12-crs-0106 revised 8 feb 2013 data analytics in electronics manufacturing ieee nsw section stefan...

Data Analytics in Electronics Manufacturing

IEEE NSW Section

Stefan Mozar

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Overview

The aim of this presentation is to show how Data

Analytics can be used to make improvements in

manufacturing, and the impact that engineering

has on the process.

Introduction

To illustrate the application of data analytics, an example will be use to illustrate an application.

Such an example is testing of electronic printed circuit board assemblies (PCA). Board testing is disruptive on the manufacturing flow.

Test engineers generally try and test as much as possible to verify a PCA is good.

Testing a PCA, typically takes much longer than the assembly process.

Thus PCAs are first completely assembled, and tested later.

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Causes of Failure

Failure

Materials

AssemblyDesign

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How Analytics can Help

Industry 4.0

Big Data or Cloud Computing will help predict the possibility to increase productivity, quality and flexibility within the manufacturing industry and thus to understand advantages within the competition.

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Using Analytics

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Forrester Wave TM

The Manufacturing Process

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Results from Manufacturing

Field Data

Design Data

Reliability

Safety

Pilot Run

What are the Data Sources Available?

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TestStrategy

Design Evaluation

Pilot Run

Reliability

Safety

Test Specifications

Screening with Simulation

Monte Carlo Simulation– It can predict production yield

– It can isolate design form process

– It provides a lot of data

– The confidence interval can be stated

– No data preprocessing required

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Simulation Steps

1. Develop an equation to calculate tolerances

2. Identify tolerance for each component

3. Include random number generator

4. Run simulation to see if spread falls within range

5. Take further action if required.

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Sample of Monte Carlo Analysis

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The Next Step …

where

Process or design capability analysis tell us about robustness.

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Additional Statistical Methods

A variety of statistical methods can be applied

Six Sigma Techniques

Optimization Models

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Application to Failure Detection

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Design Evaluation

Test Specifications

Safety

Reliability

Pilot Run

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Redundancy Check

Revised Test Specification

Production Data

High Volume Process

Conclusion

This method is best suitable for high volume production

Be careful as simulation on its own can not detect all potential problems

Data Analytics is a game changer which is turning Research & Development work into a data centric discipline.

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Any Questions?

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