th International & 26th All India Manufacturing Technology, Design and - PDF document

th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India
DECISION MAKING USING ANALYTICAL HIERARCHICAL PROCESS (AHP) FOR SELECTION OF BEST SUITABLE MAINTENANCE METHOD FOR MULTISPINDLE AUTOMAT AS32 Mr. Jayant S. Karajagikar, Department of Production Engineering and Industrial Management, College of Engineering, Pune-411005, E-mail: jsk.prod@coep.ac.in Dr. Bhagwan U. Sonawane, Department of Production Engineering and Industrial Management, College of Engineering, Pune-411005, E-mail: bus.prod@coep.ac.in ABSTRACT A different approach for carrying out criticality analysis and decision making for maintenance is adopted in this work, carried on an Automat which runs for twenty hours a day. Selecting the problem of choosing an optimum maintenance mode for machine/ equipment important function components, Reliability Centered Maintenance (RCM) theories and methods are used to evaluate the fault criticality of the important function components and confirm the estimating indexes for the maintenance modes and their weight scales, while AHP [1],[3] method is applied to establishing the hierarchy of the important function component maintenance decision-making. According to calculating and comparing the resultant weight scales of the maintenance modes for different important function components, the rational maintenance modes are ascertained and some important conclusions are found out, which provides a new thought and method for logical decision-making of good-sized complex equipment maintenance. In our case, HMT Gildemeister Multi-spindle Automat AS3 machine tool [2] is considered for the said analysis to find out the critical components and its maintenance planning. Keywords: AHP, Maintenance optimization Figure1 HMT Gildemeister Multispindle Automat AS32 1.Introduction The traditional support mode of equipment is passive, which are mainly made up of periodic Time-Based Preventive Maintenance and Break-Down Corrective Maintenance. There are several problems in this simplex support mode, such as that the accretion of the support scope brings on the notable increase of the life cycle cost, the lack of maintenance, and the overmuch of maintenance. As the rising of the fault diagnosis technology and the DECISION MAKING USING ANALYTICAL HIERARCHICAL PROCESS (AHP) FOR SELECTION OF BEST SUITABLE MAINTENANCE METHOD FOR MULTISPINDLE AUTOMAT AS32
information technology levels, Condition Based Maintenance (CBM) develops gradually, which makes the real time evaluating of the equipment technical states by means of the monitoring data so as to forecast the residual lives or the function faults of the equipments, and confirm the optimal maintenance scope and occasion on condition to improve the maintenance benefits. But it requires advanced diagnosis system and maintenance technology as sustentation. Therefore, nowadays diversiform equipment maintenance modes will all exist and develop for a period of time. It is paid attentions to that choose the apt maintenance mode to realize the maximal support benefit at the cost of the minimum resource expending. So RCM (Reliability Centered Maintenance) theories and AHP methods are applies to quantificational evaluating and optimizing in order to choose an optimum maintenance mode for equipment. 2.EVALUATION OF THE MACHINE FAULT CRITICALITY: According to RCM theory, the Spindle Bearings, Dimension setting Cu Bolts and the Toolings are selected as the important components of the equipment, whose fault criticalities will be evaluated. 2.1 Classification of Fault Inclemency Degrees: Fault inclemency degree classification is the measurement expression of the consequences caused by product faults, as Table 1 shows. Table 1 FAULT INCLEMENCY DEGREE CLASSIFICATIONS AND VALUES Tab Fault Inclemency Degree Classification Sorts Fault Consequence Description Value 1 Catastrophic fault Cause the personnel to die or the system to be destroyed 4 2 Critical fault Cause the personnel to be injured or the system to be damaged badly 3 3 Interim fault Cause the personnel to be injured or the system to be damaged lightly 2 4 Slight fault None of above 1 2.2 Description of Fault Probability Grades: There are 4 grades to describe the fault probability, as Table 2 shows. Table 2 FAULT PROBABILITY GRADES AND VALUES Tab Fault Probability Grade Quantitative Description Sorts Fault Probability Description Value 1 Usual The fault Probability is more than 20% of total faults. 4 2 Probable The fault Probability is between 10% and 20% of total faults. 3 3 Occasional The fault Probability is between 1% and 10% of total faults. 2 4 Infrequent The fault Probability is less than 1% of total faults. 1 2.3 Evaluation of Important Function Component Fault Criticality Degree: Fault criticality degree is the composite and quantitative index on both fault inclemency degree and fault probability. According to the data in Table I and Table II, the calculation results of fault criticality degree under all kinds of fault inclemency degrees and fault probabilities are as follows: the calculation results of fault criticality degree under all kinds of fault inclemency degrees and fault probabilities are as follows: [C] 4 8 12 16 3 6 9 12 2 4 6 8 1 2 3 4 According to the data in the matrix above, the fault criticality degree values of the three important function components chosen are presented as Table 3shows. 3. AHP OPTIMIZATION OF EQUIPMENT MAINTENANCE TACTIC Analytical hierarchical process is a multi-criteriadecision approach, introduced by Saaty and provides an effective way of properly quantifying the pertinent data, using a pair wise comparison between the parameters. The comparison is used to obtain weightage of importance as basis of decision criteria. Comparison must fall in admissible range of consistency, to prove the viability of the decision. This general approach of AHP consists of three stages[4]: 1.Determination of self-importance (presence) of attributes, th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India
2.Determination of relative importance of each alternative with respect to each attribute. 3.Overall priority weight determination of each of the alternatives. Intensities of importance, used for consideration of matrix have been selected on the basis of recommendation given by Saaty. 3.1 Calculation of the Evaluation Index Weight Scales for the Maintenance Modes: It is acquired that there are 3 main evaluation indexes of maintenance modes through the analysis of periodic Time-Based Preventive Maintenance (TBPM), Break-Down Corrective Maintenance (BDCM), and Condition Based Maintenance (CBM). 1) Validity of maintenance, reflecting the accomplishment degree of the maintenance mode for the equipment security and mission success; 2) Economy of maintenance, reflecting the degree of the maintenance mode for support resource expending; 3) Technology of maintenance, reflecting the technical difficulty degree of the maintenance mode for actualizing. According to RCM theory, the maintenance mode which costs less should be chosen so as to improve the support benefit under the precondition of equipment security and mission success. As a result, the evaluation indexes of diversified maintenance modes for important function components whose fault criticality degrees are different have dissimilar weight scales. In other words, the weight scales of the evaluation indexes are not fixed, but up to the fault criticality degrees of the components, which are showed as Table 4. Table 3 IMPORTANT FUNCTION COMPONENT FAULT CRITICALITY DEGREE EVALUATION HMT GilderMeister 6 Spindle Bar Automat Tab Fault Criticality Degree Values Components Fault Inclemency Degree Fault Probability Value 1 Spindle Bearings Interim fault 15% 6 2 Dimension setting Cu Bolts Interim fault 15% 6 3 Toolings Critical fault 50% 12 Table 4. WEIGHT SCALES OF THE EVALUATION INDEXES FOR MAINTENANCE MODES Tab Evaluation the Index weight scale values Components Criticality Validity Economy Tech. 1 Spindle Bearings 6 3 1 4 2 Dimension setting Cu Bolts 6 3 1 3 3 Toolings 12 6 1 2 3.2 AHP Optimizing Calculation of the Resultant Weight Scales of the Maintenance Modes According to the AHP method [5][6], the hierarchy of comparison with the maintenance modes for important function components is founded as shown in Figure 2. Figure 2 Hierarchy of Comparison with the maintenance modes for important function components. According to Figure 2, the comparison judgment matrixes from the scheme tier to the rule tier are founded as follows: TBPM BDCM CBM A B C A 1 5 1/2 B 1/5 1 1/5 C 2 5 1 (TECHNOLOGY) DECISION MAKING USING ANALYTICAL HIERARCHICAL PROCESS (AHP) FOR SELECTION OF BEST SUITABLE MAINTENANCE METHOD FOR MULTISPINDLE AUTOMAT AS32
TBPM BDCM CBM A B C A 1 3 1/3 B 1/3 1 1/5 C 3 5 1 (VALIDITY) TBPM BDCM CBM A B C A 1 1/2 1/3 B 5 1 1/5 C 3 5 1 ECONOMY) According to the weight scales of rule factors in Table 4, the comparison judgment matrixes from the rule tier to the aim tier are founded as follows: 1)Spindle Bearings: Technology Validity Economy A B C A 1 3 2 B 1/5 1 3 C 1/2 1/3 1 2)Dimension setting Cu Bolts: Technology Validity Economy A B C A 1 3 2 B 1/5 1 4 C 1/2 1/3 1 3)Toolings: Technology Validity Economy A B C A 1 6 2 B 1/6 1 1/2 C 1/2 2 1 With the application of AHP resultant weights are calculated which is illustrated in next section. 4. Result of AHP for optimized maintenance mode for all critical components: With the AHP methodology, the data in the Table 5, is presented as follows: The weight scale aggregate of maintenance modes for the Spindle bearings is {0.25651, 0.08776, 0.50714}. In the aggregate, 0.50714 is the value of 3 which stands for CBM, and it is larger than 0.25651 which stands for TBPM by 51%. The weight scale aggregate of maintenance modes for the Dimension setting Cu Bolts is {0.26477, 0.09223, 0.50714}. In the aggregate, 0.50714 is the value of 3 which stands for CBM, and it is larger than 0.26477 which stands for TBPM by 53%. The weight scale aggregate of maintenance modes for the Toolings is {0.28515, 0.09223, 0.52978}. In the aggregate, 0.52978 is the value of 3 which stands for CBM, and it is larger than 0.28515 which stands for TBPM by 54%. Table 5. THE CALCULATION OF THE COMBINATION WEIGHT VALUES Correspondingly, the maintenance tactic optimized for equipment important function components is as follows: 1) CBM should be adopted for spindle bearing maintenance. th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India
2) CBM could be adopted for Dimension setting Cu Bolts maintenance. 3) CBM could be used for Tooling maintenance. 5. Conclusion In order to find out critical components of a machine tool this work gives an all new approach which can be further utilized to optimize the maintenance tactic of equipment, the quantificational decision of selecting the maintenance mode for the important function components is made with CBM theory with the use of AHP method application which resulted in deciding the maintenance methodology. Greater benefits are resulted with the implementation of condition based maintenance instead of periodic maintenance for the Automat AS32. References 1.Saaty T.L.,(1980), The Analytical Hierarchy Process, McGraw-Hill, New York. 2.HMT Gildemeister Multi-spindle Automat AS3 machine manual. 3.Huiqi Zhang, Chunliang Chen, Zhan-Dong Bi, Junyan Liu, Yongqing Zhang, Research on RCM-AHP Based Armored Equipment Maintenance Tactic Optimizing, 978-1-4673-0788-8/12 ©2012 IEEE.4.Patrick Eagan, Laerence Weinberg, Application of AHP techniques to streamlined life-cycle analysis of two anodizing processes, VOL.33. NO.9 1999, Environmental science and Technology, American Chemical Society. 5.Omkarprasad S. Vaidya a, Sushil Kumar (2006), Analytic hierarchy process: An overview of applications, European Journal of Operational Research 169, pp.1–29. 6.M. A. Burhanuddin, Sami M. Halawani, A.R. Ahmad, & Zulkifli Tahir(Jul. 2011), Failure-based Maintenance Decision Support System using Analytical Hierarchical Process, International Journal of Advanced Computer Science, Vol. 1, No. 1, Pp. 1-9.