Prioritization of Failure Modes in Process FMEA using

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dealing with this issue is the failure mode and effect analysis FMEA Wang 1996 A. failure mode and effects analysis can be described as a systematic way of identifying failure. modes of a system item or function and evaluating the effects before they occur. 1 2 FMEA METHOD, Failure mode and effect analysis FMEA is a structured bottom up approach that starts with. known potential failure modes at one level and investigates the effect on the next subsystem. level Wang et al 1996 FMEA as a formal design methodology was developed at. Grumman Aircraft Corporation in the 1950 and 60s Coutinho 1964 and was applied to. naval aircraft flight control systems Since then it has been extensively used as a powerful. tool for safety and reliability analysis of products and processes in a wide range of industries. particularly aerospace nuclear and automotive industries Gilchrist 1993 Connor 2001. Eblieng 2000 In 1977 it was adopted and promoted by Ford Motor Company The Ford. procedure extended FMEA methodology in automotive sector to assess and prioritize. potential process and design related failures FMEA is a widely used quality improvement. and risk assessment tool in manufacturing industry This method combines the human. knowledge and experience to identify known or potential failure modes of a product or. process By evaluating the failures of a product or process and their effects FMEA team. could initiate corrective actions or preventive measures as soon as possible to eliminate or. reduce the chance of the failures occurring Shortly speaking FMEA is a useful technique to. identify 1 the potential failure modes of a product or process 2 the effects of these. failures and 3 the criticality of these failure effects in the performance of a product or. Failure mode and effects analysis FMEA is a widely used engineering technique for. defining identifying and eliminating potential failures and so on from system design. process before they reach the customer Stamatis 1995 FMEA seeks for answer for. questions like what could go wrong with the system or process involved in creating the. system how badly might it go wrong and what needs to be done to prevent failures The. purposes of FMEA are as follows, Identify potential design and process related failure modes Ideally the design or. process can changed to remove potential problems in the early stages of development. Pries 1998, Find the effects of the failure modes FMEA allows a team to analyse the effect of. each failure, Find the root causes of the failure An FMEA is designed to find the sources of the. failures of a system, Prioritise recommended actions using the risk priority number The risk priority.
number is computed using the probability of occurrence of the failure mode the. severity of the effect of the failure mode and the probability of detection of the failure. mode through manufacturing, Identify implement and document the recommended actions. The first step in performing FMEA to analytical analysis is identification of potential failure. modes These failure modes are listed and then scored based on three aspects of the failure. modes occurrence O detection D and severity S Traditionally this FMEA scoring is. done by assigning discrete values to each of the items on a predefined scale for example. from 1 to 5 or 1 to 10 Ying Ming Wang 2009 Risk priority number RPN is the product of. the severity occurrence and detection ratings And the criticality of each failure mode can. be generated by the calculation of RPN The failure having a higher RPN will have a higher. priority for corrective action or preventive measure. Risk priority number RPN S O D,1 3 Drawbacks of Traditional FMEA Approach. The main objective of FMEA is to discover and prioritize the potential failure modes by. computing respective RPN Even today RPN evaluation with FMEA is probably the most. popular reliability and failure analysis technique for products and processes Rajiv Kumar. Sharma 2005 One of the major reasons for this success is due to its visibility and easiness. Unfortunately several problems are associated with its practical implementation in real. industrial situations,The critical disadvantages include. In RPN analysis various sets of S O and D may produce an identical value however. the risk implication may be totally different Anish Sachdeva 2012. The relative importance among the three parameter ratings. The difference of risk representations between the failure modes having the same. RPN Rajiv Kumar Sharma 2005, Consider two different examples having values of S 2 O 5 D 5 and S 1 O 10 D 5. Both these events will have a total RPN 50 however the risk factor of these two events may. not necessarily be the same which may result in high risk events going unnoticed The other. drawback of the RPN ranking method is that it neglects the relative importance among S O. and D The three factors are assumed to have the same importance but in real practical. applications the relative importance among the factors exists In another example say S 2. O 10 and D 5 may have a lower RPN 100 than one with all parameters moderate say S 6. O 6 and D 6 with RPN 216 There is high difference in RPN of both the events though it. should require a higher priority for corrective action in first event. There are significant efforts have been made in FMEA to overcome the shortcomings of the. traditional RPN Wang 2009 Most notably fuzzy theory with fuzzy If then rule base have. been suggested in the literature to overcome the drawbacks The studies about FMEA. considering fuzzy approach use the experts who describe the risk factors O S and D by using. the fuzzy linguistic terms Bowles Pelaez 1995 Chin 2008 Guimaraes Lapa 2004. 2007 Pillay Wang 2003 Sharma 2005 Tay Lim 2006,2 Fuzzy Methodology.
Zadeh 1965 proposed the fuzzy set theory which is an important concept to deal with. uncertainty based information The parameters i e Severity S Occurrence O and. Detection D which are used in FMEA are fuzzified using appropriate membership functions. Chang 1996 Fuzzy system is a knowledge based system which is constructed from. expertise and experience in the form of fuzzy IF THEN rules Tay Lim 2006 Through. building knowledge based model expert knowledge and judgment can be utilized to make. the FMEA assessment method more reasonable and convenient The fuzzy conclusion is then. defuzzified to get risk priority number The main components associated with fuzzy are. Fuzzification,Fuzzy rule base,Defuzzification,2 1 Fuzzification. Fuzzification refers to transformation of crisp inputs into a membership degree which. expresses how well the input belongs to the linguistically defined terms Rajiv Kumar. Sharma 2005 Experts judgement and experience can be used for define degree of. membership function for a particular variable During Fuzzification a fuzzy logic controller. receives input data also known as the fuzzy variable and analyzes it according to user. defined charts called membership functions Klir and Yuan 1995. 2 2 Fuzzy rule base, The rule base describes the criticality level of the system for each combination of input. variables Often expressed in If Then they are formulated in linguistic terms using two. approaches i Expert knowledge and expertise ii Fuzzy model of the process. Zimmermann 1996 Experts judgement and experience can be used for define degree of. membership function for a particular variable,2 3 Defuzzification. The defuzzification process examines all of the rule outcomes after they have been logically. added and then computes a value that will be the final output of the fuzzy controller During. defuzzification the controller converts the fuzzy output into a real life data value Rajiv. Kumar Sharma 2005,3 Fuzzy FMEA for Forging Shop, A case study was carried in one of the forging plant where automotive parts were forged and. heat treated Expert s judgment and knowledge is taken in making the model using three. input parameters Severity of failure S Frequency of Occurrence of failure O and Non. detection of failure D An If Then rule base is generated using fuzzy inference engine FIS. which after defuzzification generates the fuzzy risk output number FRPN The Fuzzy. Linguistic assessment model was developed using toolbox platform of MATLAB 7 0. Forging shop has various operations for which various failure modes and effects were. collected by using expert s judgement and knowledge database. Table1 shows the combined list of,1 Functions being performed in the shop.
2 Failure that may happen,3 Potential effects of failures. 4 Potential causes of failures, To find out all these failure causes help is being taken from expertises which includes. Product and process engineer quality engineer operation and maintenance department. Failure effect and its causes are produced by several years experiences of concerned. department Total nine functions have been performed in a shop and with deep analysis 36. failure causes have been detected which may happen at manufacturing stage and cause failure. of component As shown in Table 1 these failures are expressed as F. TABLE 1 COMPONENT FUNCTIONS AND THEIR FALIURES IN FORGING SHOP. S n EFFECTS OF POTENTIAL CAUSES OF,o FUNCTION FAILURE FAILURE FAILURE. Initial Wrong Setting by operator,Weight less Shifting of stopper during. than specified Unfilling operation F2,1 Shearing,Initial Wrong Setting by operator.
Weight more Shifting of stopper during,than specified Component Over Size operator F4. More Soaking time F5,Less Thickness of Operator Missed to reduce. Comp air fuel input F6,More Soaking time F7,Operator Missed to reduce. Unfilled forging air fuel input F8,2 Heating More Soaking time F9. Temp More Surface Crack Operator Missed to reduce,than required Generation air fuel input F10.
Less Soaking Time F11,More thickness,Less Soaking Time F12. Unfilled Forging,Temp Less Surface Crack,Less Soaking Time F13. than required Generation,Surface Crack,More Strokes Per Pc F14. Generation,More Length Die unfilled More Strokes Per Pc F15. less Length Die unfilled Less Strokes Per Pc F16,Uneven strokes F17.
Surface Surface Crack,3 Swaging Play in Slides F22. Surface Development of cracks Die mismatch F23,Overlapping at Normalizing. Unfilling Less Strokes during swaging,Minor Difficult Machining F24. Hot Pcs Striking with another,Surface Dents Difficult Polishing pcs F25. S n POTENTIAL CAUSES OF,FUNCTION FAILURE EFFECTS OF.
Initial Die Setting Problem F18,Major Die Shift During Production. Mismatch uneven trimming F19,Initial Die Setting problem F20. Minor Die Shift During Production,4 Forging Mismatch Difficult Machining F21. Surface Development of cracks Play in Slides F22,Overlapping at Normalizing Die mismatch F23. Unfilling Less Strokes during swaging,Minor Difficult Machining F24.
Hot Pcs Striking with another,Surface Dents Difficult Polishing pcs F25. Dimensions of Die Wear F26,workpiece is, more as per Difficult fitting during Less no of strokes F27. 5 Trimming drawing assembly,Dimensions of,workpieces. Die Wear F28,less as per Loose fitting during,drawing assembly. Height more Rejection on subsequent Initial Die Setting problem F29. 6 Coining than required operation,Less Soaking Time F30.
Hardness more Less Normalising Temperature,than required Difficult Machining F31. 7 Normalising,More soaking time F32,Hardness less More Normalising Temperature. than required Functional failure F33, Excessive Rejection on subsequent Excessive pressure applied by. Grinding operation operator F34,8 Grinding, Grinding Less Rejection on subsequent Less pressure applied by operator. than required operation F35,Scale Not Less Barrelling time F36.
9 Barrelling removed Poor appearance, Table 2 shows the basic data from which fuzzy rules have been made Three factors have. been considered that includes severity of failure S frequency of occurrence O and. chance of non detection of failure D According to the degree of the seriousness all factors. are rated on a 0 to 10 scale This data has been evaluated in fuzzy inference engine FIS and. If Then rules prepared accordingly In Table 2 traditional method of FMEA has been used. by multiplying all the three input variables and RPN number is generated. TABLE 2 COMPARISION BETWEEN TRADITIONAL RPN AND FUZZY RPN RANKING. Failures Severity Occurrence Non Detection RPN,F1 6 4 4 96. F2 6 4 10 240,F3 4 4 5 80,F4 4 4 10 160,F5 8 4 8 256. F6 8 4 8 256,F7 8 4 8 256,F8 8 4 8 256,Heating F9 10 4 8 320. F10 10 4 8 320,F11 4 2 8 64,F12 4 2 8 64,F13 10 2 8 160.
F14 10 2 8 160,F15 8 2 8 128,F16 8 2 8 128,F17 10 2 8 160. F18 8 2 8 128,F19 8 2 8 128,F20 4 2 8 64,F21 4 2 8 64. F22 10 2 8 160,F23 10 2 8 160,F24 10 2 8 160,F25 4 2 8 64. F26 4 2 6 48,F27 4 2 6 48,F28 8 2 6 96,Coining F29 8 4 8 256. F30 10 8 8 640,F31 10 8 8 640,Normalising,F32 10 8 8 640.
F33 10 8 8 640,F34 4 2 6 48,F35 4 2 6 48,Barrelling F36 6 2 8 96. 3 1 Modeling of the fuzzy logic base FMEA, Three factors have been selected as the input parameters for our fuzzy system which is being. evaluated in well defined IF THEN rules prepared in MATLAB Fuzzy logic toolbox Experts. Prioritization of Failure Modes in Process FMEA using Fuzzy Logic Vikramjit Singha Harish Pungotrab Sarabjeet Singhb Simranpreet Singh Gillb a Department of Mechanical Engineering Lovely Professional University Phagwara Punjab India

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