Automatic Supervision in Manufacturing

ISBN-13: 9781447134602 / Angielski / Miękka / 2012 / 283 str.

Maciej Szafarczyk

Opis

Automation is a predominant objective in the development of modern and advanced manufacturing production. Automatic Supervision in Manufacturing (ASM) addresses unavoidable disturbances occurring during production. Its application results in the unmanned functioning of manufacturing systems through comprehensive and reliable supervision. Automatic Supervision in Manufacturing is a collection of contributions written by specialists in the field from Europe and the USA. It deals with the concept of automatic supervision, the classification of supervisory systems and their functions. This publication will be of great interest to researchers and engineers in the areas of production and manufacturing.

Spis treści

1. Principles of Automatic Supervision in Manufacturing and Classification of Supervisory Systems.- 1.1 Introduction.- 1.2 Quality of Products and Quality of Production.- 1.3 Disturbances, Adaptation and Supervision.- 1.4 Classification of Disturbances and Supervisory Functions.- 1.5 Monitoring, Diagnosing, Supervising.- 1.6 Automatic Supervisory Systems.- 1.7 Monitoring and Measurements.- 1.8 Supervisory Actions and Control Strategy.- References.- 2. Automatic Supervision in Turning and Boring.- 2.1 Introduction.- 2.2 Control of the Machining Process.- 2.2.1 The Process and the Machine Tool.- 2.2.2 Safety Systems.- 2.2.3 Adaptive Control with Constraints.- 2.2.4 Adaptive Control with Optimization.- 2.2.5 Geometrical Adaptive Control.- 2.2.6 Existing Experimental Systems.- 2.3 Future Needs and Developments.- Further Reading.- 3. Automatic Supervision in Milling.- 3.1 Introduction: Basic Formulations.- 3.2 General Discussion.- 3.3 Description of Individual Systems.- 3.3.1 Force Overload: Fast Adaptive Control.- 3.3.2 Torque Overload: Spindle Stall.- 3.3.3 Milling Cutter Tooth Breakage Detection.- 3.3.4 Chatter Recognition and Control (CRAC).- 3.3.5 Resonant Forced Vibrations in Finish Milling.- References.- 4. Automatic Supervision of Surface Grinding.- 4.1 Introduction.- 4.2 Quality Parameters and Closed-loop Quality Control of Precision Machining.- 4.3 High-accuracy Closed-loop Position Control for the Feed Axis of a CNC Surface-grinding Machine.- 4.4 In-process Testing of Workpiece Geometry.- 4.5 In-process Eddy Current Crack Testing.- 4.6 Diagnosis of Working Ability.- 4.7 Outlook: Manufacturing with Closed-loop Quality Control.- 4.8 Summary.- Further Reading.- 5. Automatic Supervision in Physical and Chemical Machining.- 5.1 Physical and Chemical Machining Processes.- 5.2 Needs for Automatic Control in P&C Machining.- 5.3 Control Loops in P&C Machining.- 5.3.1 Tool Feed Systems.- 5.3.2 Numerical Motion Control.- 5.3.3 Process Supervision: Safety Control and Autonomous Machining.- 5.3.4 Process Optimization.- 5.4 Adaptive Control in P&C Machining.- 5.4.1 Short Historical Overview.- 5.4.2 Sensing Problems and Sensing Parameters.- 5.4.3 Sensing Parameters and Type of AC Control.- 5.4.4 Optimum Search Algorithms for ACO.- 5.4.5 Knowledge-based Adaptive Control in P&C Machining.- 5.4.6 Results Obtained with Adaptive Control in P&C Machining.- 5.5 Conclusion.- References.- 6. Automatic Supervision in Metal-forming.- 6.1 Introduction.- 6.2 Influence of Press Type on Supervisory Action.- 6.3 Working Cycle Supervision.- 6.3.1 The Press-Tooling-Workpiece System and its Disturbances.- 6.3.2 Choice of Supervisory Parameter.- 6.4 Case Study: Backward Extrusion.- 6.4.1 Process Description.- 6.4.2 Bases for Supervision Strategy.- 6.4.3 Supervision Strategy.- 6.4.4 Teaching Stage.- References.- 7. Assembly Process Supervision within Flexible Automatic Assembly Systems.- 7.1 Introduction.- 7.1.1 Background.- 7.1.2 Steps to Take — an Overview.- 7.2 Background Work.- 7.3 General Description of the Error Types.- 7.3.1 What is an Error?.- 7.3.2 FAA as an Open System — External and Internal Errors.- 7.3.3 Errors Classification.- 7.4 The Process.- 7.4.1 Process Control.- 7.4.2 Task Execution.- 7.4.3 Motion Control.- 7.5 Error Detection and Analysis.- 7.5.1 Sensors.- 7.5.2 Choosing a Sensor.- 7.5.3 Action Alternatives and Historical Backing.- 7.5.4 Error Classification Schemes.- 7.6 Error Recovery.- 7.6.1 Philosophy.- 7.6.2 Adaptability.- References.- Further Reading.- 8. Automatic Supervision of Machine Tools.- 8.1 Introduction.- 8.2 Generic Structure of ASMT.- 8.3 Generation of Prognostic Parameters.- 8.4 System and Signal Analysis.- 8.5 Example ASMT Systems.- 8.6 Examples of Signal and System Analysis.- 8.7 Conclusions.- Acknowledgments.- References.- 9. Automatic Supervision of Control Systems.- 9.1 Diagnosis.- 9.1.1 Introduction.- 9.1.2 Diagnosis of Control-External and Internal Faults.- 9.1.3 Methods for Automatic Diagnosis and Supervision.- 9.2 Diagnosis Processes.- 9.2.1 Diagnosis of Control-Internal Faults.- 9.2.2 Diagnosis Configuration.- 9.2.3 An Example of the Implementation of Test Functions into a Control.- 9.2.4 Special Methods for Software Control.- 9.2.5 Diagnosis of Control-External Faults.- 9.2.6 Supervision of Modules with Logical Functions.- 9.2.7 Supervision of Functions for Process Behaviour by Means of Continuous Variables.- 9.2.8 Function Model.- 9.2.9 Parallel Model.- 9.3 Teleservice System for Machine Tools with ISDN.- 9.3.1 Problems.- 9.3.2 General Characteristic Data of ISDN.- 9.4 Use of Expert Systems.- References.- 10. Signal Processing for Automatic Supervision.- 10.1 Background on the Developing Needs of Sensors and Signal Processing in Manufacturing Automation.- 10.2 Signal Processing, Feature Extraction and Sensor Fusion.- 10.2.1 Introduction.- 10.2.2 Intelligent Sensor Defined.- 10.2.3 Sensor Fusion Defined.- 10.2.4 Fusion Methodologies.- 10.3 Applications of Signal Processing and Sensor Fusion.- 10.3.1 Introduction.- 10.3.2 Background on Acoustic Emission from Metal Cutting.- 10.3.3 Determination of Chip-Forming States using a Linear Discriminant Function Technique with Acoustic Emission.- 10.3.4 Tool Wear Detection using Time Series Analysis of Acoustic Emission.- 10.3.5 Sensor Integration using Neural Networks for Intelligent Tool Condition Monitoring.- 10.4 New Signal Processing Techniques for Automatic Supervision.- 10.5 Conclusion.- Acknowledgements.- References.- 11. Geometrical Adaptive Control of Manufacturing Systems.- 11.1 Introduction.- 11.2 Quality of Machined Parts.- 11.2.1 Quality of Material Properties.- 11.2.2 Quality of the Geometry and Surface of the Parts.- 11.3 Transformation of the Parameters of Geometric Quality during Machining.- 11.4 Quantitative Description of the Geometrical Quality of a Part.- 11.4.1 Dimensional Characteristics.- 11.4.2 Shape Characteristics.- 11.4.3 Surface Characteristics.- 11.5 Modelling of Dimensional Accuracy.- 11.6 Dimensional Accuracy Transfer Function.- 11.7 Geometrical Adaptive Control (GAC) System.- 11.8 Conclusions.- References.- Glossary: Terms, Abbreviations and Definitions.- M. Szafarczyk.