Process control is a statistics and engineering discipline that deals with architectures, mechanisms, and algorithms for controlling the output of a specific process. See also control theory.
For example, heating up the temperature in a room is a process that has the specific, desired outcome to reach and maintain a defined temperature (e.g. 20°C), kept constant over time. Here, the temperature is the controlled variable. At the same time, it is the input variable since it is measured by a thermometer and used to decide whether to heat or not to heat. The desired temperature (20°C) is the setpoint. The state of the heater (e.g. the setting of the valve allowing hot water to flow through it) is called the manipulated variable since it is subject to control actions.
A commonly used control device called a ‘’’programmable logic controller’’, or a PLC, is used to read a set of digital and analog inputs, apply a set of logic statements, and generate a set of analog and digital outputs. Using the example in the previous paragraph, the room temperature would be an input to the PLC. The logical statements would compare the setpoint to the input temperature and determine whether more or less heating was necessary to keep the temperature constant. A PLC output would then either open or close the hot water valve, an incremental amount, depending on whether more or less hot water was needed. Larger more complex systems can be controlled by a Distributed Control System (DCS) or SCADA system.
In practice, process control systems can be characterized as one or more of the following forms:
Discrete – Found in many manufacturing, motion and packaging applications. Robotic assembly, such as that found in automotive production, can be characterized as discrete process control. Most discrete manufacturing involves the production of discrete pieces of product, such as metal stamping.
Batch – Some applications require that specific quantities of raw materials be combined in specific ways for particular durations to produce an intermediate or end result. One example is the production of adhesives and glues, which normally require the mixing of raw materials in a heated vessel for a period of time to form a quantity of end product. Other important examples are the production of food, beverages and medicine. Batch processes are generally used to produce a relatively low to intermediate quantity of product per year (a few pounds to millions of pounds).
Continuous – Often, a physical system is represented though variables that are smooth and uninterrupted in time. The control of the water temperature in a heating jacket, for example, is an example of continuous process control. Some important continuous processes are the production of fuels, chemicals and plastics. Continuous processes, in manufacturing, are used to produce very large quantities of product per year(millions to billions of pounds).
Applications having elements of discrete, batch and continuous process control are often called hybrid applications.
Tuesday, June 5, 2007
Thursday, May 17, 2007
Raw materials
Materials are physical substances used as inputs to production or manufacturing. Raw materials are first extracted or harvested from the earth and divided into a form that can be easily transported and stored, then processed to produce "semi-finished materials". These can be input into a new cycle of production and "finishing processes to create "finished materials", ready for distribution and consumption.
An example of a raw material is cotton, which can be processed into thread, and then processed into cloth, a semi-finished material. Sewing and cutting the fabric turns it into a garment, which is a finished material. Steelmaking is another example—raw materials are extracted, refined and processed into steel, a semi-finished material. Steel is then used as an input in many other industries to make finished products.
An example of a raw material is cotton, which can be processed into thread, and then processed into cloth, a semi-finished material. Sewing and cutting the fabric turns it into a garment, which is a finished material. Steelmaking is another example—raw materials are extracted, refined and processed into steel, a semi-finished material. Steel is then used as an input in many other industries to make finished products.
Sunday, May 13, 2007
Statistical process control
Statistical Process Control (SPC) is a method of visually monitoring manufacturing processes. With the use of control charts and collecting few but frequent samples, this method can effectively detect changes in the process that may affect its quality. Under the assumption that a manufactured product has variation and this variation is affected by several process parameters, when SPC is applied to "control" each parameter the final result tends to be a more controlled product. SPC can be very cost efficient, as it usually requires collection and charting data already available, while "product control" requires accepting, rejecting, reworking and scrapping products that already went through the whole process.
History
Statistical process control was pioneered by Walter A. Shewhart and taken up by W. Edwards Deming with significant effect by the Americans during World War II to improve industrial production. Deming was also instrumental in introducing SPC methods to Japanese industry after that war. Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Dr. Shewhart drew from pure mathematical statistical theories, he understood data from physical processes never produce a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process, while others display uncontrolled variation that is not present in the process causal system at all times.[1]
General
Classical quality control was achieved by inspecting 100% of the finished product and accepting or rejecting each item based on how well the item met specifications. In contrast, statistical process control uses statistical tools to observe the performance of the production line to predict significant deviations that may result in rejected products.
The underlying assumption is that there is variability in any production process: The process produces products whose properties vary slightly from their designed values, even when the production line is running normally, and these variances can be analyzed statistically to control the process. For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of product, but some boxes will have slightly more than 500 grams, and some will have slightly less, in accordance with a distribution of net weights. If the production process, its inputs, or its environment changes (for example, the machines doing the manufacture begin to wear) this distribution can change. For example, as its cams and pulleys wear out, the cereal filling machine may start putting more cereal into each box than specified. If this change is allowed to continue unchecked, more and more product will be produced that fall outside the tolerances of the manufacturer or consumer, resulting in waste. While in this case, the waste is in the form of "free" product for the consumer, typically waste consists of rework or scrap.
By observing at the right time what happened in the process that led to a change, the quality engineer or any member of the team responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem.
SPC indicates when an action should be taken in a process, but it also indicates when NO action should be taken. An example is a person who would like to maintain a constant body weight and takes weight measurements weekly. A person who does not understand SPC concepts might start dieting every time his or her weight increased, or eat more every time his or her weight decreased. This type of action could be harmful and possibly generate even more variation in body weight. SPC would account for normal weight variation and better indicate when the person is in fact gaining or losing weight.
Bibliography
Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146-152
Deming, W E (1982) Out of the Crisis: Quality, Productivity and Competitive Position ISBN 0-521-30553-5
Oakland, J (2002) Statistical Process Control ISBN 0-7506-5766-9
Shewhart, W A (1931) Economic Control of Quality of Manufactured Product ISBN 0-87389-076-0
Shewhart, W A (1939) Statistical Method from the Viewpoint of Quality Control ISBN 0-486-65232-7
Wheeler, D J (2000) Normality and the Process-Behaviour Chart ISBN 0-945320-56-6
Wheeler, D J & Chambers, D S (1992) Understanding Statistical Process Control ISBN 0-945320-13-2
Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0-945320-53-1
History
Statistical process control was pioneered by Walter A. Shewhart and taken up by W. Edwards Deming with significant effect by the Americans during World War II to improve industrial production. Deming was also instrumental in introducing SPC methods to Japanese industry after that war. Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Dr. Shewhart drew from pure mathematical statistical theories, he understood data from physical processes never produce a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process, while others display uncontrolled variation that is not present in the process causal system at all times.[1]
General
Classical quality control was achieved by inspecting 100% of the finished product and accepting or rejecting each item based on how well the item met specifications. In contrast, statistical process control uses statistical tools to observe the performance of the production line to predict significant deviations that may result in rejected products.
The underlying assumption is that there is variability in any production process: The process produces products whose properties vary slightly from their designed values, even when the production line is running normally, and these variances can be analyzed statistically to control the process. For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of product, but some boxes will have slightly more than 500 grams, and some will have slightly less, in accordance with a distribution of net weights. If the production process, its inputs, or its environment changes (for example, the machines doing the manufacture begin to wear) this distribution can change. For example, as its cams and pulleys wear out, the cereal filling machine may start putting more cereal into each box than specified. If this change is allowed to continue unchecked, more and more product will be produced that fall outside the tolerances of the manufacturer or consumer, resulting in waste. While in this case, the waste is in the form of "free" product for the consumer, typically waste consists of rework or scrap.
By observing at the right time what happened in the process that led to a change, the quality engineer or any member of the team responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem.
SPC indicates when an action should be taken in a process, but it also indicates when NO action should be taken. An example is a person who would like to maintain a constant body weight and takes weight measurements weekly. A person who does not understand SPC concepts might start dieting every time his or her weight increased, or eat more every time his or her weight decreased. This type of action could be harmful and possibly generate even more variation in body weight. SPC would account for normal weight variation and better indicate when the person is in fact gaining or losing weight.
Bibliography
Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146-152
Deming, W E (1982) Out of the Crisis: Quality, Productivity and Competitive Position ISBN 0-521-30553-5
Oakland, J (2002) Statistical Process Control ISBN 0-7506-5766-9
Shewhart, W A (1931) Economic Control of Quality of Manufactured Product ISBN 0-87389-076-0
Shewhart, W A (1939) Statistical Method from the Viewpoint of Quality Control ISBN 0-486-65232-7
Wheeler, D J (2000) Normality and the Process-Behaviour Chart ISBN 0-945320-56-6
Wheeler, D J & Chambers, D S (1992) Understanding Statistical Process Control ISBN 0-945320-13-2
Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0-945320-53-1
Process capability
A process is a unique combination of tools, materials, methods, and people engaged in producing a measurable output; for example a manufacturing line for machine parts. All processes have inherent statistical variability which can be evaluated by statistical methods.
The Process Capability is a measureable property of a process to the specification. The output of this measurement is usually illustrated by an histogram and calculations that predict how many parts will be produced out of specification.
Two parts of process capability are: 1) Measure the variability of a process, and 2) Compare that variability with a proposed specification or product tolerance.
Contents [hide]
1 Measure the Process
2 Compare Process to a Standard
3 See also
4 External links
5 References
[edit] Measure the Process
The output of a process usually has at least one or more measureable characteristics that are used to specify outputs. These can be analyized statistically. Where the output data shows a normal distribution the process can be described by the process mean (average) and the standard deviation.
A process needs to be established with appropriate process controls in place. A control chart analysis is used to determine whether the process in "in statistical control". If the process is not in statistical control then capability has no meaning. Therefore the process capability involves only common cause variation and not special cause variation.
A batch of data needs to be obtained from the measured output of the process. The more data that is included the more precise the result, however an estimate can be achieved with as few as 17 data points. This should include the normal variety of production conditions, materials, and people in the process. With a manufactured product, it is common to include at least three different production runs, including start-ups.
The process mean (average) and standard deviation are calculated. With a normal distribution, the "tails" can extend well beyond plus and minus three standard deviations, but this interval should contain about 99.73% of production output. Therefore for a normally distribution of data the process capability is often described as the relationship between six standard deviations and the required specification.
[edit] Compare Process to a Standard
The output of a process needs to meet a customer requirement, specification, or product Tolerance (engineering). It is useful to compare the variability of the process to the intended requirement to determine its suitability.
Four indices are produced by a Capability Study, the Cp index, Pp index, Cpk index, and Ppk index. The most optimistic of these is Cp, which disregards centering, and is insensitive to “shifts and drifts” (special cause) in the data. It indicates what the capability of the process would be if no such problems existed. The most realistic estimate is Ppk, which indicates how the process really is. Because of their different properties, experienced users can compare these indices, and know what type remedial action a process needs: removal of special cause, centering, or reduction of natural variation.
In the common case, the same formula that produces Cp also produces Pp. The difference is in how standard deviation is estimated. The same is true for Ppk and the Cpk formula.
Generally, Ppk numbers below 1.0 indicate a process in need of work, while numbers as high as 1.5 indicate an excellent process.
If a process is stable and predictable, and has consistently provided high Ppk numbers for a long period, the process is a candidate for removing final inspection, which is likely to produce more defects than it finds. The process should then have the input variables controlled, and undergo periodic audits.
Perhaps the most common Capability Study error is requesting suppliers to provide just Cpk, along with shipped goods. Cpk can easily be manipulated by selectively changing the order of the data. To get spectacularly inflated values, simply sort the data. If you are to rely on a single index, then Ppk is the appropriate choice. Authoritative texts allow the substitution of Cpk for Ppk if the process is stable and predictable. This is allowed because in that case, they are equal.
References
Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0824746147
Bothe, D. R., "Measuring Process Capability", 2001, ISBN 0070066523
Godfrey, A. B., "Juran's Quality Handbook", 1999, ISBN 007034003
ASTM E2281 Standard Practice for Process and Measurement Capability Indices
Retrieved from "http://en.wikipedia.org/wiki/Process_capability"
The Process Capability is a measureable property of a process to the specification. The output of this measurement is usually illustrated by an histogram and calculations that predict how many parts will be produced out of specification.
Two parts of process capability are: 1) Measure the variability of a process, and 2) Compare that variability with a proposed specification or product tolerance.
Contents [hide]
1 Measure the Process
2 Compare Process to a Standard
3 See also
4 External links
5 References
[edit] Measure the Process
The output of a process usually has at least one or more measureable characteristics that are used to specify outputs. These can be analyized statistically. Where the output data shows a normal distribution the process can be described by the process mean (average) and the standard deviation.
A process needs to be established with appropriate process controls in place. A control chart analysis is used to determine whether the process in "in statistical control". If the process is not in statistical control then capability has no meaning. Therefore the process capability involves only common cause variation and not special cause variation.
A batch of data needs to be obtained from the measured output of the process. The more data that is included the more precise the result, however an estimate can be achieved with as few as 17 data points. This should include the normal variety of production conditions, materials, and people in the process. With a manufactured product, it is common to include at least three different production runs, including start-ups.
The process mean (average) and standard deviation are calculated. With a normal distribution, the "tails" can extend well beyond plus and minus three standard deviations, but this interval should contain about 99.73% of production output. Therefore for a normally distribution of data the process capability is often described as the relationship between six standard deviations and the required specification.
[edit] Compare Process to a Standard
The output of a process needs to meet a customer requirement, specification, or product Tolerance (engineering). It is useful to compare the variability of the process to the intended requirement to determine its suitability.
Four indices are produced by a Capability Study, the Cp index, Pp index, Cpk index, and Ppk index. The most optimistic of these is Cp, which disregards centering, and is insensitive to “shifts and drifts” (special cause) in the data. It indicates what the capability of the process would be if no such problems existed. The most realistic estimate is Ppk, which indicates how the process really is. Because of their different properties, experienced users can compare these indices, and know what type remedial action a process needs: removal of special cause, centering, or reduction of natural variation.
In the common case, the same formula that produces Cp also produces Pp. The difference is in how standard deviation is estimated. The same is true for Ppk and the Cpk formula.
Generally, Ppk numbers below 1.0 indicate a process in need of work, while numbers as high as 1.5 indicate an excellent process.
If a process is stable and predictable, and has consistently provided high Ppk numbers for a long period, the process is a candidate for removing final inspection, which is likely to produce more defects than it finds. The process should then have the input variables controlled, and undergo periodic audits.
Perhaps the most common Capability Study error is requesting suppliers to provide just Cpk, along with shipped goods. Cpk can easily be manipulated by selectively changing the order of the data. To get spectacularly inflated values, simply sort the data. If you are to rely on a single index, then Ppk is the appropriate choice. Authoritative texts allow the substitution of Cpk for Ppk if the process is stable and predictable. This is allowed because in that case, they are equal.
References
Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0824746147
Bothe, D. R., "Measuring Process Capability", 2001, ISBN 0070066523
Godfrey, A. B., "Juran's Quality Handbook", 1999, ISBN 007034003
ASTM E2281 Standard Practice for Process and Measurement Capability Indices
Retrieved from "http://en.wikipedia.org/wiki/Process_capability"
Monday, May 7, 2007
ENGINEERING THE PROCESS FLOW
Process is the assembly or manufacturing activities from raw material until finish good product. The flow process will running on assembly line by work station. Each work station must have their own process. Process set up for each workstation must considered the volume forecast & total working hours per day.
Cycle time for each workstation need to determine before plan the assembly activities to ensure that our assembly line was balance. Balancing of cycle times is very important and usefull for process control set up.
Example : Determine cycle time for PRODUCT XXX
Production/day : 10
Total working hours/day : 8
Cycle time : 10 unit production/8 hours : 0.8 hr per unit production.
Each workstation activities must be set up with cycle time 0.8 hrs. Total process activities will be done for each workstation not more or less than 0.8 hrs. The finish good product will roll out from our assembly line for each 0.8hr period time. From here size of man power to produce product xxx can be decided.
Example : Determine size of man power for PRODUCT XXX
Total man hours : 10
Total man power : Total man hours/ cycle time : 10/0.8 : 12.5 man power
Man power requred to produce product xxx with capacity 10u/day : 13 person.
Cycle time must use as mesurement tool for process control set up.
Cycle time for each workstation need to determine before plan the assembly activities to ensure that our assembly line was balance. Balancing of cycle times is very important and usefull for process control set up.
Example : Determine cycle time for PRODUCT XXX
Production/day : 10
Total working hours/day : 8
Cycle time : 10 unit production/8 hours : 0.8 hr per unit production.
Each workstation activities must be set up with cycle time 0.8 hrs. Total process activities will be done for each workstation not more or less than 0.8 hrs. The finish good product will roll out from our assembly line for each 0.8hr period time. From here size of man power to produce product xxx can be decided.
Example : Determine size of man power for PRODUCT XXX
Total man hours : 10
Total man power : Total man hours/ cycle time : 10/0.8 : 12.5 man power
Man power requred to produce product xxx with capacity 10u/day : 13 person.
Cycle time must use as mesurement tool for process control set up.
ENGINEERING THE MANUFACTURING/ASSEMBLY LINE
Assembly line must be engineering base on design capacity to ensure that the actual operation not under or over forecast. Normal practise recommended to setting the design capacity 30% more than volume required. The 30 % reservation is to fullfill the sales requirement increase more than forecast.
Design capacity should be calculate from basic/estimate process time and manhours for total manufacturing or assembly process. The working hours per year must be determine early to support the calculation of production per day and production per month.
After production capacity was setting, reverse engineering need to apply in order to draw up the basic layout for assembly line. Basic assembly line layout should considered the :
1. Total assembly process flow.
2. Raw material breakdown level.
3. Cycle time for one unit production.
4. Equipment and tool related.
5. Jigs and fixtures related.
6. Processing material required.
7. Product profile data.
8. Logistic flow.
The operation space or dimension on basic layout must be inclusive the working area, passage way, raw material racking, tool position, machine allocation, utilities required such as air point for air tool, power point for machine, and others item related to operation process flow .
From basic layout, detail technical drawing need to prepared. The technical drawings should be in individual title/item such as air piping drawing, power point drawing, process flow drawing and others. All individual technical drawing must include the small detail items to ensure that actual assembly line construction will meet the requirement setting.
Basic layout and technical drawing must review and revise(if any) to get a excelent result of assembly line design.
Design capacity should be calculate from basic/estimate process time and manhours for total manufacturing or assembly process. The working hours per year must be determine early to support the calculation of production per day and production per month.
After production capacity was setting, reverse engineering need to apply in order to draw up the basic layout for assembly line. Basic assembly line layout should considered the :
1. Total assembly process flow.
2. Raw material breakdown level.
3. Cycle time for one unit production.
4. Equipment and tool related.
5. Jigs and fixtures related.
6. Processing material required.
7. Product profile data.
8. Logistic flow.
The operation space or dimension on basic layout must be inclusive the working area, passage way, raw material racking, tool position, machine allocation, utilities required such as air point for air tool, power point for machine, and others item related to operation process flow .
From basic layout, detail technical drawing need to prepared. The technical drawings should be in individual title/item such as air piping drawing, power point drawing, process flow drawing and others. All individual technical drawing must include the small detail items to ensure that actual assembly line construction will meet the requirement setting.
Basic layout and technical drawing must review and revise(if any) to get a excelent result of assembly line design.
ENGINEERING THE MANUFACTURING PLANT
Engineering the manufacturing plant or set up new plant is the main task for production preparation. It's consequently of good sales forecast of product and must be more profitibality compared with order finish good product from others party. For example , one organisation for cars business suppose to developt manufacturing plant IF ordering the knock down (KD) more profitibality compared with ordering complete built unit (CBU) in total operation required and sales forecast in increasing position with long term period time.
After organisation decided to devolope and manage new manufacturing plant, all consideration items need to engineering for small details due to the design set up will impact to cost of investment, operation, maintenance and improvement for the future. The basic plant design will contribute to profit or loss to organisations.
The basic plant design must be well funtion for total production operation, safety to staff and environment, minimum cost for total operation and maintenance, also need to considered the future expenditure and improvement.
The raw ideas to engineering the new plant is the operation flow process and production capacity required. From here the organisation need to determine the :
1. Size of manufacturing plant.
2. Size of each building required for main operation and support.
3. Equipment and utilities.
4. Set up of total logistic flow and process flow.
5. Size of man power for total operation.
All the items mentioned will ensure that the organisation will have the RETURN OF INVESTMENT and gain the PROFIT after break event or certain period time.
After organisation decided to devolope and manage new manufacturing plant, all consideration items need to engineering for small details due to the design set up will impact to cost of investment, operation, maintenance and improvement for the future. The basic plant design will contribute to profit or loss to organisations.
The basic plant design must be well funtion for total production operation, safety to staff and environment, minimum cost for total operation and maintenance, also need to considered the future expenditure and improvement.
The raw ideas to engineering the new plant is the operation flow process and production capacity required. From here the organisation need to determine the :
1. Size of manufacturing plant.
2. Size of each building required for main operation and support.
3. Equipment and utilities.
4. Set up of total logistic flow and process flow.
5. Size of man power for total operation.
All the items mentioned will ensure that the organisation will have the RETURN OF INVESTMENT and gain the PROFIT after break event or certain period time.
Subscribe to:
Posts (Atom)