Q 557. How can Evolutionary Operation (EVOP) be used to improve a process? Explain the steps involved in implementing an EVOP experiment, and discuss some of the advantages and disadvantages of this method compared to Design of Experiments. Provide examples to support your answer.

 

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Evolutionary Operation (EVOP) is a method of process optimization that involves a trial-and-error approach to identify the optimal settings of the process variables. The method involves making small, incremental changes to the process parameters and then observing the effect of these changes on the process output. This process is repeated until the desired output is achieved.

Steps involved in implementing an EVOP experiment:

1. Define the objective: The first step in implementing an EVOP experiment is to define the objective of the process optimization. The objective can be to maximize the yield, minimize the cost, or improve the quality of the product.

2. Select the process variables: The next step is to identify the process variables that have a significant impact on the process output. These variables can be the temperature, pressure, flow rate, pH, or any other relevant parameter.

3. Define the process limits: The process limits define the range of values within which the process variables can be varied. These limits are typically set based on the operational constraints of the process and the desired output.

4. Conduct the experiment: The EVOP experiment involves making small, incremental changes to the process variables and then observing the effect of these changes on the process output. The changes are typically made using a factorial design, which involves changing one variable at a time while keeping the other variables constant.

5. Analyze the results: The results of the EVOP experiment are analyzed to identify the optimal settings of the process variables that produce the desired output. This analysis involves statistical methods, such as regression analysis and ANOVA.

Advantages and disadvantages of EVOP compared to Design of Experiments:

Advantages:

1. EVOP is a simple and cost-effective method of process optimization, requiring only a few trials to identify the optimal process parameters.

2. EVOP is particularly effective for improving existing processes, as it can identify the most critical variables and their impact on the process output.

3. EVOP can be useful for identifying interactions between the process variables that are difficult to identify using other methods.

Disadvantages:

1. EVOP is not suitable for optimizing complex processes with a large number of variables.

2. EVOP does not provide information about the relative importance of the process variables.

3. EVOP relies on trial-and-error, which can be time-consuming and may not always identify the optimal solution.

Example:

A chemical company wants to optimize the yield of a chemical reaction. The process variables that affect the yield are the temperature, pressure, and concentration of the reactants. The company conducts an EVOP experiment by varying these variables within their process limits and measuring the yield. After several iterations, the company identifies the optimal settings of the process variables that produce the maximum yield. The company can then implement these settings in their process to achieve the desired outcome.

Evolutionary Operation experiment: EVOP is SPC technique is used for industrial settings to improve the system performance- optimizing – operation conditions.

The method is helping to us making incremental changes of system input variables and Impacting on output. Again monitoring the output, and then select best combination of variables to get best output. The process is repeated process on based on real time, with the goal of continuously improving the system's performance.

Implementing an Evolutionary Operation experiment Method

1.       Define the problem: Clearly define the problem that for solution. This required process or system optimize and identifying the objectives and constraints

2.       Identify the factors: Identify the factors that affect the performance of the process or system. Example process parameters, machine setting, raw material specification, Parts specification etc.

3.       Define the experimental design: The experiment should be designed in such a way that it can show the main effects of the factors and any interactions between the factors

4.       After that we need to carry out experiment. This will involve running the process or system under different combinations of the factors and measuring the performance.

5.       After the experiment is completed, we need to analyse, which factor have maximum impact on performance of the process or system.

 

6.       Optimize the process: Based on the results of the analysis, we can implement required change for best process or system to optimize its performance.  Example Part specification / tolerance, RM change/ machine parameter

7.       Verify the results: After required changes – need to verify result of process/ system.

 

Advantages:

1.       EVOP - Identifying and reducing the variability in the process, resulting in improved process efficiency.

2.       Can be implement without using complex machine/ software

3.       Cost effective solution for process optimization as compare to other approach

4.       EVOP- Output- High Quality product

5.       EVOP process gives quick result for process optimization.

Disadvantages:

1.         The EVOP lengthy process. if the process has multiple input variables than process requires multiple iterations

2.         EVOP Limited used : it is suitable to improving a single process, and it may not be effective for complex systems with interdependent processes.

3.         Process complexity: EVOP not sutable for complex process

4.         Limited applicability: EVOP may not be effective for processes with low variability.

5.         Limited precision: EVOP may not provide precise results as it relies on trial and error testing, and the optimal solution may not be reached.

Evolutionary Operation example

Appliance Manufacturing Example

Issue: 10% of machine is rejected due to clamping Pressure and line speed

>>As per mgf process process – we find out there are 2 process parameter which are more critical for Process rejection Clamping Pressure Kg/cm2 & Line Speed cm/sec. Let us take trial run 1,2,3 as per below table and % rejection of each trial

>>Trial 4^th condition will be:  3^rd Trail+2^nd trail – 1^st trial. Then Pressure will be 10.4 and time 60sec and rejection rate 6%

>>by similar method - Trial 5^th , 6 & 7^th trial result are here

Finally we got best conditions are Clamping Pressure 10.7 Kg/cm2 & Line Speed 40 cm/sec & 0.1% rejection.

 

EVOP is a process or technique of systematic experimentation. An experimental design is established and conducted through a series of phases and cycles, through systematic changes in operating conditions. When we find a factor to be significant, we reset the operating conditions for that factor. Then, we conduct the experiment again. The process continues until no further gain is achieved.

 

EVOP is suitable when

a) There is a variation in the process performance over time

b) there are 2 to 3 major contributing factors or process variables

c) System has more number of product performance conditions

 

Disadvantages of EVOP

a) May not always find the absolute optimum, but rather a local optimum

b) takes more time to reach optimal settings compared to DOE

c) more likelihood of errors as it requires more experimental runs than DOE

 

Example

EVOP can be used to optimise a process in a manufacturing plant, where the output required is the number of defect free products. This can be achieved by variying the process parameters like temperature, pressure and time of a critical step in the process. The initial trial is conducted to establish the starting conditions for the process. The EVOP cycle involves making small changes to the process parameters such as increasing the temp by 1 degree and monitoring the impact on the output. The results are analysed and the process is repeated until the optimal conditions are achived. The optimal settings are then implemented in the production process

 

EVOP process steps

 

 

Once the process is identified

1. Determine the parameters that will be varied during the experiment, based on their potential to impact the output

2. The initial trial is conducted to establish the starting conditions. This should be based on process history and prior knowledge of the process

3. Implement the EVOP cycle making small, incremental changes to the parameters and monitoring the impact on process output.

4. Analyse results to find the optimum settings

5. Once found, implement the optimum settings in the production process

 

 

 

While all the published answers are correct, the winning answer has been provided by Amit Kumar Shukla. Well done!