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AUGMENTING PRODUCTIVITY BY MINIMIZING SCRAP RATE USING INDUSTRIAL ENGINEERING TOOLS AND AUTOMATION
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AUGMENTING PRODUCTIVITY BY MINIMIZING SCRAP RATE USING INDUSTRIAL ENGINEERING TOOLS AND AUTOMATION
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 903 AUGMENTING PRODUCTIVITY BY MINIMIZING SCRAP RATE USING INDUSTRIAL ENGINEERING TOOLS AND AUTOMATION Harshitha M1, Anusha P2, Rahul S R3, Sandesh Pai4 1-4Undergraduate Students, Dept., of Industrial Engineering and Management, Dr. Ambedkar Institute of Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – This article describes the use of industrial Engineering techniques which improves productivity by minimizing scrap percentage. Implementing IE techniques (Automation, work-study, time study, layoutdesign, operation study) to eliminate human intervention in order to reduce the scrap percentage. This leads to process improvement which resulted in a reduction of waiting time and lead time thereby increase in productivity. Key Words: Value stream mapping, Cause and effect diagram,3D-Scanner, Macro program, process chart,2^3 experiment. 1. INTRODUCTION Productivity is a measure of economic performance that compares the number of goods and services produced by outputs with the number of inputs used to produce those goods and services. Scrap rate evaluates the number of goods companies produce that become waste because of defects or errors during manufacturing. The average productivity of the company was around 1.3 to 1.4lakhs and the average scrap rate is 29%. Powder metallurgy is one of the developing manufacturing techniques in which we can manufacture from small-size inserts to big-sizediesandalso it is an easy method to produce complex shapes in which some of the products do not need any secondary machining process to attain their shape and for some products secondary machining is required. The time consumption in the inspection was more, the Scrap rate was high (both in Green Carbide Inspection and Grinding). 1.1 Problem Identification 1. High scrap rate at the Sintering process due to manual calculation in the Green Carbide Inspection area and more time consumption for production. 2. Inappropriate Measuring Instruments lead to inaccurate measurements due to component sensitivity and brittle nature. 3. Inadequate monitoring and control in the grinding process (surface grinding) leading to poor quality of the finished workpieces 1.2 Literature Review 1. According to the needs ofthe purchasedepartment of the ABC Company, the plan and formulae were kept to be the same but the manual operations from the report were totally removed. As in apropos of VBA, literature was reviewed in the context of automation in MS Excel. 2. Yakubu Yisa, in a 2^3 factorial experiment is designed to examine the influence of such factors as teaching method, gender, and level of study on students’ academic performance. 3. Mario COCCIA, the fishbone diagram can be a comprehensivetheoretical framework torepresent and analyze the sources of innovation. 4. Using an intrinsic function ofourinstitution’sEMR, vital signs and lab results from 20 individual hospitalizations were exported to a spreadsheet. 2. Aims and Objectives Aim: To improve productivity by minimizing scrap rate using Industrial Engineering tools such as (Time study, process chart, simulation, fishbone diagram, Design of Experiments, and standard operating procedure). Objectives: Avoid errorsbyeliminatingmanual calculations and entries on route cards. To reduce the waiting timeformachineoperatorsat green carbide inspection. To obtain accurate measurements using a 3D scanner by providing the standard operation procedure. To ensure the integrity of the component and to prevent damage.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 904 To determine the relevant process variables that have the potential to impact the quality of the system To Develop a structured and well-designed experimental plan that outlines the specific experiments to be conducted. 3. Methodology Figure -1: Methodology 4. Data Collection and Analysis Some of the data collected are: 1. Route card 2. Tolerance charts 3. GCI scrap data 4. Grinding Scrap data Month Total scrap (nos) Scrap due to manual calculation (nos) Scrap due to inefficient measurement system (nos ) February 335 168 58 March 341 170 60 April 220 110 40 Table -1: Scrap rate Figure -2: Scrap rate at grinding Analyzing these data using graphs, simulation, process charts, and fishbone diagrams. Figure -3: Fishbone diagram for analysis 5. Implementation of IE techniques 5.1 Implementation of excel based macro program to eliminate manual entries. The macro program reduces the error due to manual entries. Figure -3: printed output
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 905 5.2 Creating a Standard operating procedure for 3D-Scanner which is to be implemented in the process where the brittle components are measured correctly and safely. It scans the components and measures the component’s measurement without human intervention. Figure -3: SOP for 3D scanner 5.3 Implementing the DOE (23 factorial design) for the factors wheel speed, depth of cut, and feed rate. By designing an experiment, it will give which combination of factors will provide the yield. Here the factors of wheel speedincreased the yield and depth of cut increased. The feed rate is kept constant. Figure -4(A): Yates Algorithm
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 906 Figure -4(B): Yates Algorithm • From F-distribution table F(0.05,1,23) =4.28 Therefore, from the above table Feed rate (C) is significant 6. Results and discussion 6.1 Decreased scrap rate after implementation of macro program Figure -5: Scrap rate due to manual calculation Figure -6: Time consumption Out of Total Green Scrap obtained 50% of scrap is due to manual Calculation. After implementing the macro program, the scrap due to the manual calculation is reduced by 35% After the implementation of themacroprogram,the waiting time in the system decreased, and service time also decreased. 6.2 Decreased scrap due to inefficient measurement system after the implementation of 3D-scanner into the process Figure -7: Scrap rate due to inappropriate measuring instruments Out of Total Green Scrap obtained 30% of scrap is due to inefficient measurement systems. After implementing the 3D-Scanner in the process the scrap due to inefficient measurement system is reduced by 15%
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 907 6.3 Decreased hard scrap (grinding scrap) after changing the combination of method parameters. Figure -8: Scrap rate due to grinding From the graph it is observed, the scrap is reduced by 57%i i.e., initially, it was 75% and present scrap% is 18% 7. Conclusions The project work was completed successfully and the changes made wereimplemented withtheaidof IE tools such as Standard Operating procedure, Cause and effect diagram, process chart,simulation, and Design of Experiments they were able to minimize the scrap rate thereby increasing productivity. The results were shown for the production of the month of May 2023 and the results are tabulated. Implementation of an Excel-based Macro program resulted in the elimination of manual calculations and reduced scrap rate from 50% to 15%. Implementation of 3D-scanning in the processes of Green Carbide inspectionhasreducedthescrap rate from 30% to 15%. Implementation of DOE results, the scrap rate at grinding is decreased from 25% to 15% The productivity has been increased by 50000pcs per month by decreasing the scrap by 19% REFERENCES [1] Muhammad Ahmed Kalwar, “Automation of Order Costing Analysis by using Visual Basic for Applications in Microsoft Excel”, Journal of Applied Research in technology and Engineering, volume3(1),Jan31st,2022. [2] Abidin I. Z, Juahir H, Azid A, Mustafa A. D, & Azaman, “Application of Excel-VBA for computation of water quality index and air pollutant index”,MalaysianJournal of Analytical Sciences, 19(5), ISSN 1056–1064, (2015). [3] Ahmadi A, Robinson P. H, Elizondo F & Chilibroste P, “Implementation of CTR dairy model using the visual basic for application language of Microsoft Excel”, International Journal of Agricultural andEnvironmental Information Systems, 9(3), 74–86, (2018). [4] Yan Q & Wan Y, “Using the special fontandVBAprogram to make a bill of materials in the transmission line engineering”, 32(2), 335–341, (2017). [5] Belchior Junior A., Bruel R. N, Andrade D. A, Sabundjian, G, Macedo, L. A., Angelo, G., Torres, W. M., Umbehaun, P. E., & Conti, T. N, “Development of a VBA Macro-Based Spreadsheet Application for Relap5 Data Post- Processing”, International Nuclear Atlantic Conference, 978–985, (2011). [6] Muhammad Ali Khan, Automation Data Analysis using Excel, Journal of Applied Research in Received: September 26, 2021; Accepted: December 17, 2021; Published: January 31, 2022 [7] G. Salawu,” Performance optimization on waiting time using queuing theory in an advanced manufacturing environment”, south African Journal of Industrial Engineering, volume 31(4), pp 9-18, December 2020. [8] Ran,l., Xiaolei,” A survey on simulation optimization for the manufacturing system operation”, International Journal of Modeling and Simulation,volume38,pp -116- 122, 2017. [9] Sushil G, “A survey on Queuing System with Mathematical Models and Applications”, American Journal of Operational Research, Volume 7, NO.1, pp-1- 14, 2017. [10] Andreas Felsberger “A Review of Decision Support Systems for Manufacturing Systems”,SamI40workshop at i-KNOW ’16 October 18–19, 2016, Graz, Austria [11] Lessa D. J. R, Lessa F. P. R, Magalhães Junior P. A, & Guimarães H. V, “Mathematical Model andProgramming in VBA Excel for Package Calculation”, International Journal of Engineering Research and Applications, 6(5), 55-61, (2016).
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