve their goal to satisfy the customer through the exact product, quality, quantity, and price in the shortest amount of time (Tinoco, 2004) by continuously operating at the 'cutting-edge' and extend their conventional parameters to attract the consumers with creativity and innovation as the translation of the company views to "thinking for customer" (Sihombing et al., 2011). With the ever increasing commitment to continually improve the process, Lean Production is now commonly embraced by many companies which valuable concept enables them to identify the actual value of their processes and removing the non-value added activities (Hines and Taylor, 2000). In Lean Production, once the value has been defined, the production process will need to be mapped in order to gain perspective of the material flow and also the information flow. Value stream mapping (VSM) technique is the lean tool used to achieve this. VSM is an enterprise improvement technique by identifying wastes and its sources in order to continuously strive for a leaner production operation (Rother and Shook, 2003). VSM also provides the practitioner with a common platform to evaluate the current state and improve the production process (McDonald et al., 2010).

The various benefits of VSM include its ability to provide the common language for people to communicate and streamline wok processes before applying suitable lean tools and techniques (Lee and Snyder, 2007). VSM also enables manufacturers to visibly view the supply chain issues in order to be more competitive, efficient and flexible (Lasa et al., 2009). In complex manufacturing environment in which there are plenty of processes with complicated product flow, VSM has proven to untangle the complication and enabled various lean improvement implementations (Duggan, 2002) and (Braglia et al., 2006). Although VSM is able to provide a step by step approach to transform the manufacturing process into a leaner operation, it is still a static tool that is limited in the ability to dynamically capture the behaviour and complexity of the system (Lian and Van Landerghem, 2007). Therefore, an enhancement tool will need to be used and simulation is such tool that can be used where the standards icons in VSM are still maintained for ease of communication but using the data gathered as an input to dynamically model the value stream. The dynamic VSM can then be used to observe the effect of changes and improvement made to the production line without having to experiment in the actual production floor. The main reason for the VSM project is to explore the opportunity on another type of production setting which is the metal industry. The second intent is to investigate how simulation can be used to compliment VSM tool such that the key principles of Lean Manufacturing can be tested in the virtual

ABSTRACT

Value stream mapping (VSM) is a powerful lean tool used to visualize the value added activities and helps to minimize the non value added activities occurring in the manufacturing process. VSM on its own is a pen and paper tool which is very tedious and time consuming. Thus, dynamic VSM can be used through the integration of VSM and simulation modelling. This study addresses the development of current state and proposes future state VSM using simulation with a focus on a manufacturing industry to investigate how dynamic VSM can be adapted in the discrete manufacturing environment. Through the lean waste analysis done, improvements such as implementation of Poka-Yoke technique, continuous flow production, pull system and supermarket with Kanban were able to be proposed. Keywords: Lean Manufacturing, Value Stream Mapping, Simulation.

LEAN WASTE ANALYSIS AND IMPROVEMENT USING DYNAMIC VALUE STREAM MAPPING

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environment for their effectiveness without having to disrupt the manufacturing process in the actual production line.

This paper will start with some reviewed literatures on VSM and its integration with simulation. Then, we will describe in detail the development of the current state VSM using the data gathered at a metal manufacturing company. The current state VSM also acts as a framework for the development of the simulation model. A current state simulation model will then be used to perform lean waste analysis through the generation possible alternatives to observe the effect of the improvements. The optimized scenarios of the future state VSM will increase the management’s confidence in adopting Lean culture in their companies. 2.0 LITERATURE REVIEW Lean manufacturing is a comprehensive term referring to manufacturing methodologies based on maximizing value and minimizing waste in the manufacturing process (Borbye et al., 2011). Lean manufacturing is an operational strategy oriented toward achieving the shortest possible cycle time by eliminating waste in production line (Liker, 2004). It is derived from the Toyota production system and its objective is to increase the value-added work by eliminating wastes and reducing unnecessary work. In Lean Manufacturing there are many tools of making the process more efficient. One of which is VSM, a visualization tool oriented to the Toyota version of Lean Manufacturing (Toyota Production System) which helps people to understand and streamline work processes and then apply certain specific tools and techniques of the Toyota Production System (Lee and Snyder, 2007). Value stream mapping is a pencil and paper tool that helps us to see and understand the flow of material and information as a product makes its way through the value stream. Value stream mapping provides a common language for talking about manufacturing process (Rother and Shook, 2003).

VSM is a technique relatively recent that enables manufacturers to solve their economic problems in order to be more competitive, efficient and flexible (Lasa et al., 2009). VSM has been adopted and evolved to plant situations with complex characteristics (Duggan, 2002) and (Braglia et al., 2006) in which demand is random, the number of references is very diverse and have difficult grouping, there are plenty processes, many of them shared with other families and therefore the flows integration become complicated. VSM also has supporting methods that are often used in Lean environments to analyse and design flows at the system level. Although value stream mapping is often associated with manufacturing, it is also used in logistics, supply chain, service related industries, healthcare (Graban, 2011), software development, and product development. Over the years, simulation has been used to construct dynamic VSM that could enable various alternatives to be explored and tested on the computer without affecting the actual production line. Various literatures have shown that simulation based VSM is able to provide information about the dynamic nature of production process and able to shorten the time to evaluate the effect of any changes made to the system (McDonald et al., 2010), (Braglia et al., 2006), (Lian and Van Landerghem, 2007), (McDonald et. al, 2010, Braglia et. al, 2011) and (Parthanadee and Buddhakulsomsiri, 2012).

Simulation is also a tool which is capable of generating resource requirements and performance statistics whilst remaining flexible to specific organizational details. It can be used to handle uncertainty and create dynamic views of inventory levels, lead-times, and machine utilization for different future state maps. This enables the quantification of payback derived from using the principles of lean manufacturing, and the impact of the latter on the total system. The information provided by the simulation can enable management to compare the expected performance of the lean system relative to that of the existing system it is designed to replace (Detty and Yingling, 2000), and assuming that this is significantly superior, it provides a convincing basis for the adoption of lean. Abdulmalek and Rajgopal (2007) also supported that once the simulation model for the current system is verified and validated it can be used to evaluate the future state map and assess the relative impact of adopting the lean approach. In this paper, the complimentary use of VSM and simulation will be tested at metal manufacturing facility. The methodology used in this project will include:

i) Product selection. ii) Data gathering at the various work center (production, production planning, logistics, maintenance

and purchasing). iii) Development of current state value stream map (CVSM). iv) Development of simulation model. v) Lean Waste Analysis using Simulation Model. vi) Proposing the future value stream map (FVSM).

3.0 DYNAMIC VSM AT METAL MANUFACTURING The first step to any VSM activity is to start with selecting a product family. The identification of the product families were done from the customer end of the value stream. A family is a group of products that pass through similar processing steps and over common equipment in downstream processes. The tool used for this step is

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Product Quantity (PQ) analysis. Tapping et al., (2002), displays PQ analysis using product mix Pareto chart which graphically demonstrates the Pareto principle - also known as the 20:80 rules - and helps separate the “critical few” from the “trivial many”.

Figure 1 shows a combination of eight products; Hook ABC Suspension LV, Bolt & Nut GVD HRH Dia 5/8" x 7", Band Universal 7.5m & 9m, St Ltg Long, Bracket (Long Arm), Stay Rod 2.5m x 19mm, Bow with Thimble, Triangular Bracket For HV ABC, and St Ltg Bracket (Short Arm) together make up to 80% of the total quantity produced. The chart also provides the information that product to quantity ratio is approximately 20:80 (in other words, 20% of the product types account for 80% of the total quantity of products produced), thus we have a high volume, low-variety product mix on which we should focus value stream improvement efforts.

Figure 1: Pareto chart for product quantity analysis.

From PQ analysis, we continue to develop the Product Routing Matrix (PRM) to learn about how each family moves through the process. Table 1 illustrates the PRM of the metal manufacturing process. The table shows the eight products and the processes that each product needs to go through. For example, three products; Hook ABC Suspension LV, ST LTG Bracket (Short Arm), and ST LTG Long Bracket (Long Arm) has passed through similar processing steps and over common equipment in downstream process.

Table 1: Metal manufacturing product routing matrix.

Qty. Product & Process Cutting Stamping Rolling Threading Welding Platting Assembly Resupply 77000 Hook ABC Suspension LV x x x x x 52000 Bolt & Nut GVD HRH Dia 5/8” x 7” x 42000 Band Universal 7.5m & 9m x x x 32000 STLTG Long Bracket (Long arm) x x x x x 31000 Stay rod 2.5m x 19mm x x x x 29500 Bow with Thimble x x x x 29000 Triangular Bracket for HV ABC x x x 26500 STLTG Bracket (Short Arm) x x x x x

3.1 Product Selection – Hook ABC Suspension LV From the PQ analysis and PRM and discussion with the management, Hook ABC Suspension LV is chosen to represent the product family in the CVSM. This suspension hook can be customized as per the customer’s specifications. In addition, the suspension hook is widely known for its durability, quality, and corrosion resistance and is used by the client for cable suspension or dead end clamp. The customer currently ordered the product every 3 months in batches of 77000 pieces per order or 25,667 units per months using galvanized mild steel as the raw material. Figure 2 shows the basic components of the Hook ABC suspension.

Figure 2: Basic components for hook ABC suspension LV.

3.2 Data Gathering at Various Work Centre The material flow begins at the receiving area as raw material and travels through the plant until it reaches the shipping area as finished goods. The sequence of production steps to produce Hook ABC Suspension LV is shown in Figure 3. The information on the resources required to produce the Hook ABC

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suspension are also gathered in order to fully understand the nature of the process in the production line. The process from cutting until thread rolling is done in the same factory. However, the galvanized process requires the product to be sent to another processing factory located quite a distance away and is causing a high lead time to the overall process stream. The product will return to the factory to go through the marking and assembly process before being sent for packaging and ready to be shipped to customer. In addition to the product process flow, information on each process cycle time, changeover time, availability time, machine uptime and operator allocation are also gathered in order to develop the CVSM.

Cutting

(outside vendor)

1st Bending 2nd Bending Sizing Caulking

FormingThread RollingGalvanizedAssembly Marking

Figure 3: Process flow for hook ABC suspension LV.

3.3 Current State Value Stream Map (CVSM) The current state map is depicted in Figure 4. Information regarding the operation name, number of operators required, cycle time (C/T), changeover time (C/O), total available time and uptime percentage are given in each box. The cycle time is calculated in sec/batch. The changeover time is derived from estimates provided by the production personnel. The available time is calculated based on regular production time of 8 hours per shift. From the CVSM, the total production lead time is calculated to be 13 days. Total processing time of the current stream is 209 seconds, which is also the value added time. This means only 0.04 % out of total production lead time of the time is required to produce one pieces of Hook ABC Suspension LV product family. In other words, the CVSM has revealed that 99.96 % of the total production lead time consists of wastes and have opportunities to be improved.

Figure 4: Current state VSM (CVS) of hook ABC suspension LV.

3.4 Development of simulation Model A simulation model which imitates the CVSM was constructed using Simul8 software. The discrete event system modelling require input data such as cycle time, processing time, schedule repair time, total down time and inventory. However, to ensure model randomness, we use Minitab statistical package to determine the cycle time distribution to be used in the simulation model. There are two different views of the model. The first is a “facility view” that describes the work cells and related resources that make up the factory.

The second is a “product view” that describes the flow of work through the facility. Data related facility view was collected from each stations and machines. Moreover, the product should be described to be produced, along with the process plan that defines its flow through the factory. The collected data include: quantity of demanded parts by the nest stations, earliest start date and due date for completion of a job, order times for each part by the next stations, initial quantity for each part in supermarkets, cycle time for each operation, buy lead time for suppliers, quantity and arrival date for parts from outside suppliers, production and reorder lot size for outside suppliers, numbers of worker and available working

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hours. It is important to be able to determine if the system meets specifications and if its outputs are correct. Thus, one of the most essential and difficult tasks faced by the researchers is the verification and validation processes. The approach used to verify this simulation model is by constructing a flow diagram that includes each logically possible action in system can take when an event occurs, and follow the model logic for each action for each event type. The verification flow diagram presented in Figure 5, the flow of material and information as a product makes its way through the value stream

Figure 5: Partial flow diagram of the product view.

The flow begins when customer request their requirement by three month contract. After production control has received customer order, they will order material at supplier by monthly. However, the supplier has been directed to deliver the material weekly. When the materials are received, it needs to be placed in the inventory stage for several days before starting the cutting processes. After that, the work pieces will be placed at Work in Process (WIP) area waiting in queue for the next process. Next, the work pieces will flow through to the value stream. When the work pieces are assembled at the last station, the product will be waiting at the finish goods inventory to be delivered weekly to the customers. The flow shows the process to be repetitive since the same customer will be requesting the same product in the next three months time. The simulation model is now ready to be developed based on this flow chart. These sequences are critical in order for the developer to conceptually plan the flow and each logical possible action can be captured in accurately.

The goal for the validation process is to produce a model that represents true system behaviour closely enough for the model to be used as a substitute for the actual system for the purpose of experimenting with the system, analysing system behaviour and predicting system performance and the

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other goal is to increase the credibility of the model to an acceptance level. Thus, the initial sampling test is done by running the simulation model with 10 replications. Then, a power and sample size test was done and the result showed 32 observations are required to validate the model. From Figure 6, the steady-state of real system and simulation model respectively is considered as 7.82 seconds and 7.83 seconds. Both values are not too significantly thus, this simulation model is valid and ready to be used to develop the future state value stream map (FVSM)

Figure 6: Model validation: replication result.

3.5 Future State Value Stream Map (FVSM) The purpose of VSM is to highlight sources of waste and try to eliminate that waste by analyse the current state maps and then develop the future state maps. The goal is to build a chain of production where the individual processes are linked to others process either by continuous flow or pull. The researcher has found that the most useful aid to develop future state maps is the following list of questions. We follow a systematic procedure to try answering a series of structured questions; which allows us to come up with an ideal future state map that will help in eliminating or at least reducing different types of waste in the current manufacturing system. Based on the answers, the future state idea is marked directly on the current state map and after the possible solutions are explored, the FVSM are possible to be proposed.

3.5.1 How to reduce bottleneck in the value stream? From simulation, the forming process is found to be the bottleneck for the whole process. However, based on discussion with the management, the forming process is not needed if the sizing process is done well. Since this type of waste is considered as over-processing in Lean Manufacturing, the management has formed a special team to look into how to redesign the jigs and fixtures used sizing process such that the forming process can be eliminated. The result of eliminating the forming process was able to reduce the Total Production Lead Time from 13 days to 11 days which is a 15 % improvement.

3.5.2 Where can continuous flow introduced in the value stream? In pure continuous flow, the cycle time equals the lead time, as the product never sits in a queue waiting to be worked on. In other words, the waste of waiting time also can be eliminated. In fact, continuous flow production does not require the inventory in between the processes. So, waste of inventory also can be eliminated when product is not queuing for to the next process.

The proposal is to reconfigure the 1st bending, 2nd bending and sizing workstation into one workstation (operation 2, 3 and 4 in Figure 8: After Improvement). This means the operator is required to complete these 3 processes before moving on the next process which is caulking and marking (operation 5 and 6 in Figure 8: After Improvement). Since the cycle time at the thread rolling process is very fast, only one machine is needed and the threading process time still does not exceed the overall production Takt time. Thus, for this recommendation the motion, inventory, and waiting wastes are able to be eliminated.

Figure 8: Spaghetti diagram of before and after workstation assignment improvement.

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3.5.3 Where Kanban concept needs to be introduced? Based on customer requirement, the company make weekly deliver to 4 different customers with different production quantities. product can sent to a finished goods supermarket first. Currently there is no specific batch sizing determined in the production line. Consequently, a batch quantity of 60 units per tray is introduced to make it easy to meet the different quantities needs of the customers. The quantity is determined to optimize the steel rod utilization during the beginning of the process. After the shipping department withdraws trays from the supermarket to prepare them for delivery, the kanban from those trays are sent back to assembly line to trigger another batch of production

3.5.4 Where will the pull system supermarket be used in the value stream? Kanban system produces exactly what is ordered, when it is ordered, and in the quantities ordered. Kanban can act as a system of information that integrates the production line, connects all processes one to another, and connects the entire value stream to customer demand. Based on the combined processes in question 2, we considered to use the pull system supermarket at six places. The first kanban is placed before the cutting process. The weekly delivery of steel rods from supplier is stocked at this supermarket. Then, the daily demanded rods are pulled out from the cutting process based on second kanban requirement. Next, the third kanban will be located in between the proposed grouped operation of 1st bending, 2nd Bending and Sizing. That is, a batch of 60 pieces will be pulled from the previous workstation into the production kanban. Then, the chauking and marking process will pull the work-pieces to the fourth kanban to be pulled by thread rolling process. Product will enter the fifth kanban before travelling out from the factory to the galvanized process. With the batching system, the initial lead time of 5 days can be reduced to 3 days and product will enter the sixth kanban to be pulled by the assembly process. Each empty container from the six kanbans will be sent back to the previous workstations ready to be refilled.

The lean waste analysis done has enabled us to highlight the sources of wastes in the production system and with the support of simulation modelling, we are able to explore various options to improve the processes. Figure 9 summarizes the questioning scenarios discussed. The FVSM shows a 70.7% reduction in Production Lead Time (PLT) from 13 days to only 3.81 days. The process time or value added time has reduced 10% from 168 sec to 151 sec. We are also able to reduce 2 operators by combining the 1st bending/ 2nd bending/sizing operation and also the caulking and marking operation which makes up to 14% reduction on workers requirement. The process cycle efficiency (PCE) has also improved from 0.04% to 0.11%.

Figure 9: Future state value stream map.

4.0 CONCLUSION The dynamic VSM has demonstrated that simulation is a powerful tool to be used to enhance value stream mapping at the metal industry. The manufacturer is faced with higher operation cost and increased demands from the customers. This requires them to be more creative and lean manufacturing is used to assist in surfacing out the wastes occurring in the process stream. Based on Product Quantity analysis and Product Routing Matrix,

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The Hook ABC Suspension LV has been chosen to be used as the product to be used to develop the CVSM. The CVSM has enabled us to communicate with the company’s management and together we were able to visualize the various types of wastes occurring throughout the manufacturing processes.

Through the lean waste analysis, opportunity to eliminate the bottleneck which is the forming process by re-evaluating the jig and tools design of the sizing process was captured in the simulation model. The possibility to combine a few processes and reduce operators was also explored. Through line balancing, the two thread rolling machines were reduced to only one enabling another operator to be reduced at this operation. Moreover, through the introduction to the kanbans and pull supermarket system, the total production lead time was able to be further improved. 5.0 ACKNOWLEDGEMENT We would like to express our gratitude to the metal manufacturer for providing us with the facility to conduct our study. This research was supported by Universiti Teknikal Malaysia Melaka Fund no PJP/2011/FTK (2C) S00924. REFERENCES [1] Tinoco, J.C. (2004): Implementation of Lean Manufacturing, Master Thesis at University of Wisconsin-Stout. [2] Hines, P. and Taylor, D. (2000): Going Lean, Published by Lean Enterprise Research Center, Cardiff Business

School, UK. [3] Rother, M. and Shook, J. (2003): Learning to See: Value Stream Mapping to Add Value and Eliminate Muda,

Version 1.3, Published by One Cambridge Center USA. [4] McDonald, T., Eileen, M.V.A. and Antonio, F.R. (2010): Utilizing Simulation to Enhance Value Stream

Mapping: A Manufacturing Case Application. International Journal of Logistics Research and Applications, Vol.5, No.2, pp.213-232.

[5] Lee, Q. and Snyder, B. (2007): The Strategic Guide to Value Stream Mapping & Process Mapping: Genesis of Manufacturing Strategy, Published by Enna Products Corporation USA.

[6] Lasa, I.S., de Castro, R. and Laburu, C.O. (2009): Extent of the Use of Lean Concepts Proposed for a Value Stream Mapping Application. Production Planning and Control: The Management of Operations, Vol.20, Iss.1, pp.82-98.

[7] Duggan, K.J. (2002): Creating Mixed Model Value Streams: Practical Lean Techniques for Building to Demand, Published by Productivity Press.

[8] Braglia, M., Carmignani, G. and Zammori, F. (2006): A New Value Stream Mapping Approach for Complex Production Systems, International Journal of Production Research, Vol.44, Iss.18-19, pp.3929-3952.

[9] Lian, Y. and Van Landerghem, H. (2007): An Application of Simulation and Value Stream Mapping In Lean Manufacturing , International Journal of Production Research, Vol.45, Iss.13, pp.3037-3058.

[10] Borbye, L., Stocum, M., Woodall, A., Pearce, C., Sale, E., Barret, W., Clontz, L., Peterson, A. and Shaeffer, J. (2011): Industry Immersion Learning: Real-Life Industry Case Studies in Biotechnology and Business, Published by John Wiley & Sons.

[11] Liker, J.K. (2004): The Toyota Way – 14 Management Principles From the World’s Greatest Manufacturer, Published by McGraw-Hill USA.

[12] Graban, M. (2011): Lean Hospitals – Improving quality, Patient Safety and Employee Engagement, 2nd Edition Published by Productivity Press USA.

[13] Parthanadee, P. and Buddhakulsomsiri, J. (2012): Production Efficiency Improvement in Batch Production System Using Value Stream Mapping and Simulation: A Case Study of the Roasted and Ground Coffee Industry, Production Planning and Control: The Management of Operations, pp.1-22.

[14] Detty, R.B. and Yingling, J.C. (2000): Quantifying Benefits of Conversion to Lean Manufacturing with Discrete Event Simulation: A Case Study, International Journal of Production Research, Vol.38, Iss.2, pp.429-445.

[15] Abdulmalek, F. and Rajgopal, J. (2007): Analysing the Benefits of Lean Manufacturing and Value Stream Mapping via Simulation: A Process Sector Case Study, International Journal of Production Economics, Vol.107, Iss.1, pp.223-236.

[16] Sihombing, H., Yuhazri, M.Y. and Safarudin, M. (2011): Developing World Class Company’s Differentiated Competitive Advantage through Human Capital Development, Jurnal Megadigma, Vol.4, No.1, pp.17-47.

[17] Tapping, D., Luyster, T. and Shuker, T. (2002): Value Stream Management, Published by Productivity Press USA.

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MURIUKI1, J.K. and MURIITHI2, C.M.

1, 2 School of Electrical, Electronic and Information Engineering Department of Electrical and Electronics Engineering

Jomo Kenyatta University of Agriculture & Technology P.O. Box 62000-00200, Nairobi, KENYA

[email protected] [email protected]

1.0 INTRODUCTION Induction motors are the main dynamic loads in an electric power system. The power system consists of many small sized and large sized induction motors consuming power in a power network. Therefore, for improved analysis of the accuracy of model parameters identification, it is crucial to aggregate a group of induction motors and compare with the results of the individual motors. In facts, load model in large power systems has received increasing concerns in recent years for system operation and control, Kostrev et al., (2008) and Chinn (2006). This poses a threat if aggregation accuracy of induction motors parameters is not extensively analyzed, and more so if a power system is weak and prone to disturbance. Besides, different power system load models have diverse impact on the simulation result of power system dynamic loads. However, it is impractical to accurately represent each individual load due to the intense computation process involved. Appropriate dynamic load model aggregation reduces the computation time and provides a faster and efficient model derivation and parameters identification. The accuracy of these different sizes of induction motors is investigated and analyzed.

Different aggregation methods have in the past been proposed by Hakim and Berg (1976); Franklin and Morelato (1994); Lem and Alden (1994); Pillay et al., (1997); Bing et al., (2010). However, this paper discussed the method of induction motor aggregation as proposed by Karakas et al., (2009) and Muriuki et al., (2012). The goal of this paper is to represent and compare the accuracy of the aggregation of different sizes of induction motor model using a single equivalent motor model thereby reducing the computation time for large power system. The three-phase squirrel cage induction motors are connected separately to a common bus in a power system using their equivalent circuits when operating in the no–load and locked-rotor conditions. In the former operating conditions, it is assumed that the slips of all induction motors are equal to zero while for the latter the slip is equal to unity. The aggregation method is based on thevenin’ns theory of electrical network. The simulation is carried out using Matlab/Simulink. The comparison of the different sizes of induction motor aggregation is investigated using the IEEE 16 bus standard test system found in literature. 2.0 METHODOLOGY

2.1 Aggregation of Induction Motors Simulation of induction motors is computationally feasible only if the group of individual three-phase single-cage-rotor induction motors are accurately aggregated as single equivalent motor model.

ABSTRACT

Aggregation of different sizes of induction motors (IM) connected to a power system has diverse precision on the identification of the aggregate model parameters. Earlier work on the aggregation accuracy of various motors sizes has not been extensively identified. Also, IM loads forms the highest component in power system load. Therefore, the paper proposes to compare the accuracy of the aggregate model parameters of a group of IM of different sizes and validate the results by comparing with the individual induction motor parameters. The simulations and the analysis are carried out using Matlab/Simulink-based software package. The performance of the developed model show that, small-scale induction motors gives better accuracy of the aggregate motor parameter when compared with large-scale motor parameters. It is found that the proposed small-scale aggregation model gives acceptably accurate results than the large-scale aggregation model and is good for power system stability analysis. Keywords: Aggregate Model, Induction Motor, Parameter Accuracy, Matlab/Simulink.

COMPARISON OF AGGREGATION OF SMALL AND LARGE INDUCTION MOTORS FOR POWER SYSTEM STABILITY STUDY

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Aggregation of motors involves making some assumptions and therefore, this paper assumed the following: i) All the motors are of the same type and are connected in parallel and at the same bus with no other

load types. ii) The output power for each sizes of motor is maintained for ease of comparison for the two sizes of

IM while the same number of poles is maintained. iii) The equations used to obtain the aggregate model apply the equations proposed by Karakas et al.,

(2009) see equation in (1) until (21) respectively. Figure 1 Shows the equivalent circuit of the aggregate induction motor load, where RS-stator

resistance, XS-stator reactance, Rr-rotor resistance, Xr-rotor reactance, Xm- magnetizing reactance and S- Slip of the induction motor respectively whose parameters of the aggregate model are identified.

Figure 1: Equivalent circuit model of a three phase induction motor.

2.2 Dynamic Load Models Numerous researches have shown that static load models are not suitable in describing the actual power system behaviour under most of the power system operating conditions. It is however, crucial to note that dynamic load models are the significant load in power system covering 60 % of the total power system load. Therefore, it is better to have dynamic load models which accurately represent the behavior of the load under most of the operating conditions. This is due to their innate behaviour of having inductance and capacitance that store some energy in the rotating parts (rotor) of the machines. Based on this reason, the induction motors are used as dynamic load that are compared to identify their suitability in aggregation. A dynamic load model is a model that expresses the active and reactive power at any instant of time as functions of the voltage magnitude and frequency at past instants of time and, usually, including the present instant. Normally, differential equations are used to represent such kind of loads. To describe the dynamic characteristic of the load, a non-linear dynamic load model with exponential recovery is considered in different types of dynamic load model as discussed in subtopic 2.2.1 and 2.2.2.

2.2.1 Differential model The Differential model is presented a set of non-linear equations, where active and reactive powers have a nonlinear dependency on voltage Navarro (2002).

0 0

0 0r

s tr

rPdP V VT P P Pdt V V

(1)

0

0

t

T rVPV

P P

(2)

0 0

0 0rq

s tr

rdQ V VT Q Q Qdt V V

(3)

0

0

t

T rVQ QV

Q

(4)

Where αt-transient active and load dependence, Tp-acitve load recovery time, αs is the steady state active load-voltage dependence, Vo and Po are the initial voltage and power consumption, Pr is the active power recovery and PT is the total active power response.

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2.2.2 Slip dynamic load model The 2nd example of dynamic model is slip dynamic load model: the model is based on the effect of both static and dynamic load in the power system. The former dynamic model is commonly applied in the analysis of dynamic characteristics of power system load. In order to analyze the suitability and accuracy of the aggregated induction motors parameters Table 1 and Table 2 below classify small scale and large scale typical induction motor parameters.

Table1: Typical Parameters for individual small induction motors.

HP RS Rr XS Xr Xm 3 0.02 0.037 0.035 0.035 1.21

25 0.022 0.047 0.05 0.05 1.95 50 0.015 0.040 0.053 0.053 2.31

100 0.011 0.047 0.053 0.053 2.51

Table 2: Typical Parameters for individual large induction motors.

HP RS Rr XS Xr Xm 500 0.0185 0.0132 0.0851 0.0851 3.8092 800 0.0148 0.0106 0.0808 0.0808 4.0702

1500 0.0118 0.0078 0.0797 0.0797 4.2026 2250 0.0092 0.0071 0.0718 0.0718 4.1388

2.3 Case Study: Test System Description A Charles Gross 16 bus test system model was used in the analysis of the accuracy of aggregated small-scale and large-scale IM parameters. It consists of nine lines, three generators, and seven load points. The three generators are a steam plant located at Rogers, a hydrogenation plant at Russel Dam and a tie line to an external system connected at Lowry substation. The cities Grigsby, Feasterville, Philipsburg and Honnell represent the major load centers. The hydrogenation plant at Russel Dam and the steam plant at Rogers also take significant loads from the system. The parameters of the system used for analysis applied refer to Muriithi et al., (2011). Bus 3 of the 16 bus was selected for analysis of the system as detailed below: Total bus load = 10 Mw Static load = 8.32 Mw Dynamic load = (10-8.32) Mw = 1.68 Mw 1.68 Mw = (1.68*^6/746) Hp = 2,250 Hp

3.0 SIMULATION RESULTS The simulations were performed using Matlab/Simulink for obtaining and comparing the accuracy of the aggregate small-scale and large-scale induction motor parameters in a power system. The aggregate small-scale and large-scale induction motor parameters are shown in Table 3 and Table 4 respectively. By comparing these results with the typical motor parameters in table 2 and 3, it clearly shows that small aggregate motor parameters give better accuracy.

Table 3: Small-scale aggregate IM with typical data

Table 4: Large-scale aggregate IM with typical data

HP P(HP) No of IM Rs Rr Xs Xr Xm 2250 2250 1 0.0185 0.0132 0.0852 0.0852 3.809 2250 1500 2 0.0148 0.0106 0.0808 0.0808 4.07 2250 800 3 0.0118 0.0078 0.0797 0.0797 4.2026 2250 500 5 0.0090 0.0071 0.0718 0.0718 4.139

Figure 2 (a) and Figure 2(b) shows the result of the aggregate and individual active and reactive power of the small and large-scale induction motors. The results demonstrate that small motors have better accuracy in aggregation of the active and reactive powers, rather than the large scale IM. This is validated by the closeness of

HP P(HP) No of IM Rs Rr Xs Xr Xm 2250 3 750 0.02 0.037 0.035 0.035 1.21 2250 25 90 0.0219 0.0472 0.0498 0.0498 1.95 2250 50 45 0.0150 0.0402 0.0532 0.0532 2.306 2250 100 23 0.011 0.0472 0.0532 0.0532 2.512

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0 0.1 0.2 0.3 0.4 0.5-2000

-1500

-1000

-500

0

500

1000

1500

2000

t,sec

Agg

stator c

uren

t

Agg BAgg YAgg R

Agg stator phase ACurrent

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-6000

-4000

-2000

0

2000

4000

6000

t,sec

ind cu

rrent

Ind BInd YInd R

Individual stator phase A current

0 0.1 0.2 0.3 0.4 0.5-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

t,sec

ind current

Ind R Ind YInd B

Ind stator current

0 0.1 0.2 0.3 0.4 0.5-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

t,sec

Agg

current

Agg BAgg YAgg R

Aggregate stator phase A current

the curves of the aggregate and the individual active and reactive power of the small motors. Similarly, Figure 3 (a) and Figure 3 (b) shows the response of the stator phase current for the aggregate and individual induction motors. It is seen that there are small discrepancies in time domain responses from the model developed. However, small-scale aggregate motors gives a good agreement compared with the results of large-scale aggregate motors. Figure 6 and Figure 7 shows an almost similar model characteristic for stator phase A current for both individual and aggregate motors thereby, validating the results. (a) (b)

Figure 2: Active and reactive power of individual and aggregate induction motor at (a) large-scale, (b) small-scale.

(a) (b)

Figure 3: Responses of large-scale induction motor stator phase A current (a) aggregate, (b) individual.

(a) (b) Figure 4: Responses of large-scale induction motor stator phase A current (a) induvidual, (b) aggregate.

4.0 CONCLUSION This paper has presented the comparison of individual and the aggregate small and large-scale three-phase single-cage-induction motors connected at a common bus. A Matlab code was developed for aggregation of the motor parameters. The aggregation method based on no-load and locked-rotor gives better accuracy in identification of the aggregate motor stator and rotor parameters. The validity of the result has been proved by comparing the simulation results of the aggregate model of the small and large-scale motors with those of the individual induction motors. The accuracy of the aggregation of IM parameters depends on the sizes of the motor and the parameters to be identified. Therefore, depending on the parameters of the motor aggregated, the parameters of the aggregate small-scale induction motors closely resemble that of the individual induction motor parameters hence, gives better parameter accuracy which is crucial for power system analysis. REFERENCES [1] Kostrev, D., Meklin, A., Undrill, J., Lesieutre, B., Price, W., Chassin, D., Bravo, R. And Yang, S. (2008): Load

Modeling in Power System Studies: WECC progress Update. In the Proceeding of Power and Energy Society General Meeting – Conversion and delivery of Electrical Energy in the 21st Century, Pittsburgh USA, 20-24 July 2008, pp.20-24.

[2] Chinn, G.L. (2006): Modelling Stalled Induction Motors. In the Proceeding of Transmission and Distribution Conference and Exhibition, 2005/2006 IEEE PES, Dallas USA, 21-24 May 2006, pp.1325-1328.

[3] Hakim, A.M.M. and Berg, G.J. (1976): Dynamic Single Unit Representation of Induction Motor Groups, IEEE Transactions on Power System, Vol.95, Iss.1, pp.155-165.

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[4] Franklin, D.C. and Morelato, A. (1994): Improving Dynamic Aggregation of Induction Motor Models, IEEE Transactions on Power System, Vol.9, Iss.4, pp.1934-1941.

[5] Lem, T.Y.J. and Alden, R.T.H. (1994): Comparison of Experimental and Aggregate Induction Motor Responses, IEEE Transactions on Power System, Vol.9, No.4, pp.1895-1900.

[6] Pillay, P., Sabur, S.M.A. and Haq, M.M. (1998): A Model for Induction Motor Aggregation for Power System Studies, Electric Power Systems Research, Vol.42, Iss.3, pp.225-228.

[7] Bing, Z. Yong, T. and Wenchao, Z. (2010): Model Representations of Induction Motor Group In Power System Stability, In the Proceeding of International Conference on Computer Application and System Modelling, Taiyun China, 22-24 October 2010, pp.166-176.

[8] Karakas, A., Li, F. and Adhikari, S. (2009): Aggregation of Multiple Induction Motors Using MATLAB-Based Software Package, In the Proceeding of Power Systems Conference and Exposition IEEE/PES, Seattle USA, 15-18 March 2009, pp.1-6.

[9] Muriuki, J.K., Muriithi, C.M. and Kinyua, D.M. (2012): Suitability of Aggregation Methods of Induction Motor Models for Voltage Stability Analysis, Global Engineers & Technologists Review, Vol.2, No.4, pp.23-28.

[10] Navarro, I.R (2002): Dynamic Load Models for Power Systems - Estimation of Time Varying Parameters During Normal Operation, PhD thesis at Lund University, pp.40-42.

[11] Muriithi, C.M., Ngoo, L.M. and Nyakoe, G.N. (2011): Investigating the impact of power system stabilizer in a multi-machine system with induction motor load, in the Proceeding of IEEE AFRICON, Livingstone, 13-15 September 2011, pp.1-6

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NOORIRINAH1, O., ROHANA2, A., JEEFFERIE3, A.R. and NUZAIMAH4, M.

1, 2, 4 Faculty of Engineering Technology 3 Faculty of Manufacturing Engineering

Universiti Teknikal Malaysia Melaka Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, MALAYSIA

[email protected] [email protected]

[email protected] [email protected]

1.0 INTRODUCTION The supersonic particle deposition or cold spray method is a relatively new process by which coating of ductile materials or composite materials with significant ductile phase content can be produced without significant heating of the sprayed powder. The kinetic energy of the particles is sufficient to produce large deformations and high interfacial pressure and temperature, which appear to produce a solid state bond (Gilmore et al., 2010). Supersonic particle Deposition (SPD) was initially developed in the mid-1980s at the Institute for Theoretical and Applied Mechanics of the Siberian Division of the Russian Academy of Science in Novosibirsk (Grujicic et al., 2004). Experiments were performed in a supersonic wind tunnel with very small particles entrained in the high velocity gas stream. The erosive behavior of this particle-laden flow on an object in the wind tunnel was studied. It was discovered that above a particular minimum particle velocity, the abrasion caused by the particles changes to adhesion of the particles, i.e., a coating is formed on the object. This effect is enhanced by an increase in gas temperature (Stoltenhoff et al., 2002). The global maintenance, repair and services industry in aviation industry involve dimensional restoration or tooling repair for metal mold. Standard practice, manual that provided by OEM of the part will be referred and if the defect, example chipped rotor blade is exceed it repairable limit, the part is considered scrap. In this case, Cold spray process can provide total solution through deposition the thick coating of the same material with the substrate to the chipped area without build internal stress. This will provide cost saving through re-use of the part. Thermal spray technique also involved in dimensional restoration or tooling repair but it lead to build up internal stress in the part, influence fatigue failure of the part. Application of heat in thermal spray also lead to higher porosity compare to cold spray technique and it influence the strength and shorten the life of the part. Objective of this paper is to study cold spray technique as potential solution in dimensional restoration for aviation industry in Malaysia. 2.0 THEORETICAL OVERVIEW Cold spray process is a technology in which metal, composite or polymer particles generally 1-50 µm in diameter are accelerated to velocities in a range between 300-1500 m/s by entrainment in a supersonic jet of

ABSTRACT

The global Maintainence , Repairing and Overhaul (MRO) services industry is forecast to be worth US$65 billion by 2020. Malaysia plans to become a regional hub for aircraft maintenance and repairs. The country was currently ranked fifth in Asia and tenth in the world in terms of aircraft maintenance, repair and overhaul. Maintenance or repair technique play a major role in increasing useful life of the aerospace part and it increase the confident level of the user. Supersonic particle deposition has attracted serious attention because it unique thick dimensional restoration properties can be obtained by the process that is not achievable by thermal spraying. This uniqueness is due to the fact that dimensional restoration deposition takes place without exposing the spray or substrate material to high temperature and its ability to perform a bonding with the underlying material without the creation of heat affected zones which are typical of other deposition processes, thermal spray and are undesirable in many structural applications. This paper is an outcome to discuss supersonic particle deposition process as potential repair technique for aircraft metal mould, which is widely acclaimed for and on top of that, re-use of material will contribute to cost saving and in-haze the environment. Keywords: Cold Spray, Supersonic Particle Deposition, Repair, Metal Mould, Aircraft.

A REVIEW ON SUPERSONIC PARTICLE DEPOSITION AS POTENTIAL SOLUTION FOR AIRCRAFT METAL MOLD REPAIR

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compressed gas powder particles to impact a solid surface. The cold spray process utilizes Nitrogen or Helium as a carrier gas with pressures ranging between 100-500 psi. The carrier gas is heated within the gun to temperatures up to about 600°C (Grujicic et al., 2004). Compressed gas of an inlet pressure enters of an inlet pressure and flows through a converging / diverging DeLaval-type nozzle to attain a supersonic velocity. The solid powder particles are metered into the gas flow upstream of the converging section of the nozzle and accelerated by the rapidly expanding gas to achieve higher gas flow velocities in the nozzle, the compressed gas is often pre-heated. These droplets then impact in a substrate to give a high yield of a partially solid deposit of controlled shape. This deposit is cooled by the gas stream and solidification is completed at much slower rates than the initial cooling rates in spray. Particle bonding in cold spray process is due to high rate deformation of the particle, adiabatic shear instability and requires high particle velocity > Vcritical (Grujicic et al., 2004). Advantages of cold spray technology are low temperature process, which it operate below melting point of metals and this will contribute to porosity control below 1%. High density deposits for dimensional restoration can be done for thick coating at high deposition rate and free form also can be fabricate because of compressive residual stresses contribute from low temperature process without creation of internal stress in the part. 3.0 CASE STUDY Aircraft fairing is a structure in aircraft design used to reduce drag and improve appearance. Joggle is a part in the nickel shell mold fairing for Airbus A380 as depicted in Figure 1(b). Problem with this joggle are un-even thickness of the joggle and this will lead to un-even fairing structure for A380 as shown in Figure 1(a).This joggle already undergo thermal spray treatment to overcome un-even thickness problem but it not successful.

(a) (b) Figure 1: joggle (a) undergoes thermal spray process, uneven thickness and scratches, (b) insufficient thicknes.

This joggle is fabricated from Nickel, if using cold spray technique to restore thickness to this joggle area,

the same material must be used as shown in Figure 2. Powder feed stock for nickel generally is nickel, Ni, 99.7% basic mixture with aluminum,Al,99.5%, zinc,Zn,99.7% and alumina,Al203,92%. Particle size will be range -45 to 5µm. Carrier gas temperature for nickel powder coating material is 350-500°C, Gun pressure will be 100-200psi with Gun traverse speed 40mm/second and powder feed rate will be 18 gram per minute. Standoff distance will be 10-25mm. Typical coating or restore dimensional properties using all the parameters above will be deposition efficiency up to 32 %, porosity volume less than 0.5 % with Hardness 76-79HRB and bond strength 6500psi.

(a) (b) Figure 2: Joggle (a) un-even thickness (b) additional thickness required.

4.0 CONCLUSION Dimensional restoration or tooling repair for aircraft part can be done using cold spray technology. This technology can provide cost saving through re-use of part by dimensional restoration process and in-haze environment by saving energy consumption. It recommended this technology for aviation industry in Malaysia, in-line with Malaysia plans to become a regional hub for aircraft maintenance and repairs and MRO services industry is forecast to be worth US$65 billion by 2020.

problematic area

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5.0 ACKNOWLEDGEMENT This work collaboration Faculty of Engineering Technology, University Technical Malaysia Malacca, (UTeM) and CTRM Aviation Sdn.Bhd, Malaysia. REFERENCES [1] Gilmore, D.L., Dykhuizen, R.C., Neiser, R.A., Roemer, T.J. and Smith, M.F. (1999): Particle Velocity and

Deposition Efficiency in The Cold Spray Process, Journal of Thermal Spray Technology, Vol.8, Iss.4, pp.576-582.

[2] Grujicic, M., Zhao, Z.L., de Rosset, W.S. and Helfritch, D. (2004): Adiabatic Shear Instability based Mechanism for Particle/ Substrate Bonding in the Cold-Gas Dynamic-Spray Process, Journal Materials & Design, Vol.25, pp.681-688.

[3] Stoltenhoff, T., Kreye, H. and Richer, H.J. (2002): An Analysis of The Cold Spray Process and Its Coatings, Journal of Thermal Spray Technology, Vol.11, Iss.4, pp.542-550.

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No 59, Jalan Puncak 1,

Taman Puncak,

75450, Bukit Katil, Melaka,

MALAYSIA.