1. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Ch 21 Inspection Principles and Practices Sections: 1.Inspection Fundamentals 2.Sampling vs. 100% Inspection 3.Automated Inspection 4.When and Where to Inspect 5.Quantitative Analysis of Inspection

2. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Inspection The means by which poor quality is detected and good quality is assured  Traditionally accomplished using labor-intensive methods  Sampling inspection is common  Manual inspection is usually performed after parts are already made  If defective product is produced, it is too late to correct during regular processing  Defective parts that are already made must be scrapped or reworked

3. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. New QC Approaches to Address these Problems  100% automated inspection rather than sampling inspection using manual methods  On-line sensor systems for inspection during or immediately after processing  Feedback control of sensor data to manufacturing process  Software tools for SPC  Advanced inspection and sensor technologies (e.g., CMMs, machine vision)

4. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Types of Inspection 1.Inspection for variables  One or more quality characteristics of the part or product are measured  Requires appropriate measuring instrumentation 2.Inspection for attributes  Part or product is inspected to decide whether it conforms to the accepted quality standard  Sometimes based on judgment of inspector  Sometimes uses a gage  Sometimes involves counting number of defects

5. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Inspection Procedure 1.Presentation – item is presented for examination 2.Examination – consists of measuring or gaging a quality characteristic, or searching for and counting defects 3.Decision – to accept or reject the item? 4.Action – the item is accepted or rejected  If rejected, can the item be reworked?  Additional action may include adjustments in the manufacturing process

6. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Which Features to Inspect? Key Characteristics  In general, inspecting every feature is unnecessary  Certain features are more important  Key characteristics (KCs) include:  Matching dimensions of assembled components  Surface roughness on bearing surfaces  Straightness and concentricity of high-speed rotating shafts  Finishes of exterior surfaces on consumer products such as cars

7. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Inspection Errors  Errors can occur in the inspection procedure during the examination and decision steps 1.Type I error – when a good item is classified as defective  A “false alarm” 2.Type II error – when a defective item is classified as good  A “miss”

8. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Why Errors Occur in Manual Inspection  Complexity of the inspection task  Inherent variations in the inspection procedure  Judgment required by inspector  Mental fatigue  Inaccuracies in the measuring or gaging instruments

9. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Why Errors Occur in Automated Inspection  Complexity of the inspection task  Resolution of the inspection sensor, as affected by “gain” or other sensitivity adjustments  Equipment malfunctions  “Bugs” in the computer program controlling the inspection procedure

10. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Inspection Accuracy The capability of an inspection procedure to avoid type I and type II errors  Measures of inspection accuracy:  p 1 = probability that a conforming item is classified as conforming  p 2 = probability that a nonconforming item is classified as nonconforming  Probability of inspection errors:  Probability of type I error = (1 – p 1 )  Probability of type II error = (1 – p 2 )  Actual fraction defect rate = q

11. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Inspection vs. Testing  Inspection is used to assess the quality of a part or product relative to design specifications  Testing is used to assess the functional aspects of the product  Does the product operate the way it is supposed to?  Will it operate in environments of extreme temperature and humidity?  In QC testing, the item is observed during actual operation or under conditions that might be present during operation

12. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Sampling Inspection 1.Variables sampling – the mean value of the quality characteristic of interest is compared to an allowed value  The batch is rejected if the mean value compares unfavorably 2.Attributes sampling – if the number of defects in the sample is greater than the acceptance number the batch is rejected  The allowed value or acceptance number is chosen so that the probability of rejecting the batch is small unless the quality level is indeed poor

13. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Acceptance Sampling In the construction of an acceptance sampling plan, the supplier and customer must agree on the following specifications:  Acceptable quality level (AQL)  A quality level that is less than perfect but deemed acceptable to the customer = q 0  Lot tolerance percent defective (LTPD)  A lower quality level that is deemed unacceptable = q 1

14. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Possible Statistical Errors in Sampling 1.Type I error – rejecting a batch of product that is equal to or better than the AQL  Actual q  q 0  Probability of a type I error (  ) is called the producer’s risk 2.Type II error – accepting a batch of product whose quality is worse than the LTPD  Actual q  q 1  Probability of a type II error (  ) is called the consumer’s risk  Supplier and customer must also agree on these risks

15. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Design of an Acceptance Sampling Plan  Based on the agreed upon values of AQL, LTPD, producer’s risk (  ), and consumer’s risk (  ),  The values of the sample size Q s and acceptance number (or allowed value) N a can be determined  Operating characteristic curve (OC curve) for a given sampling plan = plot of the probability of accepting the batch as a function of the fraction defect rate q in the batch  Average outgoing quality curve (AOQ curve) for a given sampling plan = plot of the mean quality level passing through the sampling plan

16. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Operating Characteristic Curve  Typical OC curve for a given sampling plan shows the probability of accepting the lot for different fraction defect rates of incoming batches  AQL = acceptable quality level (q 0 )  LTPD = lot tolerance percent defective (q 1 )

17. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Average Outgoing Quality Curve  AOQ curve for a given sampling plan shows the quality level of the batch as it exits the inspection plan  AOQL = average outgoing quality limit - the maximum average defect rate of the batch and corresponding incoming defect rate q

18. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. 100% Manual Inspection  In principle, the only way to achieve 100% acceptable quality is to use 100% inspection  Two problems with 100% inspection when performed manually 1.Inspection cost per part is applied to every part in the batch rather than a small portion of the batch (the sample) 2.Errors (types I and II) that accompany human inspection

19. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. OC Curve in 100% Inspection  Theoretically, the probability of accepting the batch in 100% inspection is 1.0 if the quality level is better than the AQL and zero if the quality level is worse than the AQL  Two problems with 100% inspection when performed manually: 1.Expense of inspecting every part 2.Errors in inspection procedure (type I and type II errors)

20. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Automated Inspection Automation of one or more steps in the inspection procedure 1.Automated presentation of parts to human inspector  Human inspector performs examination and decision steps 2.Automated examination and decision  Manual loading (presentation) and unloading 3.Complete automation of entire cycle (presentation, examination, and decision)

21. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Errors Can Also Occur in 100% Automated Inspection  Relationship between sensitivity of an automated inspection system and the probabilities of type I and type II errors  p 1 = Pr(conforming item is correctly classified)  p 2 = Pr(nonconforming item is correctly classified)

22. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. More on Automated Inspection Full potential of automated inspection is best achieved when  It is integrated into the manufacturing process  100% inspection is used  Results of the procedure lead to positive action  Feedback process control  To allow compensating adjustments in the process to reduce variability and improve quality  Parts sortation  Defects are separated from process output

23. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Action Steps in Automated Inspection Feedback process control Sortation into two or more quality levels

24. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Off-Line and On-Line Inspection  Off-line inspection – performed away from manufacturing process, usually after a time delay  On-line inspection – performed when the parts are made, either as an integral step in processing, or immediately afterward  On-line/in-process inspection – performed during the manufacturing process  On-line/post-process inspection – performed right after the process

25. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Off-Line Inspection  Performed away from the manufacturing process  Usually a time delay between processing and inspection

26. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. On-Line/In-Process Inspection  The inspection procedure is performed during the manufacturing operation  Allows for corrective action on current work unit

27. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. On-Line/Post-Process Inspection  Measurement or gaging procedure is accomplished immediately following the production process  On-line because it is integrated with the manufacturing workstation, and the results can immediately influence the production process for the next work part

28. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Product Inspection vs. Process Monitoring  On-line/in-process inspection is more feasible for process variables than for product variables  Process monitoring for product quality relies on assumption of deterministic manufacturing:  Process is in statistical control  Process capability is good  Cause-and-effect relationships between process variables and product quality are known, and mathematical models for these relationships have been derived

29. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Distributed Inspection vs. Final Inspection  Distributed inspection – when inspection stations are located along the line of work flow  Most extreme – inspection and sortation are located after every processing step  More cost effective – inspections are strategically placed at critical points in the manufacturing sequence  Final inspection – one comprehensive inspection immediately before shipment to customer  Quality conscious manufacturers combine the two approaches

30. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. Quantitative Analysis of Inspection 1.Effect of defect rate in serial production 2.Final inspection vs. distributed inspection 3.Inspection vs. no inspection

31. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. What the Equations Tell Us  Distributed inspection/sortation reduces the number of parts processed in a sequence of production operations compared to one final inspection  As the ratio of unit processing cost to unit inspection cost increases, the advantage of distributed inspection over one final inspection increases  Partially distributed inspection is less effective than fully distributed inspection in reducing waste  The “law of diminishing returns” operates in distributed inspection systems – each additional inspection station yields less savings

32. ©2008 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher. For the exclusive use of adopters of the book Automation, Production Systems, and Computer-Integrated Manufacturing, Third Edition, by Mikell P. Groover. What the Equations Tell Us  Inspections should be performed immediately following processes with a high fraction defect rate  Inspection should be performed prior to high cost processes  Either no inspection or 100% inspection is more appropriate than sampling inspection  Whichever is better depends on the relative values of inspection/sortation cost vs. damage cost (of defects that pass around the inspection plan)