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Application Failure Mode Effect Analysis on 3D Printing Covid-19 Face Mask
Didit Damur Rochman
1, Riki Ridwan Margana
2, Asep Anwar
3, Rendiyatna Ferdian
4 1Universitas Widyatama Bandung, Indonesia2Universitas Widyatama Bandung, Indonesia 3Universitas Widyatama Bandung, Indonesia 4Universitas Widyatama Bandung, Indonesia
1diditdr@widyatama.ac.id, 2 riki.ridwan@widyatama.ac.id,3 asep.anwar@widyatama.ac.id, 4rendiyatna.ferdian@widyatama.ac.id
Article History: Received: 10 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published
online: 20 April 2021
Abstract: The condition of the Covid 19 virus outbreak in the world, especially in Indonesia in early 2021, is still experiencing an increase, although in terms of the percentage it has decreased compared to before. Health protocols according to WHO recommendations by using masks, washing hands with soap in water flow and social distancing are still implemented even though some people have given vaccine injections. Making masks made by 3 D printers in the Production Systems laboratory at Widyatama University to be distributed to the public still has defects in making these masks. FMEA can be used as a way to reduce product defects in mask production to prevent the Covid-19 virus by using a 3D printer. Each stage of the process must be evaluated and brainstormed to calculate the RPN value of each failure that will result in a defective product. The highest failures were printing stopping in the middle and reducing layer shift and developing a set of work procedures for printing personnel. After applying from FMEA, the number of defective products has decreased since the 8th day of production of Covid-19 masks with this 3D printer.
1. Introduction
Production defects are the most avoided things in manufacturing processes. So that minimizing production defects must be applied continuously through the PDCA process as part of continuous improvement. Sometimes defect identification is a simple thing to do, is considered easy by manufacturers and concentrates on reducing the quantity of defective products at a certain time and is not part of the learning, when the same defect reappears, the actions taken are inconsistent. The conditions of the Covid-19 pandemic have had a major impact on the manufacturing process in several small and medium business sectors. Especially in the production or manufacturing sector, consumer demand is decreasing, raw material difficulties, employee layoffs are factors that amplify efforts to comprehensively reduce defective products.
Planning to reduce production defects should be carried out when designing products such as by applying QFD (Cohen, 1995). But if the product already exists and is being produced, improving product design to reduce defects can be done using other traditional quality improvement tools such as the use of Failure Mode Effect Analysis (FMEA) (Stamatis, 2003).
The conditions of the Covid-19 pandemic require everyone to maintain social distancing, use masks and wash their hands frequently according to WHO recommendations. Maintaining a distance is a physical activity that does not require special tools, and washing your hands is sufficient with soap, but facial masks require special products. When WHO suggested using masks, the supply of masks for the needs of medical personnel was sold out by the public. Even though WHO recommends that the use of cloth masks is sufficient, the public interest for medical grade face masks remains high. Using a cloth mask is enough to reduce the transmission of the Covid-19 virus, it can be washed after use and used again. But the disadvantage of this type of mask is that the filtration rate is only up to 26% maximum, and does not cover the surface of the face completely.
The use of 3d-printed masks can reduce the transmission of covid-19 and meet the demand for medical grade face masks and keep the face mask supply chain for medical personnel uninterrupted (Duda et al, 2020). Selection of face masks for covid-19 that are 3d printed as suggested by Rochman et.al (2020) using AHP must consider printing speed, quantity and ease of cleaning. 3D printers can be obtained for as low as $ 200 using fused filament techn ology so that it is widely adopted by hobbyists, students, businesses and manufacturers.
Ownership of 3d printers in Indonesia is generally among academics and hobbyists for the FDM 3d printer type because of its affordable prices. Community service carried out by the Widyatama University production system laboratory, to assist the community in fulfilling a durable covid-19 face mask using a 3d printer. but in the
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process of producing the Covid-19 face mask, there are many product defects. This paper will discuss the use of FMEA in the production of covid-19 face masks using a 3D printer to reduce production defects.2. Method
FMEA is a method of evaluating the possibility of a failure of a system, design, process or service for handling steps (Stamatis, 2003). In FMEA, every possible failure that occurs is quantified to be prioritized for treatment. This FMEA research was conducted to see the risks that may occur in production operations.
Figure 1 FMEA Components
a. Frequency (occurrence)
Occurrence can be determined how much disruption can cause a failure in maintenance operations and plant operational activities.
b. Severity
The severity of this can be determined how serious the damage is caused by the failure of the process in terms of quality product, maintenance operations and plant operational activities.
c. Detection Level (detection)
This detection rate can be determined how the failure detected before it occurs. The detection rate can also be influenced by the number of controls that govern the process mesh. The more controls and procedures that regulate the maintenance operation handling system nets and plant operational activities, the higher the failure detection rate is expected.
The form of FMEA activities is not standardized, each company has its own form to reflect organizational interests and customer problems. The direction of each firm's value criteria reflects the interests of the organization, processes, products and customer needs. the steps for making FMEA (Stamatis, 2003) are as follows:
1. Review the process 2. Brainstrom potential waste
3. List the failure or defect, its causes and potential effects 4. Determine the level of severity
Table 1 Severity Rating
5. Determine the level of occurrence
FMEA
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Table 2 Occurence Rating6. Determining the detection level
Table 3 Detection Rating
7. Calculating the Risk Priority Number (RPN)
RPN is the result of multiplying severity (S), occurrence (O), and detection (D), where the mathematical equation can be expressed as follows:
RPN = (S) x (O) x (D) 8. List RPN to be followed up
9. Take action to reduce or eliminate high critical defect.
10.Calculate the RPN result as a defect or failure that will be reduced or eliminated. This step is carried out if the activity is to reduce critical defects.
The scope of FMEA's application in this paper starts from the Pre-Printing, Test Print and Production stages or mass printing.
Figure 2 Production and FMEA Process Scope
Pre-printing stage, loading the stereo lithography (STL) file on the SLIC3R software is carried out: • Optimization of the number of parts on the printer bed size.
• Optimization of printing parameters includes nozzle size, layer height, printing speed, nozzle temperature and cooling, based on the characteristics of the PETG material.
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• Printing simulation to see the possibility of part placement problems on the bed, as well as optimization of printing time.Print test stage is carried out printing for product samples and the real time in printing is measured and the possibility of post processing that needs to be done.
Printing stage is carried out after the results of the print test are in accordance with the design and there are no problems in the printing test process.
3. Result and Discussion
Production of COVID-19 masks in a production system laboratory using a filament fused 3D printer of the CoreXY and Gantry types. Production of 450 face masks in September 2019 used food grade PETG material and did not require a heated chamber as ABS plastic material. Production is carried out in 2 batches of 225 units per batch and each batch is produced within 15 working days.
After the test print and production stages on the 3rd day or after production of 45 units, defects can be identified during the production process (3d printing), a brainstorm is carried out between the labs lecturer and students. This product defect appears in the 3d printer setup process, printing and the materials used, the frequency of occurrence of defects, identified causes and impacts. The results of this brainstorm are in table 5.
The failure with the highest RPN of 192 was stop extruding mid print and layer shifting. Improvement actions that can be taken for both are basically preventive in nature as in table 4.But failure that causes defective products with high frequency is mid print stop extruding due to problems from electricity (suddenly the power voltage drops which often occurs in the rainy season) and filament runout. The problem of filament runs out because the size of the filament spool on the market is 1 kg, so every printing must be calculated for the filament needs and the remaining filament. Furthermore, not sticking to bed failure often occurs due to disciplinary problems of personnel when setting up the printer for printing. The lowest failure is the spotted rough surface because the humid filament contains a lot of water, so that when heated on the nozzle, moisture will appear and become air bubbles on the printout. This condition does not cause major but minor defects which can be overcome by surface finishing either by sanding and painting the printed results.
The action plan for each failure causing product defect is communicated and reminded at all times to printing personnel. In addition, the procedure is made in writing and distributed to all lab personnel or made notes in front of a 3D printer to remind the steps of this procedure. The results of this FMEA can reduce product defects starting on the 8th day of production of the 1st batch as presented in table 4.
Table 4 Daily Production Defect
Batch Days Production (Piece) Defect
1 1 15 4 2 15 2 3 15 1 4 15 3 5 15 4 6 15 2 7 15 3 8 15 1 9 15 2 10 15 1 11 15 1 12 15 1 13 15 1 14 15 2 15 15 1 16 15 1 2 1 15 2 2 15 1 3 15 2 4 15 1 5 15 1 6 15 1 7 15 1 8 15 1 9 15 1 10 15 3 11 15 1 12 15 1 13 15 1 14 15 1
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Table 5 FMEA Covid Face Mask 3D PrintingFailure Mode Effect Sev er ity Causes Occ u rr en ce Current Control Dete ctio n R PN Action Taken Filame nt break Produ ct defect 6 Moist filament 6 Hygromete r on the filament box Filament sensor 4 14 4 Make sure the silica gel in the filament box is always fresh and replace it if it has changed color Spotted rough surface Coarse threaded parts 2 Moist filament Temperatur e too high 4 Temperatur e sensor Hygromete r on the filament box 4 32 • Make
sure the silica gel in the filament box is always fresh and replace it if it has changed color • Resetin g temperature in slicer • Post processing by sanding Stop extruding mid print Produ ct defect 8 Filament Runout Electrical problem 6 Filament sensor 4 19 2 • Big spool filament > 1Kg using filament joiner • Periodi c visual checks by personnel during printing • Placing UPS Not sticking to bed Produ ct defect (Lift corner, completel y unstick) 8 Dirty printer bed 6 Visual 2 96 • Clean bed periodically using IPA (Iso Prophile Alcohol)
• Bed
temperature low • Apply glue stick to bed too thin Layer separation and splitting Produ ct defect 8 Moisture in room air 2 Room Hygrometer 2 32 • Rain season, air conditioning set to dehumidifier • Nozzle temperature too low Layer shifting Produ ct defect 8 Nozzle crash Loose printing part 4 Visual 6 19 2 • Periodi c visual checks by personnel during printing
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Skippedbelt
• Clean the printer bed
• Lower
print speed
4. Conclusion
FMEA can be used as a way to reduce product defects in the production of Covid-19 face masks by using a 3D printer. Each stage in the process must be evaluated and brainstormed to calculate the RPN value for each failure that will result in a defective product. The highest failure is that printing stops in the middle and layer shifting can be reduced and a set of work procedures developed for printing personnel. After applying from FMEA, the number of defective products per day decreased starting from the 8th day of production of Covid-19 masks with this 3D printer.
The next research is to evaluate the filament requirement before printing and compare it with the existing filament on the printer in real time, so that there will be no printing failure due to filament runout by combining the filament weight sensor on the 3D printer firmware.
5. Reference
1. Cohen, L. (1995). Quality Function Deployment: How to Make QFD Work for You. Britania Raya: Addison-Wesley.
2. Duda, S., Hartig, S., Hagner, K., Meyer, L., Intriago, P. W., Meyer, T., & Wessling, H. (2020). Potential risks of a widespread use of 3D printing for the manufacturing of face masks during the severe acute respiratory syndrome coronavirus 2 pandemic. Journal of 3d Printing in Medicine, 10.2217/3dp-2020-0014. https://doi.org/10.2217/3dp-2020-0014
3. Didit Damur Rochman, Asep Anwar, Riki Margana, Rendiyatna (2020), 3D PRINT COVID-19
MASK DESIGN SELECTION USING ANALYTICAL HIERARCHY PROCESS,
https://repository.widyatama.ac.id/xmlui/handle/123456789/12159
4. Jabarullah, N.H., Razavi, R., Hamid, M.Y., Yousif, Q. A. & Najafi, M. (2019) Potential of Ge-adopted Boron Nitride Nanotube as Catalyst for Sulfur Dioxide Oxidation, Protection of Metals and Physical
Chemistry of Surfaces, 55 (4), 671-676.
5. Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution. Jerman: ASQ Quality Press.
6. Zhang, B., Jabarullah, N. H., Alkaim, A. F., Danshina, S., Krasnopevtseva, I. V., Zheng, Y., & Geetha, N. (2021). Thermomechanical fatigue lifetime evaluation of solder joints in power semiconductors using a novel energy based modeling. Soldering & Surface Mount Technology, https://doi.org/10.1108/SSMT-06-2020-0028.
