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					Data Mining Applications in
      Manufacturing

      Dr Jenny Harding
           Senior Lecturer
    Wolfson School of Mechanical &
     Manufacturing Engineering,
      Loughborough University
 Identification of Knowledge - Context

                      Intelligent
                       Support
                       Systems
                                                                Problem Areas
                                                                         Additional Facilities?

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                                                   w Des
                                    New
                                        Prod
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                                     Exis
                                          t
                                    Prod ing
                                                         g
                                                  Existin s                 Extra Resources?
                                         ucts     De sign
                                                                            New Technology?

Tools:
                                            New
Information Modelling                     Business
                                          Objectives
Process Modelling
                                                                       Redesigned Processes?
Artificial Intelligence
Simulation                                How do we find and use the
Data Mining???                              “Right” Knowledge?
Sources of Manufacturing Knowledge




                           Production Processes


         People
Expertise and Experience
                                 Processes
                              (Operational Data)
 Facts or Folklore?           Under Exploited?
Background History

Why Data Mining?
  What is the “Right” Knowledge?
     Are “experts” the only sources?
        Is there any substance in folklore?
            Or is the knowledge hidden
            somewhere else as well?
Reported Applications of DM in Manufacturing


                                                                         Harding, J A, Shahbaz, M, Srinivas
                                                     8                   and Kusiak, A, 2006, "Data Mining in
                                                     7                   Manufacturing: A Review", American
                                                     6                   Society of Mechanical Engineers




                                                         No. of Papers
                                   8
                                                     5                   (ASME): Journal of Manufacturing
                                             7       4                   Science and Engineering.
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                                   1     2           1                   Earliest - Fault diagnosis (1987)
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Reported Applications of DM in Manufacturing (2)

Summary:
 Use of data mining applications in Manufacturing is
 relatively new

 To date – most of the research done has focused on
 semi-conductor industry (fault detection and
 productivity improvement)

 To date – most of the reported research uses decision
 trees, clustering and neural networks
       Challenges for DM in Manufacturing

Why slow adoption?
 ? Investment – very wide range of tools and techniques
 and not obvious in most applications which will produce
 best results and rewards (DM preparation costs are high).
 ? Availability and quality of data – substantial data
 cleaning and pre-processing required.
 ? Combinations of expertise needed – Data Mining
 Expertise + Expertise in Processes / Technologies
 under consideration
 ? Inadequate Exploitation of Resulting Knowledge -
 “one-off” applications addressing specific problems
      Challenges for DM in Manufacturing (2)

Why slow adoption?
 ? Uncertainty and risk - there are no guarantees that
 useful knowledge will be discovered. In highly
 competitive situations high risks may be unacceptable
 ? Complexity and diversity - extremely difficult to
 devise generic data mining processes even for
 particular types of manufacturing problems
Case Studies



   Experience from literature and
   industrial case studies shows……….
An Example


                  ne
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      Th




                                               Height
                                               Height
                                               Height
                                               Height
 aa
 ab
 ac
 ad

                         Width
                                                        A simple sample
                                                            product
                                               ac
                                          ab
                                     aa
Data Cleaning
 • Very important! (But very time-consuming)
 •Iterative Process

   Process 1   Data

               Data    Data      consolidated
   Process 2                      Error-free
                      Cleaning
                      Module        table
               Data
   Process n



General Classifications          Possible Causes
Identical Records                Breakdowns / Restarts
Some Duplication in Records      Rework cycles
Confusing Records                Operator Error
Missing Values in Records        Operator Training
etc                              Etc., etc.
Example Thoughts during Cleaning

     Should Reworked records be included
   or ignored?
     Can Missing values be replaced or
   should the whole record be rejected?
      Can domain experts of more detailed
   files be consulted about replacing the
   abnormal data?
     All the replaced values need to be
   documented for future consultation.
Data Transformation

 Upper Limit   Uout
               Upper
               H-Upper
               M-Upper
               S-Upper
               Nominal
               S-Lower
               M-Lower
               H-Lower
Lower Limit    Lower
               Lout          01   02       03   04 05   06   07 08   09   10   11


  Products - split           σ         σ           σ

                         Process variables
  tolerance range for
  dimension.             Normal distributions?
  Equal divisions???     Equal divisions???
Data Mining

 A complete set of dimensional / manufacturing
 data for each product needs to be collected and
 transformed to make each record

 Regression analysis used first – are there any
 simple relationships that can be exploited?

 Several data mining approaches tried

 Association Rules the Apriori Algorithm has
 been most successful
Data Mining

 In Manufacturing - Data mining decisions should
 be made in the context of the physical realities of
 the data that is being examined.
 In very highly controlled manufacturing
 processes, unusual combinations of results
 (representing problems) may be rare.

 Support level kept very low to reduce risk of
 missing unusual but important combinations of
 manufacturing outputs
Association Rule
Used in mostly Market Basket Analysis
Apriori Property:
  “Any subset of a large itemset must be large”
Rules are in the form of
             A Ł B or A Ł (B & C) etc.
                e.g. Chips Ł Coke
•Each rule is based on certain Support and
Confidence level.
Rule Quality


Need to check Quality of the generated
rules – use Support, Confidence, Statistical
methods AND check with domain experts
that the rules make sense in the
Manufacturing Context.


Support: Support is the frequency of the occurrence of
items in a transaction or record.
Confidence: Confidence is the certainty of the
discovered rule.
Example Rules
Types of Manufacturing Rules that may be identified
 include:
Product Design Errors or Constraints
Process Errors or Constraints
 From Product Data - Rules of the form
 Good Dimension output          Ł      Good Dimension output
 Bad Dimension output           Ł      Bad Dimension output
 Good Dimension output          Ł      Bad Dimension output

 From Product and Process Data
 Process variable values that produce Good Product Results
 Process variable values that produce Poor Product Results
Example Rules
•Rules that indicate one dimension being “nominal”
 results in another dimension being “nominal” are
 reassuring – but generally not very interesting

•Rules that indicate one dimension being “nominal”
 results in another dimension being out of tolerance or
 away from “nominal” are more interesting.


Possible Causes include:-

 Design Error
 Process Error
 Process Constraints
Results
• Data Mining can be used              to   extract
  Manufacturing process limitations.
• Data Mining can be used to discover design
  constraints and then help to improve the product
  design, (if manufacturing process is acceptable).
• Data Mining can be used to improve the output
  quality by controlling the manufacturing process
  variables.
Future Considerations……….
Data Mining projects tend to be “one-off”
  applications to solve a particular problem

 But how do we make sure the acquired
 knowledge is made use of when it has been
 discovered?

Greater benefits would be gained if they were
  part of an ongoing Knowledge Reuse
  programme
     Data Mining Network
      Local Site / Process n-1                                                       Local Site / Process 2

                          Local Data                                     Local Data
                            Mining                                         Mining
                           Engines                                        Engines


                                      Local Pool of     Local Pool of
                                       Knowledge         Knowledge
     Local Databases and Files                                                   Local Databases and Files



                                                 Main Data
                                                 Warehouse
              Local Pool of
               Knowledge
                                              Main Pool of
                                                                                     Local Site / Process 1
Local Site / Process n                         Knowledge
                         Local Data
                                              Integration &             Local Data
                           Mining              DM Engine                  Mining
                          Engines                                        Engines


                                                        Local Pool of
                                                         Knowledge
      Local Databases and Files                                                  Local Databases and Files
Any Questions




       Thank You


      J.A.Harding@lboro.ac.uk

				
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