Management of Product Quality Data in Engineering by GAJULA SHASHI KIRAN (206747) Data Management for Engineering Applications 1

Introduction Data Quality: “Degree of excellence exhibited by the data”. “Complete, standards based, consistent, accurate and time stamped”. “Backbone for the integrity of the data management” “The processes and technologies involved in ensuring the conformance of data values to business requirements and acceptance criteria”. 2

Attributes of Data Quality 3

Attributes of Data Quality(1) 4 Involves describing various categories of desirable attributes(dimensions) of data. High-quality data needs to pass a set of quality criteria. Those include – Accuracy – Integration – Validation – Completeness – Relevance – Consistency

Attributes of Data Quality(2) Accuracy: An aggregated value over the criteria of integrity, consistency, and density Integrity: An aggregated value over the criteria of completeness and validity Completeness: Achieved by correcting data containing anomalies Validation: process of ensuring that program operates on clean and useful data Consistency: Concerns contradictions and syntactical anomalies Relevancy: a level of consistency between the data content and the area of interest of the user. 5

Product Quality Data(PDQ) 6 PDQ is a field of PLM relating to the quality of product data Different types of product data – Geometrical Data and CAD – Complex Product Structures – Non-geometrical Data, Simulation, FEM, etc. Particularly focus on the geometrical and organizational quality of CAD data CAD data participates in all the stages of PLM processes

Problems in CAD: Main problems are caused due to Dissimilar software systems Lost data Inconsistent product versions Poor communication between CAD,CAM,CAE CAD model quality problems Due to inherent flaws in modeling software itself. 7

Fig - The CAE Process without Interoperability 8

Common Types of Model Quality Problems Fig- Types of Model Quality Problems 9

CAPVIDIA SOFTWARE It mainly focuses on – Software product development, – Engineering Services (CAD/CAE) Technology focuses on CAD Data Translation, Repair & Healing, Validation It comprises of three stages – Verification – Validation – Comparison 10

„ Verification – Impedes reuse of native model in most CAD processes – Require geometry changes during CAE/CAM model reuse. – Unrealistic features can cause divergence between CAE and CAM models Validation – Introduced during translation, migration, remastering or archiving. – Introduced during rework for CAE/CAM reuse Comparison – Unintentional changes between design revisions or for an engineering change order – Unintentional changes caused by complex parametric relationships unknown to user 11

Design Verification 12

Translate validation : Verify native model for downstream Validate that translated model has equivalent quality and shape Identify process issues for support to resolve 13

Source:ITITranscen Data 14

Source:ITITranscen Data 15

CADIQ Functions: It compares geometry assembly structure, design features and product manufactring information It identifies model based design Data quality issues It is easily integrated into PLM workflow processes Fix topology and geometry problems within CAD CADfix Functions: Interoperability tool Transfers geometry data Repairs data according from given source system to get use in target system 16

17

18

19

20

21

22

23

24

Conclusion By maintaining data quality we meet the operational needs Improve the customer service 25

References The Impact of Poor Data Quality on the Typical Enterprise, Communications of the ACM Research in attacks, intrusions, and defenses 16th international symposium, RAID 2013, Rodney Bay, St. Lucia, October , 2013 ; proceedings

Thanking you 27

Queries???? 28