Statistical Process Control (SPC) applied in plastic injection moulded lenses. Jafri Mohd Rohani, Chan Kok Teng

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1 1 Statistical Process Control (SPC) applied in plastic injection moulded lenses Jafri Mohd Rohani, Chan Kok Teng Fakulti Kejuruteraan Mekanikal, Universiti Teknologi Malaysia, 81310, Skudai, Johor Abstract In order to survive in a competitive market, improving quality and productiviti of product or process is a must for any company. Some simple techniques like the seven basic quality control(qc) tools provide a very valuable and cost effective to meet those objective. This paper presents a case study in which a local plastic injection moulding company employs part of the seven basic quality control(qc) tools to significantly improved the process yield quality from initial % to 7.4 %. However, to make them successful as cost effective and problem solving tools, strong commitment from top management is desired. 1.0 Introduction The competitive business in the telecommunication market has enhanced the company in this study to provide lower cost quality product. Quality improvement program had been designed and been implemented to increase the potential of profit. By improving the quality, it is also mean to improve the productivity and low reject rate. Quality goals can be included in the business plan and as a degree of a product or service excellence provided to customer. The key of quality improvement of this company is not only focusing an external customer but also it internal customer. The purpose of this study is to improve the quality of plastic injection moulded lenses used in telecommunication device. The objective of this study is to reduce the defect rate from 13.49% to 10%. The company had implemented part of the seven quality old tools in their problem solving technique. The most commonly used seven quality old tools [1]: a. Check Sheet b. Pareto Chart c. Histogram

2 2 d. Scatter Diagram e. Process Flow Chart f. Cause and Effect Diagram or Fish Bone Diagram g. Control Chart The control chart is perhaps the most used of the seven quality old tools. It is the key tool in statistical process control(spc) because it displays process behavior graphically and it is used to monitor and control processes within the specified control limits [2]. There are two basic types of control chart, depending on the type of data collected; namely variable control chart and attribute control chart. Variable control chart are designed to control product characteristics and process parameters which are measured in continuous scale. Example of product characteristics are length, weight, and diameter and process parameters are temperature, pressure, and PH [3]. The primary variable control chart used are the X-bar and R chart and moving range chart, while the other two, rarely used charts include X-bar and s chart and median chart [4]. Attribute control charts are designed to control the process which are stated in terms of good or bad, accept or reject, go/no-go, or pass or fail criteria(eg. Conforming or non-conforming) [3]. The distinction between nonconforming or defective unit and nonconformities or defects is very important in attribute control chart because it will determine the selection type of attribute control chart. An example of nonconformities or defects in injection moulded lenses are flow lines/marks, dirty dots and scratches. A nonconforming or defective unit, however, may fail to meet the assesment criteria because of one or more nonconformities or defects exists. For attribute data, there are: p chart, np chart, c chart and u chart. The p and np chart are the most widely used primarily to monitor the fraction of nonconforming unit, while, the c and u chart be used to monitor the number of nonconformities or defects. The details about the theoritical discussion and future research of attribute control chart, see reference [5].

3 3 2.0 Methodology and Techniques The company had collected the data for three months which based on daily check sheet which include the quantity output of good parts and defective parts as shown in Figure 3.1. PERCENTAGE (%) Types of Defects Month Total Production Total Good Parts In-Process Fall-Out Rework Fall-Out Flow Line / Marks Dirty Dots Scratches Other Defective Gate Crack ROI / Line Check Silver Foil Peel Foil Defective Dust Alignment Out Sink Mark February March April Average Target Total % Defect: February: 14.80% March: 13.48% April: 12.18% Average: 13.49% Figure 3.1: Data Collection From information in Figure 3.1. the Pareto chart was constructed to find the most common defect as shown in Figure 3.2. The chart revealed that flow lines/ marks is the highest defect with average of 5.04%, dirty dots with average of 3.96%, and follow ed by scratches with average of 2.27%. All others minor defects had been shown in the Pareto chart as shown in Figure 3.2. Only top three major defects had been chosen for this case study.

4 4 Pareto: Type of Defect (Feb - March - April ) Defect (%) Series2 Series1 Series6 Series3 Series4 Series Flow Line / Marks Dirty Dots Scratches Other Defective Gate Crack ROI / Line Check Silver Foil Peel Foil Defective Defect Types Dust Alignment Out Sink Mark Figure 3.2: Pareto Chart Figure 3.3, Figure 3.4 and Figure 3.5 show the fishbone diagram of the top three analysis of the top three defect types. The root causes of these three defect types can be separated into group, such as people, work method, environment, material, and equipment. a. Flow Line/Marks Flow lines/marks is usually caused by injection moulding process parameters such as holding time, injection temperature and flow pressure. Raw material itself and tooling design can also cause to the problem. Figure 4.2 shows some possibility that might cause to the flow lines/marks.

5 5 Pay attention Attitude Machine Operator Training Skill Materials Type of material Specification Drying time Drying temperature Melt flow index/characteristics Flow Lines/Marks Material flow speed Holding time Work Method Heat / temperature Of injection Co efficiency Machine Maintenance Figure 3.3: Fishbone Diagram of Flow Lines / Marks b. Dirty Dots Dirty dots is not only caused by incoming raw material but also due to mould and operator handling. Figure 4.4 shows shows some possibility that might cause to the dirty dots. Pay attention Connecter between hoppers and machine Attitude Process Operator Cleanness Handling Material Specificat Under ion limitation Handling Cleanness Cleanness Maintenance Environmen Cleanness Dirty Dots Work Method Machine c. Scratches Figure 3.4: Fishbone Diagram of Dirty Dots Besides the packaging and handling process that might cause to the scratches, mold condition itself, might cause to this problem. Figure 4.5 shows the fishbone diagram of scratches.

6 6 Machine Operator Materials Machine Packing Attitude Attitude Skill Negating Packaging component Tray Transferrin g Clean Scratches Packing process Instrument Negating Lighting Method handling Work Method Measurement Figure 3.5: Fishbone Diagram of Scratches 3.0 Improvement Action Plan The related areas for improvement can be classified into operator, material, machine, work method and environmnet. Figure 3.6, Figure 3.7 and Figure 3.8 summarize the action plan for respective analysis of the top three defects.

7 7 Type Machine Operator Material Machine Work Method Action Plan Suggestion for Flow line/marks -Must have skill / training provide- Knowledge -Must have good attitude / pay full attention -Follow work procedure works -Every incoming material/resin must go through MFI (Melt Flow Index) -Must have correct drying time /temperature (in hopper).as Specification -A preventive maintenance ensure machine always in good condition -Machine must always ensure to follow correct temperature, holding time, flow condition during inject Figure 3.6: Action Plan for Flow lines/marks Type Operator Material Machine Work Method Environment Action Plan Suggestion for Dirty Dots -Material must handle properly from any dirt -Must have good attitude / pay full attention. -Cleanness- size dirt dot under limitation. -Machine, mould, hopper must confirm clean all the time -Connector between machine and hopper must confirm clean -Follow procedure -Work environment must clear Figure 3.7: Action Plan for Dirty dots Type Operator Material Machine Work Method Environment Action Plan Suggestion for Scratches -Condition packaging. Follow work Instruction -Must have good attitude / pay full attention -Skill/knowledge on negating -Packing component. Correct design/requirement -Transferring/handling on tray -Clean -Packing process must correct. Follow work instruction.] -Method handling on part -Work environment must clear Figure 3.8: Action Plan for Scratches

8 8 4.0 Results Analysis and SPC implementation After implementing the action plans for the top three defects, the significant improvement was observed. Based on three months data collected, the Pareto chart in Figure 4.2 shows that monthly defect had reduced from 10.28% in May to 8.27% in June and 7.41% in July. Pareto: Type of Defect (May - June - July) Defect (%) May June July May June July Flow Line / Marks Dirty Dots Scratches Other Defective Gate Crack ROI / Line Check Silver Foil Peel Foil Defective Dust Alignment Out Sink Mark Defect Types Figure 4.2: Pareto chart of May, June and July The p chart was also constructed to analyze the process and help determine how yield might be improved further as shown in Figure 4.5, Figure 4.6 and Figure 4.7. The p chart was constructed according to MIL STD 105E LEVEL II AQL 0.4% SINGLE NORMAL INSPECTION as requested by the customer.

9 9 Figure 4.3 shows that inspection level II had been recommended and applied to the sampling plan with code letter K and L for different sample size depending on output lot size of above 1200 and below parts. Sampling size code letters Lot or batch size Special inspection levels General inspection levels S-1 S-2 S-3 I II III 2 to 8 A A A A A B 9 to 15 A A A A B C 16 to 25 A A B B C D 26 to 50 A B B C D E 51 to 90 B B C C E F 91 to 150 B B C D F G 151 to 580 B C D E G H 281 to 500 B C D F H J 501 to 1200 C C E G J K 1201 to 3200 C D E H K L 3201 to C D F J L M to C D F K M N to D E G L N P to D E G M P Q to over D E H N Q R Figure 4.3: Sampling size code letters The customer has requested that our part be inspected according to MIL

10 10 STD 105E LEVEL II AQL 0.4% SINGLE NORMAL. Based from Figure 4.4, it shows that for level II code letter K sample size are 125 and if 2 or more defective unit had found, all parts in that lot will be rejected and for letter code L show that sample size are 200 and defective size are allowed for only 2 or less in sample size of 200. TABLE II-A - Single Sampling Plans for normal inspection (Master Table) Sample size code Letters Sample size Acceeptable Quality Levels (normal inspection ) Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re A B C D E F G H J K L M N P Q R Use first sampling plan below arrow. If sample size equals, lot or batch size, do 100 percent inspection. Use first sampling plan above arrow. Ac Re acceptance number Rejection number Figure 4.4: Single Sampling Plans for Normal 1nspection

11 11 Fraction Defective (p) Fraction Defective (p) Chart May st Shift 2nd Shift 3rd Shift Subgroup Number UCL CL LCL Figure 4.5: p chart for May Data Interpretation: 1) Figure 4.5 (1 st and 2 nd shift) show the higher reject was caused by scratches from packaging material and from operator and sink marks from machine 2) 3 rd shift shows the constant reject quantity but it was the major causes to the total reject which is due to scratches by operator and some other defective like stain mark that also caused by operator. Dirty dots due to resin/material and some small quantity flow line also causes to defective. Corrective Action: 1) All reject lot had been go through 100% sorting. Those scratches and sink mark parts had been scrapped and separated from good parts. 2) Operator had reworked the part that caused by stain mark by using hexane to clear and for those caused by dirty dots had been sorted it out.

12 12 Fraction Defective (p) Fraction Defective (p) Chart Jun e 1st Shift 2nd Shift 3rd Shift Subgroup Number UCL CL LCL Figure 4.6: p chart for June Data Interpretation: 1) Figure 4.6(1 st shift) shows that the higher reject was caused by scratches due to operator and stains (others) due to mould. 2) 2 nd and 3 rd shift from chart show that the reject caused are almost the same mostly was due to operator handling. 3) The uneven upper control limits of above chart different sampling size. From calculation sampling size Upper Control Limit (UCL) with for sample size 125 and only for sample size 200. Corrective Action: 1) Those scratches part had been sorted out and separate from good part. All scratches part had been scraped. 2) Mould surface had been cleaned from dirty that caused to the stain mark.

13 13 Fraction Defective (p) Fraction Defective (p) Chart July st Shift 2nd Shift 3rd Shift Subgroup Number UCL CL LCL Figure 4.7 p chart for July Data Interpretation: 1) Figure 4.7 shows that sample size is much more constant that June data 2) 3 rd shift still remain as a major defect sequence than 2 nd shift and 1 st shift the reject is not only due to scratches that cause by operator but the carelessness of operator also cause to the broken ribs of the parts when they did the negating process. Some others reject include with dirty dots and gate crack Corrective Action: 1) All rejected lots had been sorted and all defected part had been separated from good parts. Good parts will be used back for the next process. 2) Operator had been advised to be more careful on their handling to prevent defect that caused by human error.

14 Conclusion Part of the Seven QC tools had been used for quality improvement activities. For example, fish-bone diagram had been use to describe an unsatisfactory condition or phenomenon and help to examine why that problem arised by systematically arranging the contributable factors. An p chart had also been used to monitor the distribution pattern of average defect rate. An improvement action plan had been set-up, then the data had been collected for the three months and reexamine the defect results. The set goals had been achieved which based on three month collected that showed the average defect had changed to 7.4 % from initial %. It was noted that even a simple QC tools can make significant improvement to the company. Some future improvement plan that had been suggested and recommended: 1 Company should be more discipline and all operators must go through some simple training especially on handling parts to avoid from some defect that caused by handling like finger print, stain mark and scratches. Especially, new operators must knowh how to handle the parts properly. Work instruction sheet can be used as a guide for proper work. 2 Machine must have a daily check sheet and machine operator must check the machine condition for every shift to confirm the machine are in good condition which mean that the machine pressure, temperature and holding time must be accurate to avoid from flow lines/marks defects. Mould must confirm always in good condition and clean from any dirt or dust that might caused to stain mark and scratches. 3 Every incoming lots material must go through Melt Flow Index checking to confirm the material to avoid from flow line defect.

15 15 References [1] Ishikawa,K [1985], What is Total Quality Control, Prentice Hall, Englewood Cliff, N.J. [2] Bisgaard, S. [1993], Statistical Tools for Manufacturing, Manufacturing Review, vol. 6, no. 3, September, pp [3] Freeman, J, Mintzas,G,[1999], Simulating c and u Control Schemes, The TQM magazine, vol. 11, no. 4, pp [4] Anjard, R.P.[1995], SPC chart selection process, Microelectronic Reliability, vol. 35, no. 11, pp [5] Wodall, W.H.[1997], Control Charts Based on Attribute Data: Bibiography and Review, Journal of Quality Technology, vol. 29, no. 2, April, pp