Heng ChangChinese Culture University

Front Panel• Controls = Inputs• Indicators = Outputs

Block Diagram• Accompanying “program” for front

panel• Components “wired” together

LabVIEW Programs Are Called Virtual Instruments (VIs)

VI Front PanelFront Panel Toolbar

GraphLegend

BooleanControl

WaveformGraph

Icon

PlotLegend

ScaleLegend

VI Block Diagram

Wire Data

GraphTerminal

SubVI

While LoopStructure

Block Diagram Toolbar Divide

Function

Numeric Constant

Timing Function

Boolean Control Terminal

Express VIs, VIs and Functions• Express VIs: interactive VIs with configurable dialog page• Standard VIs: modularized VIs customized by wiring• Functions: fundamental operating elements of

LabVIEW; no front panel or block diagram

Express VIExpress VI Standard Standard VIVI

FunctionFunction

Controls and Functions Palettes

Controls Palette (Front Panel Window)

Functions Palette (Block Diagram Window)

ControlTerminals

Block Diagram Window

Front Panel Window

Indicator Terminals

Creating a VI

• Block diagram executes dependent on the flow of data; block diagram does NOT execute left to right

• Node executes when data is available to ALL input terminals

• Nodes supply data to all output terminals when done

Dataflow Programming

Section II – SubVIs

What is a subVI? Making an icon and

connector for a subVI

Using a VI as a subVI

Block Diagram NodesIcon Expandable Node Expanded Node

• Function Generator VI• Same VI, viewed three different ways• Yellow field designates a standard VI• Blue field designates an Express VI

SubVIs Sub VIsSub VIs

Section III – Data Acquisition

Data acquisition (DAQ) basics Connecting Signals Simple DAQ application

Computer

DAQ Device

Terminal Block

Cable

Sensors

Data Acquisition in LabVIEW

Traditional NI-DAQSpecific VIs for performing:• Analog Input• Analog Output• Digital I/O• Counter operations

NI-DAQmxNext generation driver: • VIs for performing a

task• One set of VIs for all

measurement types

Hardware Connections

BNC-2120

SCB-68

NI-ELVIS

SC-2075

Charts

Waveform chart – special numeric indicator that can display a history of values

Controls >> Graph Indicators >> Waveform Chart

Acceptance Sampling Plans Acceptance sampling plan is a

statement regarding the required sample size for product inspection and the associated acceptance or rejection criteria for sentencing each individual lot.

Graphs Selected from the Graph palette of Controls menuControls>>All Controls>>Graphs

Waveform GraphWaveform Graph – Plot an array of – Plot an array of numbers against their indicesnumbers against their indicesExpress XY GraphExpress XY Graph – Plot one array against – Plot one array against anotheranotherDigital Waveform GraphDigital Waveform Graph – Plot bits from – Plot bits from binary databinary data

Acceptance Sampling Defined

Acceptance Sampling is a form of inspection that is used to determine whether or not goods are coherent with a set standard of quality

Risks of Acceptance Sampling

Producers Risk The risk associated with a producer

rejecting a lot of materials that actually have good quality Also referred to as a Type I Error

Risks of Acceptance Sampling

Consumers Risk The risk associated with a consumer

accepting a lot of materials that actually have poor quality Also referred to as a Type II Error

Acceptance Sampling

Acceptance sampling is a process that helps to determine whether to accept or reject the sample being observed

Typical Application of Acceptance Sampling

The decision to accept or reject the shipment is based on the following set standards: Lot size = N Sample size = n Acceptance number = c Defective items = d

If d <= c, accept lot If d > c, reject lot

Acceptance Sampling

Acceptance sampling is a method used to accept or reject product based on a random sample of the product.

The most effective use of acceptance sampling is as an auditing tool to help ensure that the output of a process meets requirements.

Producers risk refers to the probability of rejecting a good lot. In order to calculate this probability there must be a numerical definition as to what constitutes “good” AQL (Acceptable Quality Level) - the

numerical definition of a good lot. The ANSI/ASQC standard describes AQL as “the maximum percentage or proportion of nonconforming items or number of nonconformities in a batch that can be considered satisfactory as a process average”

Consumers Risk refers to the probability of accepting a bad lot where: LTPD (Lot Tolerance Percent Defective) -

the numerical definition of a bad lot described by the ANSI/ASQC standard as “the percentage or proportion of nonconforming items or noncomformities in a batch for which the customer wishes the probability of acceptance to be a specified low value.

Operating Characteristic Curve The Operating Characteristic Curve

(OC Curve) is typically used to represent the four parameters (Producers Risk, Consumers Risk, AQL and LTPD) of the sampling plan as shown below where the P on the x axis represents the percent defective in the lot:

Operating Characteristic Curve for AQL=.020, α = 0.05, LTPD= 0.80, β = 0.10

Developing a Sampling Plan for Variables Control Limits

Points on an acceptance sampling chart that distinguish the accept and reject region(s).

Also, the points on a process control chart that distinguish between a process being in or out of control.

Factors to Consider in Designing a Plan The probability of rejecting a good lot (α error) The probability of accepting bad lot (β error) The size of the sample (n)

Fundamentals of Operations Management 4e© The McGraw-Hill Companies, Inc., 2003 S6–30

Establishing Control Limits for Acceptance Sampling Using Variables

Exhibit S6.13Exhibit S6.13

Fundamentals of Operations Management 4e© The McGraw-Hill Companies, Inc., 2003 S6–31

Determining the Probability of Committing a Type II error (β error)

Exhibit S6.14Exhibit S6.14

Acceptance Sampling Plans by Variables Controlling a process parameter such

as the mean or standard deviation. The desirable settings of the parameters

are such that certain conditions regarding the mean lot quality and the corresponding probability of lot acceptance are satisfied.

Acceptance Sampling Plans by Variables (cont.) Controlling the proportion of the

product that is nonconforming. Single or double specification limits and

a process variability that is known or unknown can be used to determine the plans.

Operating Characteristic (OC) Curve The OC curve plots the probability the probability of

accepting the lot against actual lot lot fraction defectivefraction defective, which displays the discriminatory power of the sampling plan.

The vendor (supplier) usually would focus on a specification of product quality, traditionally called acceptable acceptable quality level (AQL), quality level (AQL), which would yield a high probability for accepting a lot.

Operating Characteristic (OC) Curve (cont.) The consumer would also focus on another

point at the other end of the OC curve, traditionally called lot tolerance percent lot tolerance percent defective (LTPD), rejecting quality level defective (LTPD), rejecting quality level (RQL) or limiting quality level (LQL).(RQL) or limiting quality level (LQL).

Note that the AQL is a level of quality representing the specified high probability of acceptance of accepting a lot, and on the other hand a low probability of lot acceptance is given to a lot with defect level as high as the LTPD.

Controlling Process Averagesingle specification limit and known process standard deviation

Suppose the lower specification limit for a process is specified. A variable sampling plan may call for selecting a random sample of size n from the lot. The sample average is then calculated. If the sample average is less than the acceptance limit , the lot is rejected; otherwise, the lot is accepted.

aX

OC Curve of Controlling Process Average

Controlling the Proportion Nonconforming of a Lot The probability of rejecting lots of a good

quality level for which the proportion nonconforming is p p11 should be equal to αα. The probability of accepting lots of a poor quality level for which the proportion nonconforming is pp22 should be equal to ββ.

With Form 1, the standard normal deviate, ZL or ZU, is compared to a critical value k. If ZL or ZU is greater than or equal to k, the lot is accepted; otherwise, the lot is rejected.

OC Curve of Lot Proportion Nonconforming

Conclusions

Acceptance sampling plan by variables consists of specifying the sample size n and an acceptance criterion.

Generally, the acceptance probability is independent of the variance and sample size.

And, the sample size is independent of the variance.

Discussion, Q&A