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Statistical process control in machining, a case study for machine tool capability and process capability
Materials & Design Materials and Design 27 (2006) 364–372 www.elsevier.com/locate/matdes
Statistical process control in machining, a case study for machine tool capability and process capability Ali Rıza Motorcu, Abdulkadir Gu¨llu¨
*
Department of Mechanical Education, Technical Education Faculty, Gazi University, 06500 Teknikokullar/Ankara, Turkey Received 12 July 2004; accepted 9 November 2004 Available online 10 December 2004
Abstract In this experimental study some statistical calculations have been made to eliminate quality problems such as undesirable tolerance limits and out of circularity of spherodial cast iron parts during machining. X–R control charts have been constructed on the data obtained from this manufacturing to discover and correct assignable causes, so that the machine capability (Cp) and the process capability (Cpk) can be determined. In order to compare design tolerance on working drawings and attained tolerances on workpieces after machining five mass production lines were set up in a medium sized company. The results obtained from five X–R control charts and the data gathered from all production lines were processed and evaluated. At this stage of the study, it was observed that some parts were oval and out of tolerance limits, machines and processes were insufficient and production was instable. Through machining data and follow up studies some assignable causes for faulty workpieces were discovered, and ovalness and out of tolerance limits problems were eliminated. In addition to these developments, surface roughness of machined workpieces was improved. All these activities show that in small or medium sized companies statistical quality control can be useful component of production provided that sufficient finance and qualified personal are utilized. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Statistical quality control; Machine tool capability and process capability; Machinability; Cutting parameters
1. Introduction High quality production provides some advantages such as reduced scrap or remachining and increased market share. For this purpose there are some requirements to be met. First of all the organization should be cooperative and the quality should come first. On the other hand, in order to meet quality requirements of final product, quality should be achieved at every stage of production [1]. Another way of achieving good quality during production is to use the statistical period techniques at *
Corresponding author. E-mail addresses: [email protected] (A.R. Motorcu), agullu@ gazi.edu.tr (A. Gu¨llu¨). 0261-3069/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.matdes.2004.11.003
every stage of production. If the production is statistically under control the process can continue and there is no need for a change in the process. However, if it is not statistically under control, the assignable causes should be discovered and removed from the process. Statistical quality control methods apply statistical principles and techniques at every stage of design, manufacturing, and servicing. Statistical quality control methods are quite different from traditional methods and they have made great contribution to improvements in companies dealing with mass production. In traditional methods, the product is manufactured first and then it is checked to determine whether it meets the quality requirements. The product that does not meet the quality requirements is rejected and sent back to the machines for remachining or correction otherwise it is
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thrown away as scrap. If faulty products are too much, in order to eliminate the assignable causes or the problem necessary corrections are made by examining production period (Fig. 1a). However, statistical quality control is the vital part of production. Instead of checking the finished product after production, it is applied at every period of production. If this period is under control, the next period is considered, otherwise the assignable causes are discovered and corrected (Fig. 1b). Today, in order to preserve and improve quality companies perform their work in three phases: firm organization phase, process phase and application/ performance phase [2]. The priority orders of information and data in these phases are control, diagnosis, and planning respectively. Determination of how this information is distributed and equalized on organizational level is of vital importance for the success of company [2]. Quality improvement processes and calculations are generally carried out at design stage or at work stages. At these stages the standards for the product are quality oriented and customer requirements are taken into account. But at organizational level, the tasks and the procedures at process level should also be organized together. The standards desired for the product may have some criteria such as exact size, material composition, and production time. All employees should understand the necessity of quality and employ required techniques at their daily works [2]. Even tough quality is a must in almost every company, it receives limited attention by managements. Some firms use traditional methods and some of them prefer statistical quality control methods. There are plenty of research work concerning quality improvement. Control process and quality management philosophy was defined according to quality requirements by Jabnoun [3] and it was pointed out that not enough
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attention for quality was paid by managements. For accepting the manufactured parts as identical in mass production, it is enough to try to manufacture parts according to working drawings and tolerances. Because sizes and tolerances obtained after manufacturing make them identical rather than the dimensions and tolerances on working drawings. For this reason, when unbiased decisions about a production period is needed statistical techniques based on unbiased information obtained from product or process are used. Control charts, process capability definitions and design of experiments have been used for years. There is considerable theoretical and experimental research work for improving product quality and processes using statistical techniques. Xie and Goh [4] discussed statistical techniques and their roles for process development considering recent research works and they summarized design techniques by giving some examples. They focused on statistical techniques used for improving quality continuously. In another work, the application of statistical process control in a firm manufacturing chemical and plastic products and its usage was discussed. Focusing on outer necessary factors, statistical process control application was realized. Optimization of processes which is one of the important part of statistical process control was discussed in manufacturing activities and the success of statistical process control was evaluated [5].
2. Process control definitions: machine tool capability (Cp) and process capability (Cpk) Definitions of process control are used to establish qualified measurements for potential and performance of process in industry which are elements of capacity [6]. Capacity analysis is made by using a data set in statistical calculations for defining the systemÕs capability.
First period step Production Period SPC applications
Adjust the period Product
Is it under control?
No Does it meet the specifications?
Scrap or remachining
Scrap or remachining
No Yes
Discover causes
Customer
(a)
Yes
(b)
Eliminate causes
Fig. 1. Quality control methods: (a) traditional quality control method; (b) statistical quality control method.
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In order to define the system capability, the values are compared. If the product is approximately 100% in tolerance limits, it can be said that the system is ‘‘capable’’. The tolerance limits are determined by customers, engineers and management and they are classified as requirements, aims, specifications and standards. There should be lower and upper limits of specification restrictions for the definition of the system. It is accepted that the data should distribute normally for making capacity analysis calculations. A histogram is plotted to see if the data distribute normally or not. Before making capacity analysis, control charts are plotted on the data gathered from the system to see the system stability. Traditional applications of control charts are used to discover the points exceeding the tolerance limits of the part. In modern production systems, since products are generally inspected automatically, data obtained during the use of traditional charts is not suitable for a specific dimension of sample parts. Alongside this the sample size should be selected larger. To make a clear decision about the capability of a production line, enough number of sample parts should be manufactured and inspected [7]. Capability analysis helps to determine the ability for manufacturing parts in the tolerance limits and engineering values. Capability analysis can be applied not only to a manufacturing period but also to a machine tool [1]. Capability analysis gives the information about the system development during the period. Machine tool capability (Cp) and process capability (Cpk) are used to determine the efficiency. Cp is used to determine the systemÕs location in tolerance limits. The size of deviations from the average value of process dimensions will indicate how well the production is. If the system is not at the center of specification values, the trend of Cp is progressing faultily. Cpk is used to determine the average so that the system will works better in the specification limits. If the value of Cpk is 1 it shows that the manufacturing is going on in the system specification limits staying at 99.73% level (±3r limits). If the system centralized at the target value, Cp and Cpk values will be aqual. When the values of Cp and Cpk is 1, this is considered, as the minimum requirement of the system for some companies. Alongside this, larger Cp and Cpk values, for instance 2, are accepted by many companies. Cp and Cpk are defined by the following equations [8]: Range d2 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 ðX X ave Þ SD2 ¼ n1
SD1 ¼
Cp ¼
UTL LTL 6:SD1
ð1Þ
ð2Þ
ð3Þ
Cpk ¼
UTL LTL 6:SD2
ð4Þ
where SD1 guessed standard deviation Range tolerance, the difference between upper and lower tolerance limits d2 center line factor used for calculation of 3r control limits SD2 experimental standard deviation X measured size Xave average of measured sizes n number of measurements UTL upper tolerance limit LTL lower tolerance limit The value of d2 was taken as 3.472 for n = 15 [9]. Capability helps to reach the target values which are important for customers. If a product deviates from the target values, it means the product specifications do not meet the requirements. This causes increase in costs and decrease in sales. An experienced team is required for capability analysis and this team tries to improve it. They collect necessary information and data, and they develop theories and do necessary calculations [10]. Small and medium sized companies must make correct decisions and develop more efficient processes in order to survive in the competitive market. Thereby, correct understanding of the components of variables, definition of factors causing variations and keeping them under control are all important for small sized companies [10,11]. Develeyd et al., presented two papers on application of statistical methods. First paper is on the capability analysis in Swedish industry and the second one is on statistical quality control in two small companies of ceramics industry which do not have large production capacities. These cases shown that efficiencies of some medium sized companies may be increased by applying the statistical methods developed [11]. 3. Statistical process control in machining the cast parts, a case study on Cp and Cpk analysis With this study, eliminating the quality problems of work pieces during machining in a medium sized company comprising a casting and machining work shop was aimed. The process was required to reevaluate because of some problems accrued during assembly and after quality control of products in the company where the assembly of the products was carried out. Obtaining the permission and support of the management a team was gathered, the data was collected and analyzed and the reason for problems investigated [12]. The workpiece is the part of a construction machine. It was cast as spheroidal cast iron and machined using various chip removing operations namely rough and
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finish turning and drilling operations. First turning operations are applied on the sizes of 62.500–62.250, 56.410–56.490 and 63.512–63.550 must be machined within the specification limits and must be under control. The size, tolerance values and surface roughness values of the sample part are shown in Fig. 2. After having examined the faulty parts, it was discovered that there were some problems with the sizes of 62.500–62.250, 56.410–56.490 and 63.512–63.550; some problems with geometric tolerances and some problems with the surface finish. In other words, there were some parts out of tolerance limits, some parts out of circularity (ovalness), some parts with poor surface finish. First the process in the foundry where the parts were cast was examined. Then, in the machining work shop where the quality problem had not been able to solved, the machining problems were identified by doing statistical process control during production. Using the lot acceptance sampling plan, a single-sampling plan in statistical study, it was accepted that 10% of Plot size of 600–750 parts sent to the partner company which represents the whole lot. 75 sample parts were taken from each lot and the accepted quality level was determined as 5%. Samples were taken from five
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production lines where the same amount of products were manufactured in every hour. In order to plot X– R control graphics and to do process capability analysis, statistical parameters were calculated using the measurements values taken from the samples that represent the whole process [9,11]. For one size, dimension distribution of the products manufactured in each production line affects the average of the whole process in a different way. Thereby, carrying out statistical work for each production line, normal distribution diagrams and histograms were prepared and how these affect the whole process was investigated. In Table 1, for the production line 1 statistical study results of 62.500–62.250, 56.410–56.490 and 63.512– 63.550 sizes are given. In Fig. 3, for the production line 1 histograms for 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes are presented. In the production line 1, 62.500–62.250 size the average was calculated as 62.270 and it was determined that normal distribution curve was located in the middle (Table 1, Fig. 3). Staying of the average value within the tolerance limits indicates that no faulty product is manufactured. However, for this production line it can not be said that the manufacturing is
DETAIL J
A
DETAIL C
N7
5˚
DETAIL J
A
0.08 F
Ø5 9.5
N9
0.05 D 0.80x 45˚
DETAIL C DE TA Y C
0.05 D N8
0.08 F
D
Fig. 2. Technical drawing of the sample part.
Ø6 6.7
N8
F
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Table 1 For the production line 1, statistical calculation results for 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes Sample no.
62.500–62.250
56.410–56.490
63.512–63.550
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15
62.270 62.280 62.255 62.255 62.275 62.265 62.280 62.280 62.265 62.265 62.275 62.265 62.280 62.265 62.270
56.450 56.450 56.430 56.450 56.440 56.445 56.435 56.450 56.435 56.450 56.445 56.450 56.440 56.450 56.435
63.540 63.540 63.540 63.530 63.540 63.530 63.540 63.535 63.540 63.540 63.540 63.535 63.540 63.540 63.540
Average SD1 SD2 Range Cp Cpk
62.500–62.250
56.410–56.490
63.512–63.550
62.270 0.0072 0.0085 0.0250 5.7870 4.8735
56.444 0.0057 0.0071 0.0200 2.3148 1.8549
63.538 0.0028 0.0036 0.0100 2.1990 1.7191
12
8
6
10
5 4 3
Frequency
Frequency
Frequency
6
4
8 6 4
2 2
2
1
0
0
0 62.255 62.265 62.270 62.275 62.280
56.430 56.435 56.440 56.445
56.450
63.530
63.535
63.540
Fig. 3. For the production line 1, histograms of 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes.
continuing normally. The averages of 56.410–56.490 and 63.512–63.550 sizes stays within the tolerance limits and it can be said that there is no faulty production for these sizes. The results of statistical calculations of the process with data obtained from all production lines and process capability values are listed in Table 2. Since the gap beTable 2 Statistical results of the process Statistical parameters
62.500–62.250
56.410–56.490
63.512–63.550
Average SD1 SD2 Range LSL USL Cp Cpk LCL (average) UCL (average) LCL (range) UCL (range)
62.2470 0.00576 0.01998 0.0200 62.500 62.250 7.2337 2.0851 62.2517 62.2427 0.0330 0.0070
56.4367 0.00748 0.00924 0.0260 56.490 56.410 1.7825 1.4422 56.4425 56.4309 0.0430 0.0090
63.5390 0.00403 0.00569 0.0140 63.550 63.512 1.5710 1.1124 63.5421 63.5359 0.0231 0.0049
tween the upper tolerance values and lower tolerance values in three sizes is large, the values of machine tool and process capability were desired to be larger than 2 (Cp, Cpk > 2) [12]. Since in the production line 1, for 62.500–62.250 Cp = 5.7870 > 2 and Cpk = 4.8732 > 2 the machine tool and process is sufficient (Table 1). In the same production line during machining of 56.410–56.490 and 63.512–63.550 sizes while the machine capability was accomplished since Cp was greater than 2, it was determined that the process capability was not achieved because Cpk was less than 2. The process stability was observed in this production line after evaluated of capability data obtained from statistical calculations of production line 1. These obtained values showed that having reevaluated the process, process parameters were required to be redetermined. When examining of Table 2 for other two sizes, except for 62.500–62.250 size, both machine tool and process capability were accomplished. On the other hand for two sizes of 62.500–62.250, process capability value Cpk approached to the limit value. When compared the Cp and Cpk values of production
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63.550 for the whole process were presented. In Fig. 3 for 62.500–62.250 sizes, while size dimensions were accumulated near to the lower tolerance value and no production was made out of tolerance in production line 1, Fig. 4 for the same size the normal distribution curve was concentrated near to the lower tolerance value and it can be seen that the production was made in high frequency out of lower tolerance. Being high of size frequency manufactured out of tolerance shows that there is a problem at least in one or more production lines. When examining Fig. 4, it can be seen that no production was made out of tolerance
line 1 in Table 1 and the Cp and Cpk values of the process in Table 2 while the machine tool capability of all sized in production line 1 was achieved, in the whole process, accept for 62.500–62.250 size, any machine tool and process capability was not achieved. It can be seen that at least one or more production lines affect the machine and process capability badly. It was observed that the production deviated from the aimed values and the production was not continued stably after doing machine and process capability work. In Fig. 4, the histograms and normal distribution curves of 62.500–62.250, 56.410–56.490 and 63.512–
20
20
40
18
18
35
16
16
14
14
10
Frequency
Frequency
12
12 10
25 20
8
8
6
6
4
4
2
2
5
0
0
0
15
Fig. 4. The histograms of 62.500–62. 250, 56.410–56.490 and 63.512–63.550 sizes for the whole process.
Fig. 5. The X–R graphics and normal distribution curve for the 62.500–62.250 size.
63.550
63.540
63.535
63.530
63.525
63.520
56.450
56.445
56.440
56.435
56.430
56.425
56.415
56.420
56.410
10
62.220 62.225 62.230 62.235 62.240 62.245 62.250 62.255 62.260 62.265 62.270 62.275 62.280 62.285 62.290 62.295 62.300 62.305 62.310 62.315 62.320 62.325 62.330
Frequency
30
370
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for 56.410–56.490 and 63.512–63.550 sizes. The normal distribution curves and the frequency were concentrated near average values for both sizes. Using the data obtained from the statistical calculations in Table 2, X–R graphics charts were prepared for each size and normal distribution curves were plotted. In the X–R control graphic shown in Fig. 5 while the averages study near the control limits in the production line 2 and 3 of the sample groups, the averages stay out of the control limits in the production lines 1, 4 and 5. Due to making production out of the control limits it was necessary to stop the production and rearrange the manufacturing and machine tool parameters by evaluating the process completely. As it can be seen from the normal distribution curve in Fig. 5 the average of averages Xave is smaller than the lower value (Xave = 62.247 < 62.500). Thereby it can be said that most of the products manufactured are faulty. When examining the X–R charts of 56.410–56.490 size in Fig. 6, it is clearly seen that the averages of the production line 1 and 3 are out of the control limits and the average of the production line 2 approached to the control limits. Whereas in the production line 4 and 5 since the averages came close to the average of averages, the production continuous normally. The production line 1, 2 and 3 must be stopped, examined and the necessary arrangements must be made. In Fig. 7 for 63.512–63.550 size while the production is made out of the control limits in the production line 1, 2 and 3, the production is carried out normally in the
production line 4 and 5 according to the X control graphic. Since the production is made out of the control limits, the production lines must be stopped, machine tool and machining parameters reexamined and the necessary arrangements must be made. Since the values of the average of averages for 56.410–56.490 and 63.512–63.550 sizes accumulated between the tolerance values of the two sizes faulty products were not manufactured however, it can be said that there is a tendency that these sizes can be manufactured faulty in some production lines.
4. Machinability parameters and rearranging the process As a result of the statistical studies, it was necessary to stop the process, reexamine the process parameters and rearrange the process in order to reassure machine tool and process stability. Workpiece material, machine tool and cutting parameters which are three important components of machinability were reevaluated. First of all the structure of the workpiece material were examined. Since the casting and machining of the part are carried out in different work shops in the firm, having done arrangements in the casting work shop, it was determined that there existed shell hardening of the outer surface of the part because of dismantling of casting dies before the necessary waiting time. Casting die sets were dismantled after the
Fig. 6. The X–R graphics and normal distribution curve for the 56.410–56.490 size.
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Fig. 7. The X–R graphics and normal distribution curve for the 63.512–63.550 size.
required time in order not to reduce the tool life and therefore not to increase the tool costs because of this excessive shell hardening. After the statistical studies carried out during manufacturing in the production workshop, the parameters of two main components of machinability were investigated in order to prevent manufacturing out of tolerances and ovality and to obtain the ideal surface roughness. In order to prevent the ovality, the settings of the center line of the chuck and tail stock of the CNC machine tool and chuck jaws were examined. Having redetermined the work piece reference points on the CNC machine tool, the center setting of the machine tool were readjusted. It was determined that inappropriate cutting speeds and feed rate values, hard shell and sandy areas on the outer surface of the part reduced the cutting tool life. It was also determined that early fractures of cutting tools were occurred during machining and different from the sizes programmed in CNC part programs were obtained because of the insert corner wear. Since tool wear was not determined dynamically, size dimensions were determined as larger than they should be after a number of parts manufactured. Since the tool wear was not measured and the tool life was not determined correctly, the production out of tolerance values were occurred. In order to prevent
this, the cutting parameters in the CNC part program namely cutting speed, feed rate and cutting depth were examined [13]. Taking into account of insert nose radius values of CCGT09T304, HC2DNGA 443TN and CCGT09T304 used for machining 62.500–62.250, 56.410– 56.490 and 63.512–63.550 sizes, (0.4 mm, 1.2 mm and 0.4 mm respectively) and the desired surface roughness values (N8 = 0.16 lm, N7 = 0.8 lm and N8 = 0.16 lm respectively), the feed rate values were redetermined as 0.130 mm/rev, 0.163 mm/rev and 0.130 mm/rev respectively [13]. When machining the spheroidal cast part, 100 mm/ min cutting speed were selected and spindle speeds were entered to the CNC part program as 505 rev/ min, 568 rev/min and 497 rev/min for machining 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes respectively [13]. In order to prevent excessive tool wear, cracks and fractures when machining the outer shell of the part, the rough cutting depth of the first pass was increased and the damages generated by intermittent impacts and sandy areas to the tool nose and cutting edge minimized. After entering the new calculated parameters into the CNC program, it was determined that how much the cutting tool wore and the tool life expired after how
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many parts. According to the criteria determined, the operator was told to change the insert before itÕs tool life. After rearranging the machining parameters, the production was resumed, it was investigated that how the new values had affected the process and the statistical studies were accomplished. As a result of the studies the parts were manufactured within the tolerance limits and ovality and surface roughness problems were eliminated. These results were observed by measuring and controlling the parts manufactured.
5. Results and discussion Statistical quality control is applying statistical rules and techniques to each phase of design, manufacturing and service. One of the techniques for assuring the quality during production is to apply the statistical period control techniques to each phase of production during or after manufacturing. Statistical quality control have had great deal of improvements in companies using mass production. Capability analysis helps to determine the ability for manufacturing between tolerance limits and engineering specifications. Capability analysis can be applied not only to production period but also to a machine or machine tool. Capability analysis gives the information about changes and tendencies of the system during production. It is used to determine the system tendencies between tolerance limits. Deviations and faults of the average of process dimensions can be seen. Cp and Cpk are used to determine capability. In this study, statistical calculations for a product manufactured using mass production in a medium sized company were carried out. An X–R chart was constructed for each production line using the manufacturing data obtained from each line respectively and the machine tool – process capability was determined. The X–R graphics of the data obtained from the production line 1 and whole production lines and capability values were compared in order to determine the differences in sizes having tolerances of the part manufactured by five different production lines. It was determined that the production had not carried on normally before the statistical work. After the statistical study, during the machining the sizes having quality problems the production lines where parts were manufactured out of tolerance were determined. It was detected that the machine tool and process capability for the whole process was inadequate and the mass production was unstable. Ovality, faults regarding manu-
facturing out of tolerance limits were eliminated and surface roughness was improved. As a result of this study, the employee understood the quality requirement, the cost due to unquality production was reduced and the production was enabled to carry on normally with the help of problem solving desire of them. Since this study reduced the unquality costs during production, the company management supported the study. However, because of economic problems, the necessary financial support was not given and therefore the statistical quality control studies could not pursued efficiently. As can be seen from the study accomplished, the statistical quality control and capability method is effective for determining the quality problems and solving them in small and medium sized companies that manufacture parts by machining. In todayÕs competitive market, one of the problems of small companies is insufficient investments and lacking sufficient qualified employee having practical and theoretical knowledge for the job.
References [1] Ug˘ur N, C ¸ altug˘ H. Statistical quality control, KOSGEB Education Centre, no. 25, Ankara; 1995. [2] Dolinsˇek S, Kopacˇ J. Linkage between quality assurance tools and machinability criteria. J Mater Process Technol 2001; 118:133–7. [3] Jabnoun N. Control processes for total quality management and quality assurance. Work Study 2002;51(4):182–90. [4] Xie M, Goh TN. Statistical techniques for quality. TQM Mag 1999;11(4):238–42. [5] Chaudhry SS, Higbie JR. Practical implementation of statistical process control in a chemicals industry. Int J Qual Reliab Manage 1989;6:5. [6] Pearn WL, Chen KS. One-sides capability indices Cpu and CpL: decision making with sample information. Int J Quality Reliab Manage 2002;19(3):221–45. [7] Xie M, Lu XS, Goh TN, Chen LY. A quality monitoring and decision-making scheme for automated production process. Int J Quality Reliab Manage 1999;16(2):148–57. [8] C ¸´ınar A. Study of process capability: An example and difficulties. Engineer and Machine 1987;28(329):31–2. [9] Ercan F. Quality control on the industrial area, Ankara: Gazi University; 1987. p. 66–100. [10] Gu¨llu¨ A, Motorcu AR. Elimination of the quality problems encountered during mass production by using statistical quality control. Turk J Eng Environ Sci 2003;27:83–93. [11] Deleryd M, Garvare R, Klefsjo¨ B. Experience of implementing statistical methods in small enterprises. TQM Mag 1999;11(5):341–51. [12] Ug˘ur N, C ¸ altug˘ H. Statistical methods on quality control, KOSGEB Education Centre, no. 25, Ankara, 1995. [13] Motorcu AR. Developing a program for machining parts on CNC milling machines according to optimum time, cost and quality, MSc thesis, Gazi University Institute of Science and Technology, Ankara; 2001. p. 65–90.
Statistical process control in machining, a case study for machine tool capability and process capability Ali Rıza Motorcu, Abdulkadir Gu¨llu¨
*
Department of Mechanical Education, Technical Education Faculty, Gazi University, 06500 Teknikokullar/Ankara, Turkey Received 12 July 2004; accepted 9 November 2004 Available online 10 December 2004
Abstract In this experimental study some statistical calculations have been made to eliminate quality problems such as undesirable tolerance limits and out of circularity of spherodial cast iron parts during machining. X–R control charts have been constructed on the data obtained from this manufacturing to discover and correct assignable causes, so that the machine capability (Cp) and the process capability (Cpk) can be determined. In order to compare design tolerance on working drawings and attained tolerances on workpieces after machining five mass production lines were set up in a medium sized company. The results obtained from five X–R control charts and the data gathered from all production lines were processed and evaluated. At this stage of the study, it was observed that some parts were oval and out of tolerance limits, machines and processes were insufficient and production was instable. Through machining data and follow up studies some assignable causes for faulty workpieces were discovered, and ovalness and out of tolerance limits problems were eliminated. In addition to these developments, surface roughness of machined workpieces was improved. All these activities show that in small or medium sized companies statistical quality control can be useful component of production provided that sufficient finance and qualified personal are utilized. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Statistical quality control; Machine tool capability and process capability; Machinability; Cutting parameters
1. Introduction High quality production provides some advantages such as reduced scrap or remachining and increased market share. For this purpose there are some requirements to be met. First of all the organization should be cooperative and the quality should come first. On the other hand, in order to meet quality requirements of final product, quality should be achieved at every stage of production [1]. Another way of achieving good quality during production is to use the statistical period techniques at *
Corresponding author. E-mail addresses: [email protected] (A.R. Motorcu), agullu@ gazi.edu.tr (A. Gu¨llu¨). 0261-3069/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.matdes.2004.11.003
every stage of production. If the production is statistically under control the process can continue and there is no need for a change in the process. However, if it is not statistically under control, the assignable causes should be discovered and removed from the process. Statistical quality control methods apply statistical principles and techniques at every stage of design, manufacturing, and servicing. Statistical quality control methods are quite different from traditional methods and they have made great contribution to improvements in companies dealing with mass production. In traditional methods, the product is manufactured first and then it is checked to determine whether it meets the quality requirements. The product that does not meet the quality requirements is rejected and sent back to the machines for remachining or correction otherwise it is
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thrown away as scrap. If faulty products are too much, in order to eliminate the assignable causes or the problem necessary corrections are made by examining production period (Fig. 1a). However, statistical quality control is the vital part of production. Instead of checking the finished product after production, it is applied at every period of production. If this period is under control, the next period is considered, otherwise the assignable causes are discovered and corrected (Fig. 1b). Today, in order to preserve and improve quality companies perform their work in three phases: firm organization phase, process phase and application/ performance phase [2]. The priority orders of information and data in these phases are control, diagnosis, and planning respectively. Determination of how this information is distributed and equalized on organizational level is of vital importance for the success of company [2]. Quality improvement processes and calculations are generally carried out at design stage or at work stages. At these stages the standards for the product are quality oriented and customer requirements are taken into account. But at organizational level, the tasks and the procedures at process level should also be organized together. The standards desired for the product may have some criteria such as exact size, material composition, and production time. All employees should understand the necessity of quality and employ required techniques at their daily works [2]. Even tough quality is a must in almost every company, it receives limited attention by managements. Some firms use traditional methods and some of them prefer statistical quality control methods. There are plenty of research work concerning quality improvement. Control process and quality management philosophy was defined according to quality requirements by Jabnoun [3] and it was pointed out that not enough
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attention for quality was paid by managements. For accepting the manufactured parts as identical in mass production, it is enough to try to manufacture parts according to working drawings and tolerances. Because sizes and tolerances obtained after manufacturing make them identical rather than the dimensions and tolerances on working drawings. For this reason, when unbiased decisions about a production period is needed statistical techniques based on unbiased information obtained from product or process are used. Control charts, process capability definitions and design of experiments have been used for years. There is considerable theoretical and experimental research work for improving product quality and processes using statistical techniques. Xie and Goh [4] discussed statistical techniques and their roles for process development considering recent research works and they summarized design techniques by giving some examples. They focused on statistical techniques used for improving quality continuously. In another work, the application of statistical process control in a firm manufacturing chemical and plastic products and its usage was discussed. Focusing on outer necessary factors, statistical process control application was realized. Optimization of processes which is one of the important part of statistical process control was discussed in manufacturing activities and the success of statistical process control was evaluated [5].
2. Process control definitions: machine tool capability (Cp) and process capability (Cpk) Definitions of process control are used to establish qualified measurements for potential and performance of process in industry which are elements of capacity [6]. Capacity analysis is made by using a data set in statistical calculations for defining the systemÕs capability.
First period step Production Period SPC applications
Adjust the period Product
Is it under control?
No Does it meet the specifications?
Scrap or remachining
Scrap or remachining
No Yes
Discover causes
Customer
(a)
Yes
(b)
Eliminate causes
Fig. 1. Quality control methods: (a) traditional quality control method; (b) statistical quality control method.
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In order to define the system capability, the values are compared. If the product is approximately 100% in tolerance limits, it can be said that the system is ‘‘capable’’. The tolerance limits are determined by customers, engineers and management and they are classified as requirements, aims, specifications and standards. There should be lower and upper limits of specification restrictions for the definition of the system. It is accepted that the data should distribute normally for making capacity analysis calculations. A histogram is plotted to see if the data distribute normally or not. Before making capacity analysis, control charts are plotted on the data gathered from the system to see the system stability. Traditional applications of control charts are used to discover the points exceeding the tolerance limits of the part. In modern production systems, since products are generally inspected automatically, data obtained during the use of traditional charts is not suitable for a specific dimension of sample parts. Alongside this the sample size should be selected larger. To make a clear decision about the capability of a production line, enough number of sample parts should be manufactured and inspected [7]. Capability analysis helps to determine the ability for manufacturing parts in the tolerance limits and engineering values. Capability analysis can be applied not only to a manufacturing period but also to a machine tool [1]. Capability analysis gives the information about the system development during the period. Machine tool capability (Cp) and process capability (Cpk) are used to determine the efficiency. Cp is used to determine the systemÕs location in tolerance limits. The size of deviations from the average value of process dimensions will indicate how well the production is. If the system is not at the center of specification values, the trend of Cp is progressing faultily. Cpk is used to determine the average so that the system will works better in the specification limits. If the value of Cpk is 1 it shows that the manufacturing is going on in the system specification limits staying at 99.73% level (±3r limits). If the system centralized at the target value, Cp and Cpk values will be aqual. When the values of Cp and Cpk is 1, this is considered, as the minimum requirement of the system for some companies. Alongside this, larger Cp and Cpk values, for instance 2, are accepted by many companies. Cp and Cpk are defined by the following equations [8]: Range d2 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 ðX X ave Þ SD2 ¼ n1
SD1 ¼
Cp ¼
UTL LTL 6:SD1
ð1Þ
ð2Þ
ð3Þ
Cpk ¼
UTL LTL 6:SD2
ð4Þ
where SD1 guessed standard deviation Range tolerance, the difference between upper and lower tolerance limits d2 center line factor used for calculation of 3r control limits SD2 experimental standard deviation X measured size Xave average of measured sizes n number of measurements UTL upper tolerance limit LTL lower tolerance limit The value of d2 was taken as 3.472 for n = 15 [9]. Capability helps to reach the target values which are important for customers. If a product deviates from the target values, it means the product specifications do not meet the requirements. This causes increase in costs and decrease in sales. An experienced team is required for capability analysis and this team tries to improve it. They collect necessary information and data, and they develop theories and do necessary calculations [10]. Small and medium sized companies must make correct decisions and develop more efficient processes in order to survive in the competitive market. Thereby, correct understanding of the components of variables, definition of factors causing variations and keeping them under control are all important for small sized companies [10,11]. Develeyd et al., presented two papers on application of statistical methods. First paper is on the capability analysis in Swedish industry and the second one is on statistical quality control in two small companies of ceramics industry which do not have large production capacities. These cases shown that efficiencies of some medium sized companies may be increased by applying the statistical methods developed [11]. 3. Statistical process control in machining the cast parts, a case study on Cp and Cpk analysis With this study, eliminating the quality problems of work pieces during machining in a medium sized company comprising a casting and machining work shop was aimed. The process was required to reevaluate because of some problems accrued during assembly and after quality control of products in the company where the assembly of the products was carried out. Obtaining the permission and support of the management a team was gathered, the data was collected and analyzed and the reason for problems investigated [12]. The workpiece is the part of a construction machine. It was cast as spheroidal cast iron and machined using various chip removing operations namely rough and
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finish turning and drilling operations. First turning operations are applied on the sizes of 62.500–62.250, 56.410–56.490 and 63.512–63.550 must be machined within the specification limits and must be under control. The size, tolerance values and surface roughness values of the sample part are shown in Fig. 2. After having examined the faulty parts, it was discovered that there were some problems with the sizes of 62.500–62.250, 56.410–56.490 and 63.512–63.550; some problems with geometric tolerances and some problems with the surface finish. In other words, there were some parts out of tolerance limits, some parts out of circularity (ovalness), some parts with poor surface finish. First the process in the foundry where the parts were cast was examined. Then, in the machining work shop where the quality problem had not been able to solved, the machining problems were identified by doing statistical process control during production. Using the lot acceptance sampling plan, a single-sampling plan in statistical study, it was accepted that 10% of Plot size of 600–750 parts sent to the partner company which represents the whole lot. 75 sample parts were taken from each lot and the accepted quality level was determined as 5%. Samples were taken from five
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production lines where the same amount of products were manufactured in every hour. In order to plot X– R control graphics and to do process capability analysis, statistical parameters were calculated using the measurements values taken from the samples that represent the whole process [9,11]. For one size, dimension distribution of the products manufactured in each production line affects the average of the whole process in a different way. Thereby, carrying out statistical work for each production line, normal distribution diagrams and histograms were prepared and how these affect the whole process was investigated. In Table 1, for the production line 1 statistical study results of 62.500–62.250, 56.410–56.490 and 63.512– 63.550 sizes are given. In Fig. 3, for the production line 1 histograms for 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes are presented. In the production line 1, 62.500–62.250 size the average was calculated as 62.270 and it was determined that normal distribution curve was located in the middle (Table 1, Fig. 3). Staying of the average value within the tolerance limits indicates that no faulty product is manufactured. However, for this production line it can not be said that the manufacturing is
DETAIL J
A
DETAIL C
N7
5˚
DETAIL J
A
0.08 F
Ø5 9.5
N9
0.05 D 0.80x 45˚
DETAIL C DE TA Y C
0.05 D N8
0.08 F
D
Fig. 2. Technical drawing of the sample part.
Ø6 6.7
N8
F
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Table 1 For the production line 1, statistical calculation results for 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes Sample no.
62.500–62.250
56.410–56.490
63.512–63.550
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15
62.270 62.280 62.255 62.255 62.275 62.265 62.280 62.280 62.265 62.265 62.275 62.265 62.280 62.265 62.270
56.450 56.450 56.430 56.450 56.440 56.445 56.435 56.450 56.435 56.450 56.445 56.450 56.440 56.450 56.435
63.540 63.540 63.540 63.530 63.540 63.530 63.540 63.535 63.540 63.540 63.540 63.535 63.540 63.540 63.540
Average SD1 SD2 Range Cp Cpk
62.500–62.250
56.410–56.490
63.512–63.550
62.270 0.0072 0.0085 0.0250 5.7870 4.8735
56.444 0.0057 0.0071 0.0200 2.3148 1.8549
63.538 0.0028 0.0036 0.0100 2.1990 1.7191
12
8
6
10
5 4 3
Frequency
Frequency
Frequency
6
4
8 6 4
2 2
2
1
0
0
0 62.255 62.265 62.270 62.275 62.280
56.430 56.435 56.440 56.445
56.450
63.530
63.535
63.540
Fig. 3. For the production line 1, histograms of 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes.
continuing normally. The averages of 56.410–56.490 and 63.512–63.550 sizes stays within the tolerance limits and it can be said that there is no faulty production for these sizes. The results of statistical calculations of the process with data obtained from all production lines and process capability values are listed in Table 2. Since the gap beTable 2 Statistical results of the process Statistical parameters
62.500–62.250
56.410–56.490
63.512–63.550
Average SD1 SD2 Range LSL USL Cp Cpk LCL (average) UCL (average) LCL (range) UCL (range)
62.2470 0.00576 0.01998 0.0200 62.500 62.250 7.2337 2.0851 62.2517 62.2427 0.0330 0.0070
56.4367 0.00748 0.00924 0.0260 56.490 56.410 1.7825 1.4422 56.4425 56.4309 0.0430 0.0090
63.5390 0.00403 0.00569 0.0140 63.550 63.512 1.5710 1.1124 63.5421 63.5359 0.0231 0.0049
tween the upper tolerance values and lower tolerance values in three sizes is large, the values of machine tool and process capability were desired to be larger than 2 (Cp, Cpk > 2) [12]. Since in the production line 1, for 62.500–62.250 Cp = 5.7870 > 2 and Cpk = 4.8732 > 2 the machine tool and process is sufficient (Table 1). In the same production line during machining of 56.410–56.490 and 63.512–63.550 sizes while the machine capability was accomplished since Cp was greater than 2, it was determined that the process capability was not achieved because Cpk was less than 2. The process stability was observed in this production line after evaluated of capability data obtained from statistical calculations of production line 1. These obtained values showed that having reevaluated the process, process parameters were required to be redetermined. When examining of Table 2 for other two sizes, except for 62.500–62.250 size, both machine tool and process capability were accomplished. On the other hand for two sizes of 62.500–62.250, process capability value Cpk approached to the limit value. When compared the Cp and Cpk values of production
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63.550 for the whole process were presented. In Fig. 3 for 62.500–62.250 sizes, while size dimensions were accumulated near to the lower tolerance value and no production was made out of tolerance in production line 1, Fig. 4 for the same size the normal distribution curve was concentrated near to the lower tolerance value and it can be seen that the production was made in high frequency out of lower tolerance. Being high of size frequency manufactured out of tolerance shows that there is a problem at least in one or more production lines. When examining Fig. 4, it can be seen that no production was made out of tolerance
line 1 in Table 1 and the Cp and Cpk values of the process in Table 2 while the machine tool capability of all sized in production line 1 was achieved, in the whole process, accept for 62.500–62.250 size, any machine tool and process capability was not achieved. It can be seen that at least one or more production lines affect the machine and process capability badly. It was observed that the production deviated from the aimed values and the production was not continued stably after doing machine and process capability work. In Fig. 4, the histograms and normal distribution curves of 62.500–62.250, 56.410–56.490 and 63.512–
20
20
40
18
18
35
16
16
14
14
10
Frequency
Frequency
12
12 10
25 20
8
8
6
6
4
4
2
2
5
0
0
0
15
Fig. 4. The histograms of 62.500–62. 250, 56.410–56.490 and 63.512–63.550 sizes for the whole process.
Fig. 5. The X–R graphics and normal distribution curve for the 62.500–62.250 size.
63.550
63.540
63.535
63.530
63.525
63.520
56.450
56.445
56.440
56.435
56.430
56.425
56.415
56.420
56.410
10
62.220 62.225 62.230 62.235 62.240 62.245 62.250 62.255 62.260 62.265 62.270 62.275 62.280 62.285 62.290 62.295 62.300 62.305 62.310 62.315 62.320 62.325 62.330
Frequency
30
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for 56.410–56.490 and 63.512–63.550 sizes. The normal distribution curves and the frequency were concentrated near average values for both sizes. Using the data obtained from the statistical calculations in Table 2, X–R graphics charts were prepared for each size and normal distribution curves were plotted. In the X–R control graphic shown in Fig. 5 while the averages study near the control limits in the production line 2 and 3 of the sample groups, the averages stay out of the control limits in the production lines 1, 4 and 5. Due to making production out of the control limits it was necessary to stop the production and rearrange the manufacturing and machine tool parameters by evaluating the process completely. As it can be seen from the normal distribution curve in Fig. 5 the average of averages Xave is smaller than the lower value (Xave = 62.247 < 62.500). Thereby it can be said that most of the products manufactured are faulty. When examining the X–R charts of 56.410–56.490 size in Fig. 6, it is clearly seen that the averages of the production line 1 and 3 are out of the control limits and the average of the production line 2 approached to the control limits. Whereas in the production line 4 and 5 since the averages came close to the average of averages, the production continuous normally. The production line 1, 2 and 3 must be stopped, examined and the necessary arrangements must be made. In Fig. 7 for 63.512–63.550 size while the production is made out of the control limits in the production line 1, 2 and 3, the production is carried out normally in the
production line 4 and 5 according to the X control graphic. Since the production is made out of the control limits, the production lines must be stopped, machine tool and machining parameters reexamined and the necessary arrangements must be made. Since the values of the average of averages for 56.410–56.490 and 63.512–63.550 sizes accumulated between the tolerance values of the two sizes faulty products were not manufactured however, it can be said that there is a tendency that these sizes can be manufactured faulty in some production lines.
4. Machinability parameters and rearranging the process As a result of the statistical studies, it was necessary to stop the process, reexamine the process parameters and rearrange the process in order to reassure machine tool and process stability. Workpiece material, machine tool and cutting parameters which are three important components of machinability were reevaluated. First of all the structure of the workpiece material were examined. Since the casting and machining of the part are carried out in different work shops in the firm, having done arrangements in the casting work shop, it was determined that there existed shell hardening of the outer surface of the part because of dismantling of casting dies before the necessary waiting time. Casting die sets were dismantled after the
Fig. 6. The X–R graphics and normal distribution curve for the 56.410–56.490 size.
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Fig. 7. The X–R graphics and normal distribution curve for the 63.512–63.550 size.
required time in order not to reduce the tool life and therefore not to increase the tool costs because of this excessive shell hardening. After the statistical studies carried out during manufacturing in the production workshop, the parameters of two main components of machinability were investigated in order to prevent manufacturing out of tolerances and ovality and to obtain the ideal surface roughness. In order to prevent the ovality, the settings of the center line of the chuck and tail stock of the CNC machine tool and chuck jaws were examined. Having redetermined the work piece reference points on the CNC machine tool, the center setting of the machine tool were readjusted. It was determined that inappropriate cutting speeds and feed rate values, hard shell and sandy areas on the outer surface of the part reduced the cutting tool life. It was also determined that early fractures of cutting tools were occurred during machining and different from the sizes programmed in CNC part programs were obtained because of the insert corner wear. Since tool wear was not determined dynamically, size dimensions were determined as larger than they should be after a number of parts manufactured. Since the tool wear was not measured and the tool life was not determined correctly, the production out of tolerance values were occurred. In order to prevent
this, the cutting parameters in the CNC part program namely cutting speed, feed rate and cutting depth were examined [13]. Taking into account of insert nose radius values of CCGT09T304, HC2DNGA 443TN and CCGT09T304 used for machining 62.500–62.250, 56.410– 56.490 and 63.512–63.550 sizes, (0.4 mm, 1.2 mm and 0.4 mm respectively) and the desired surface roughness values (N8 = 0.16 lm, N7 = 0.8 lm and N8 = 0.16 lm respectively), the feed rate values were redetermined as 0.130 mm/rev, 0.163 mm/rev and 0.130 mm/rev respectively [13]. When machining the spheroidal cast part, 100 mm/ min cutting speed were selected and spindle speeds were entered to the CNC part program as 505 rev/ min, 568 rev/min and 497 rev/min for machining 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes respectively [13]. In order to prevent excessive tool wear, cracks and fractures when machining the outer shell of the part, the rough cutting depth of the first pass was increased and the damages generated by intermittent impacts and sandy areas to the tool nose and cutting edge minimized. After entering the new calculated parameters into the CNC program, it was determined that how much the cutting tool wore and the tool life expired after how
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many parts. According to the criteria determined, the operator was told to change the insert before itÕs tool life. After rearranging the machining parameters, the production was resumed, it was investigated that how the new values had affected the process and the statistical studies were accomplished. As a result of the studies the parts were manufactured within the tolerance limits and ovality and surface roughness problems were eliminated. These results were observed by measuring and controlling the parts manufactured.
5. Results and discussion Statistical quality control is applying statistical rules and techniques to each phase of design, manufacturing and service. One of the techniques for assuring the quality during production is to apply the statistical period control techniques to each phase of production during or after manufacturing. Statistical quality control have had great deal of improvements in companies using mass production. Capability analysis helps to determine the ability for manufacturing between tolerance limits and engineering specifications. Capability analysis can be applied not only to production period but also to a machine or machine tool. Capability analysis gives the information about changes and tendencies of the system during production. It is used to determine the system tendencies between tolerance limits. Deviations and faults of the average of process dimensions can be seen. Cp and Cpk are used to determine capability. In this study, statistical calculations for a product manufactured using mass production in a medium sized company were carried out. An X–R chart was constructed for each production line using the manufacturing data obtained from each line respectively and the machine tool – process capability was determined. The X–R graphics of the data obtained from the production line 1 and whole production lines and capability values were compared in order to determine the differences in sizes having tolerances of the part manufactured by five different production lines. It was determined that the production had not carried on normally before the statistical work. After the statistical study, during the machining the sizes having quality problems the production lines where parts were manufactured out of tolerance were determined. It was detected that the machine tool and process capability for the whole process was inadequate and the mass production was unstable. Ovality, faults regarding manu-
facturing out of tolerance limits were eliminated and surface roughness was improved. As a result of this study, the employee understood the quality requirement, the cost due to unquality production was reduced and the production was enabled to carry on normally with the help of problem solving desire of them. Since this study reduced the unquality costs during production, the company management supported the study. However, because of economic problems, the necessary financial support was not given and therefore the statistical quality control studies could not pursued efficiently. As can be seen from the study accomplished, the statistical quality control and capability method is effective for determining the quality problems and solving them in small and medium sized companies that manufacture parts by machining. In todayÕs competitive market, one of the problems of small companies is insufficient investments and lacking sufficient qualified employee having practical and theoretical knowledge for the job.
References [1] Ug˘ur N, C ¸ altug˘ H. Statistical quality control, KOSGEB Education Centre, no. 25, Ankara; 1995. [2] Dolinsˇek S, Kopacˇ J. Linkage between quality assurance tools and machinability criteria. J Mater Process Technol 2001; 118:133–7. [3] Jabnoun N. Control processes for total quality management and quality assurance. Work Study 2002;51(4):182–90. [4] Xie M, Goh TN. Statistical techniques for quality. TQM Mag 1999;11(4):238–42. [5] Chaudhry SS, Higbie JR. Practical implementation of statistical process control in a chemicals industry. Int J Qual Reliab Manage 1989;6:5. [6] Pearn WL, Chen KS. One-sides capability indices Cpu and CpL: decision making with sample information. Int J Quality Reliab Manage 2002;19(3):221–45. [7] Xie M, Lu XS, Goh TN, Chen LY. A quality monitoring and decision-making scheme for automated production process. Int J Quality Reliab Manage 1999;16(2):148–57. [8] C ¸´ınar A. Study of process capability: An example and difficulties. Engineer and Machine 1987;28(329):31–2. [9] Ercan F. Quality control on the industrial area, Ankara: Gazi University; 1987. p. 66–100. [10] Gu¨llu¨ A, Motorcu AR. Elimination of the quality problems encountered during mass production by using statistical quality control. Turk J Eng Environ Sci 2003;27:83–93. [11] Deleryd M, Garvare R, Klefsjo¨ B. Experience of implementing statistical methods in small enterprises. TQM Mag 1999;11(5):341–51. [12] Ug˘ur N, C ¸ altug˘ H. Statistical methods on quality control, KOSGEB Education Centre, no. 25, Ankara, 1995. [13] Motorcu AR. Developing a program for machining parts on CNC milling machines according to optimum time, cost and quality, MSc thesis, Gazi University Institute of Science and Technology, Ankara; 2001. p. 65–90.