Peter Scallan, in
Process Planning, 2003 The control charts for variables are constructed by using measured values. However, it is not always
possible or appropriate to measure a variable. That is to say, that non-conformance is not always measurable. For example, surface imperfections such as scratches or dents cannot be measured. However, the number of times this type of default occurs can be counted and this is in essence what an attribute chart does. Although much simpler than variable charts, they should not be used instead of these and should only be used when it is impossible to measure the quality characteristic
under consideration. Four basic attributes charts are used, but unlike variables, they are not used in pairs. However, they can be classified into pairs according to what they monitor and/or control. This is because two of the charts monitor the proportion and number of defectives and the other
two monitor the proportion and number of defects in the sample and per sample unit. A defective can be defined as a unit that fails due to one or more non-conforming characteristics. A defect can be defined as a non-conforming characteristic of a unit. Therefore, a unit can have a number of defects. p and np charts. A p chart monitors the proportion of defectives in a lot or batch. Therefore, it counts the number of non-conforming
units in a lot or batch. The np chart monitors the number of defects. However, for the same data set with a constant sample size both should look the same. A summary of the chart data is given in Table 8.12. TABLE 8.12. p and np chart data
c and u charts. The c chart is used to monitor the number of defects in a sample while the u chart monitors the average number of defects per sample unit. The c chart is similar to the np chart except that it counts defects as opposed to defectives. A summary of the chart data is given in Table 8.13. TABLE 8.13. c and u chart data
Sampling for attribute control chartsThe sample size for attributes will be influenced by the probability of finding a defective or a defect. Therefore, as a general rule the sample size should be large enough to have several defects or defectives (Evans and Lindsay, 1993; SMMT, 1994). In general, sample sizes for attributes are larger than those for variables. For p charts, sample sizes can be variable but it is recommended that they do not vary by more than ± 25% of the average sample size (SMMT, 1994). However, the sample size for the np chart should be constant. The c chart also deals with constant sample sizes. Typically, samples can be an assembly unit, an area of material or a group of units. The n chart deals with a variable sample size and the rule for the p charts holds for c charts. For all charts, it is best if samples are taken randomly but units should be made consecutively. Finally, although p and u charts cope with variable sample sizes, the use of a constant sample size reduces the amount of calculation required. Setting up the attribute chartThe approach to setting up attribute charts is almost identical to that for variable charts. There are two main differences. The first is that the number of samples may be slightly higher for attribute charts and is typically 25–30. The other major difference is in establishing the control limits. This is because the process mean is used to calculate the limits and there are no statistical constants used. The formulae for calculating the control limits for all four attributes charts are illustrated in Table 8.14. TABLE 8.14. Formulae for control limits for attribute charts
It should be noted that if the lower control limit as calculated is negative then it is taken to be zero. Using the attributes control chartThe method for using attribute charts is very similar to that for variables charts. The six basic steps are: 1.collect samples at regular intervals; 2.count defects or defectives as appropriate; 3.calculate the data to be plotted in the case of np, c or u charts as required; 4.calculate the process mean using the appropriate formula and draw; 5.calculate the UCL and LCL and draw; 6.check the plotted chart for out of control conditions. This is best illustrated through a worked example. Read full chapter URL: https://www.sciencedirect.com/science/article/pii/B978075065129550009X Statistical process control (SPC)Robin Kent, in Quality Management in Plastics Processing, 2016 Setting up an attribute chartAfter the type of attribute chart to be used is decided (see Section 5.19) the process of setting up an attribute control chart is very similar to that of setting up a variables control chart and is shown on the lower right on the opposite page. •Using a running and stable process, assess the selected sample size at the decided frequency. •Tip - The sample size for attributes control charts will be significantly higher than for variables control charts. Do not underestimate how long it will take to assess the samples. •Do not adjust the process or make any changes during the run. •Decide on the assessment attribute that it is desired to control and record the assessments (nonconformities or nonconforming items). •Calculate p, np, c or u depending in the chart type. •Continue until 25 sets of data are available. •Plot the results for p, np, c or u on a preliminary control chart in time order. •Calculate the centre line value from the relevant equation in Section 5.19. •Calculate the UCL and LCL from the relevant equation in Section 5.19. Note: If the LCL calculation gives value < 0 then set the LCL as 0. •Mark these trial control limits on the chart. If any of the points on the chart are outside the trial control limits then discard these points and recalculate the control limits. •Continue this process until all points are within the trial control limits (provided there are still at least 20 data points). •Plot the average for the centre line and the Upper and Lower Control Limits on the control chart. Standards need to be: •Realistic. •Well-defined (especially at the border lines). •Strongly enforced. Read full chapter URL: https://www.sciencedirect.com/science/article/pii/B9780081020821500058 Processing quality managementRobin Kent, in Quality Management in Plastics Processing, 2016 Follow a processIf the rules for attribute charts indicate a non-conforming attribute then the process is ‘out-of-control’ and action needs to be taken. The process is as follows: Attributes are generally the visual aspects of the moulding but can include dimensions if these are monitored by ‘go/no-go’ gauges. A fail on a ‘go/no-go gauge’ is then treated as an attribute defect. What is the concern?There are many visual non-conformities that can arise in injection moulding and the terminology varies not only from company to company but also from country to country. A list of some of the terms for the most common defects is shown below. If the mould is the cause of attribute failure then fix the mould. Do not try to adjust the process. You might get the mould working but this will only push the ‘good’ cavities close to the edge of the processing window and reduce the robustness of the complete process. The first step in solving attributes issues is to define what the actual concern is and to get everybody to agree on what you are dealing with. Without agreement on what the non-conformity actually is it will be impossible to resolve the issue. •Tip - The range of terms used to describe visual defects is very wide. Visual samples or photographs of each type of concern will help people to agree on what the issue is. Start a collection for the future now - I have my own samples. Surface and visual (attribute) concernsThere are many different types of concerns that can be tracked via attributes. The terminology varies throughout the industry and one man’s ‘silver streak’ is another man’s ’splay’, some of the more common terms are:
When is the concern?When an attribute concern appears is vital to finding a solution and the options are shown on the upper right. Concerns can be: •Mould and machine trial related - this type of concern must be resolved before the process is accepted for production. •Start-up related - this type of concern is at start-up with a mould, machine and material that previously produced good product. In this case the focus is on determining what has changed between the runs. Start-up-related concerns must be corrected by the setter before the machine is handed over to production. Tip - The main reason for start-up concerns is generally changes in machine settings made on the previous run. Restore the machine to the ‘good’ settings for production. •Tip - If the mould is being used on a different machine then settings will obviously need to be modified. •Tip - Check that the mould has not been modified since the last good run. •Tip - Check if the material is from a batch that previously produced good products. If a new batch/delivery has been used then try a different batch to eliminate material issues. •Tip - Retain a sample of ‘good’ material that can be used to quickly check for material issues if start-up is a problem. •Quality control-related - this type of concern occurs on a running process when the chart shows that the process is ‘out-of-control’. In this case the focus is on determining what has changed during the run and any changes in material, machine or mould should be checked. •Tip - For QC-related concerns look at the material and machine first. Where is the concern?The location and frequency of the concern, especially in multi-cavity moulds, can give excellent information on the source of an attribute concern and the options are shown on the lower right •Tip - The important thing is to use all of the available information. How do we solve the concern?The Internet has many ‘troubleshooting guides’ for visual defects but my favourites are from Andy Routsis1, 4Plas2, and Kenplas3. • 1. Routsis, A. 2015. ‘Injection Molding Reference Guide’. Pg 46-47. Available free from www.traininteractive.com. •2. 4Plas. 2014. ‘Injection Moulding Troubleshooting Guide’, www.4plas.com/download.php?id=111. •3. Kenplas. 2016. ‘Trouble shooting for the injection molding process’, www.kenplas.com/ service/imtroubleshooting.aspx. Analyse everything and use the Quality Tools (see Chapter 7) to isolate the root cause and then solve it permanently.  Read full chapter URL: https://www.sciencedirect.com/science/article/pii/B9780081020821500101 Six Sigma Education and Using the Existing Quality Methods and ProceduresM. Joseph GordonJr., in Six Sigma Quality for Business and Manufacture, 2002 CONTROL CHARTSThere are two basic types of control charts: 1.Measurements or “variables charts,” that are used when actual readings are recorded, the so-called X, R, and s charts. 2.Attribute charts, that use visual or go, no/go data – the traction or percentage defective charts called, P charts. There are two different situations in which these charts are used: 1.When establishing a new process – either one that has undergone extensive changes or one that investigates on-going control after a preliminary frequency-distribution analysis has demonstrated initial control. Data is collected on product quality characteristics and control limits. And central tendency values are then calculated. Hence this condition is termed one of “no standard given”. 2.When central tendency and spread values are initially established. This condition is known as “standard given”. It assumes that the process is in control based on whatever data was used to establish the limits, arbitrary or real. These are based on production or service specifications and their requirements or on a target value established between the customer and supplier for the products. The approach for calculating the control limits for these two charts is based on the laws of probability. The methods of calculations that vary between the measurement and percentage charts will be discussed in detail. The process to follow in setting up the control limit chart is: No Standard Given1. Select the quality characteristics to be studied. 2.Record data on a required number of samples with an adequate number of units per sample. The minimum is five samples. 3.Determine control limits for the sample data. 4.Analyze the state of control in the process. (a)Too much variation. (b)Products move in and out of control. (c)Well-controlled process. When establishing control limits, several samples will often be out of control. In this case, trace down the problem in the process and repeat steps 2 and 3 until the process is in control. Standard Given1. Select the quality characteristic to be studied. 2.Establish the central tendency value and the spread to be used. All available data must be used to show that control exists. 3.Determine the control limits from these values. 4.Establish that these control limits are economical, practical, and required. 5.Establish the control limit values and plot them on graph paper. 6.With the manufacturing process in control, begin recording results from production samples selected at periodic intervals. 7.Take corrective action if the characteristics of the production samples exceed the control limits. An example of the graph paper used to plot the data is shown in Figure 12. Figure 12. Control chart blank graph sheet. Read full chapter URL: https://www.sciencedirect.com/science/article/pii/B9780444510471500069 An empirical evaluation of attribute control charts for monitoring defectsSurath Aebtarm, Nizar Bouguila, in Expert Systems with Applications, 2011 1 IntroductionManufacturing of products always deals with variation in production. This variation is due to common and/or special causes (Mittag & Rinne, 1984; Oakland, 2003). When a process contains only common causes of variation, the process is in control. The average level of events, errors, or defects per unit can be used to calculate the process capability. The causes of variation are related to large magnitude of non conformities. When a process contains only special causes, the process is out of control. To deal with process variation, control charts are effective tools that are widely used for quality inspection. The main purpose of a control chart is to continually monitor a given process by illustrating its behavior (Montgomery, 1985). A control chart can be also used to define the process capability before starting large production. In a process, data can be continuous or discrete. Attribute charts have been widely used to monitor discrete data. Since attribute data can be gathered from every process or even transformed from continuous data, attribute control charts are widely used in many fields to monitor both manufacturing and non-manufacturing processes (Bain & Engelhardt, 1992). For instance, a control chart monitoring the number of defects can be used for manufacturing purposes, and a control chart monitoring the number of accidents per week is used in non-manufacturing issues (Oakland, 2003). Although an attribute chart is not always as effective tool as continuous control charts to find root problems and solutions, it is an economical tool to collect and analyze the process characteristics before continuous charts can be applied (Doty, 1996). Besides, attribute charts are more practical in many cases. For example, monitoring number of survival patients per year is more practical than monitoring how long patient can survive which usually uses continuous control charts (Bain & Engelhardt, 1992). C-chart is used to monitor the actual total number of defects per unit. For example, number of defects per item and number of patients in a hospital per day (Doty, 1996). Constructing C-chart is inexpensive since the plotted data are count data which does not require measurement, and can be collected from daily reports in many cases. Furthermore, C-chart is used for plotting numbers of defects. Thus, it is simpler to plot C-chart than any other control chart by just plotting raw data without necessity of transformation (Bain & Engelhardt, 1992). However, there are some points that need to be considered before applying attribute charts. Attribute control charts can be biased if an inspector misjudges a product to be a defective (Oakland, 2003). For measuring small variables changes, attributes are not as sensitive as continuous charts to represent the process, since an attribute chart plots only in term of acceptable or not, instead of exact value of data. The result of using attribute charts is sometimes out of reality because in some cases some small differentiation cannot count as a defective in reality (Oakland, 2003). Furthermore, C-chart is known as ineffective control chart, since it creates excessive false alarm, especially, in the case of high yield manufacturing (Niaki & Abbasi, 2007). The main goal of this research is to present, compare and discuss several selected approaches that were proposed to improve C-chart. Indeed, it is largely unclear how these different control charts vary in performance. Using simulated data sets, the performances of these approaches are compared via standard measures such as average run length and loss function. According to this comparison some conclusions are drawn in order to guide users in selecting appropriate chart. The rest of the paper is organized as follows. In the second Section, the classic Shewhart’s C-chart and its weaknesses will be discussed. Section 3 presents some key parameters that are widely used for evaluating control charts. In Section 4, some improved attribute charts will be presented in details. Then, the performance of each chart will be discussed in Section 5, and the conclusion of this paper will be drawn in Section 6. Read full article URL: https://www.sciencedirect.com/science/article/pii/S0957417410014569 What type of control chart would be used to monitor the number of defectives in the output of a process invoice processing?A p control chart is used to look at variation in yes/no type attributes data. There are only two possible outcomes: either the item is defective or it is not defective. The p control chart is used to determine if the fraction of defective items in a group of items is consistent over time.
What type of control chart would be used to monitor the number of defectives in the output of a process for making iron castings?Explanation: The p-chart or the Control Chart for Fraction Nonconforming is used to plot “the number of defectives in the output of any manufacturing process” data, on a control chart.
Which type of control chart should be used to monitor the number of defects per unit?The u-chart is a quality control chart used to monitor the total count of defects per unit in different samples of size n; it assumes that units can have more than a single defect. The y-axis shows the number of defects per single unit while the x-axis shows the sample group.
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What type of control chart would be used to monitor the number of defectives for a process with a constant sample size?
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