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Control charts

 

Many manufacturing industries and huge businesses use control charts to figure out whether their processes happen in a controlled statistical state or not. Control charts are process-behavior charts, commonly known as Shewhart charts, plotted against time. They show the progress of a process over time.

The future outcomes are predicted from the data applied on these control charts. These charts have a central line of average, a lower control limit line, and an upper limit line to track the performances of the business processes. If any changes or corrections are needed to control the parameters of a process, they can be tracked via control charts. If no such correction is needed, this implies that the work progress is stable.

Types of control charts

Chart

Process observation

Process observations relationships

Process observations type

Size of shift to detect

 
and R chart

Quality characteristic measurement within one subgroup

Independent

Variables

Large (≥ 1.5σ)

 x̄ and s chart

Quality characteristic measurement within one subgroup

Independent

Variables

Large (≥ 1.5σ)

Shewhart individuals control chart (ImR chart or XmR chart)

Quality characteristic measurement for one observation

Independent

Variables

Large (≥ 1.5σ)

Three-way chart

Quality characteristic measurement within one subgroup

Independent

Variables

Large (≥ 1.5σ)

p-chart

Fraction nonconforming within one subgroup

Independent

Attributes

Large (≥ 1.5σ)


 

Chart

Process observation

Process observations relationships

Process observations type

Size of shift to detect

 
and R chart

Quality characteristic measurement within one subgroup

Independent

Variables

Large (≥ 1.5σ)

 x̄ and s chart

Quality characteristic measurement within one subgroup

Independent

Variables

Large (≥ 1.5σ)

Shewhart individuals control chart (ImR chart or XmR chart)

Quality characteristic measurement for one observation

Independent

Variables

Large (≥ 1.5σ)

Three-way chart

Quality characteristic measurement within one subgroup

Independent

Variables

Large (≥ 1.5σ)

p-chart

Fraction nonconforming within one subgroup

Independent

Attributes

Large (≥ 1.5σ)

np-chart

Number nonconforming within one subgroup

Independent

Attributes

Large (≥ 1.5σ)

c-chart

Number of nonconformances within one subgroup

Independent

Attributes

Large (≥ 1.5σ)

u-chart

Nonconformances per unit within one subgroup

Independent

Attributes

Large (≥ 1.5σ)

EWMA chart

Exponentially weighted moving average of quality characteristic measurement within one subgroup

Independent

Attributes or variables

Small (< 1.5σ)

CUSUM chart

Cumulative sum of quality characteristic measurement within one subgroup

Independent

Attributes or variables

Small (< 1.5σ)

Time series model

Quality characteristic measurement within one subgroup

Autocorrelated

Attributes or variables

N/A

Regression control chart

Quality characteristic measurement within one subgroup

Dependent of process control variables

Variables

Large (≥ 1.5σ)

 


 

Features of control charts

1. The points in the control charts represent statistical measurements of the data like mean, proportion, range, etc.
2. Mean is calculated for all the samples of the data in control charts for analysis.
3. At the mean, a centre line is drawn.
4. Find the standard deviation for all the samples.
5. Upper and lower limits in the control charts indicate favorable and unfavorable situations of the processes for which the standard deviation was found.

Uses of control charts

1. Controlling limits with the help of control charts is more accessible in engineering tolerance.
2. Specification limits can also be controlled to focus on the main operation with maximum outputs.
3. Control charts help to detect events that change whenever an actual process changes.
4. It helps to eliminate harmful elements that hinder the progress of the work during manufacturing. v 5. Engineers can detect the systematic patterns and suggest introduction in a process known as special-cause variation. This is done to add warning limits in the control charts that can improve the quality of a product.

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