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Process engineering
This document discusses process engineering and optimization. It covers topics like experience-based planning, decision tables, process capability analysis, basic machining calculations, and process optimization. The goal is to select process parameters like feed rate, cutting speed, and depth of cut to maximize material removal rate while balancing factors like tool life, machining time, forces and power requirements. Both single-pass and multi-pass models are considered.
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Process engineering
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- 2. Contents 3.0 Introduction 3.1 Experience-Based Planning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 3. 3.0 Introduction 3.1 Experience-Based Planning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 4. Introduction • Manufacturing • Raw material → Finished product • Process capability ismachine tools scientific knowledge for = historic and ?? each process (≠machine tools) historic Process Process Capability Scientific knowledge i-Design Lab.
- 5. Process Capability • Threelevels of Process Capability Universal Shop Machine -level -level -level • Important parameters • The shapes and sizes • The dimensions and geometric tolerances • The material removal rate • The relative cost • Other cutting characteristics/constraints i-Design Lab.
- 6. 3.0 Introduction 3.1 Experience-BasedPlanning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 7. Experience-Based Planning "The accumulationof experience is knowledge " • Problem of Experience-Based • requires a significant period of time to accumulate • represents only approximate, not exact knowledge • is not directly applicable to new processes or new systems • Machinist Handbooks • has long been a standard manufacturing practice i-Design Lab.
- 8. 3.0 Introduction 3.1 Experience-BasedPlanning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 9. Decision Tables andDecision Trees • Describing the actions associated with conditions • Help systematize decision making • Translate each other • Difference • Ease and elegance of presentation and programming when a computer is used Condition Action Stub Entries i-Design Lab.
- 10. Decision Table i-Design Lab.
- 11. Decision Table • Whenconstructing, consider factors • Completeness, Accuracy, Redundancy • Consistency, Loops, Size • Merge Merge i-Design Lab.
- 12. Decision Table • Tablesplitting and parsing i-Design Lab.
- 13. Decision Tree • Singleroot, Node, Branch • Branch – ‘IF’, branches in series – ‘AND’ Node Branch Root i-Design Lab.
- 14. 3.0 Introduction 3.1 Experience-BasedPlanning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 15. Information Required toMake the Decision Shape Size Limitation Capability Tolerance Surface Finish Cutting Force Limitation Power Consumption i-Design Lab.
- 16. Process Boundaries • Oneway to represent process capability • Limiting size, tolerances, surface finish • System-dependant i-Design Lab.
- 17. 3.0 Introduction 3.1 Experience-Based Planning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 18. Feed and FeedRate • Feed • The relative lateral movement between the tool and the workpiece during a machining operation (= thickness of the chip) • Feed in turning and drilling • The advancement of the cutter per revolution of the workpiece (turning) or tool (drilling) • Unit - ipr (inch per revolution) • Feed in milling • The advancement of the cutter per cutter-tooth revolution • Unit - inch per revolution per tooth • Feed rate - ipm (inch per minute) • Equation (3. 17) i-Design Lab.
- 19. Machining • Cutting Speed • The maximum linear speed between the tool and the workpiece • Equation (3. 18) • Depth of cut • Width of the chip • Equation (3. 19) • Metal-Removal Rate • How fast material is removed from a workpiece • Equation (3. 20) ~ (3. 28) Short processing time MRR is Large ( ) Short the life of cutter i-Design Lab.
- 20. Machining Time • Totalamount of time • Parameter • The length of the workpiece • Overtravel of the tool for clearance • The number of passes required to clear the volume • Equation (3. 29) ~ (3. 31) i-Design Lab.
- 21. Tool Life • Erosion(Wear) • Crater wear • High Temperature • Flank wear • Friction • Breakage (Catastrophic Failure) • F. W. Taylor • Tool-life Equation • Relation of Tool life and Cutting speed i-Design Lab.
- 22. Machining Force andPower Requirements • Important considerations in selecting process parameters (feed, speed, and depth of cut) • Not limiting values • Machining force • Equation (3. 35) ~ (3. 37) • Cutting power • Equation (3. 38) ~ (3. 39) i-Design Lab.
- 23. Process Parameters • Feed,Speed, Depth of cut • Process selection becomes an iterative procedure • Process Selection • Machining parameters are adjusted to accommodate the system constraints • Parameters affects the time and cost i-Design Lab.
- 24. 3.0 Introduction 3.1 Experience-Based Planning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 25. Process Optimization Short processing time MRR is Large ( ) Short the life of cutter • Tool has been worn → Replace • Trade-off between increased machining rate and machine idle time i-Design Lab.
- 26. Single-Pass Model • Assumethat only one pass to produce the required geometry • The depth of cut is fixed • Constraint • Spindle-speed constraint • Feed constraint • Cutting-force constraint • Power constraint • Surface-finish constraint • Equation (3. 40) ~ (3. 47) i-Design Lab.
- 27. Multipass Model • Assumption of single-pass model is relaxed • Can be reconstructed into a single-pass model • The depth of cut is a control variable • Constraint • Spindle-speed constraint • Feed constraint • Cutting-force constraint • Power constraint • Surface-finish constraint • Depth-of-cut constraint • Equation (3. 63) ~ (3. 67) • No general solution method i-Design Lab.
- 28. 3.0 Introduction 3.1 Experience-Based Planning 3.2 Decision Tables and Decision Trees 3.3 Process-Capability Analysis 3.4 Basic Machining Calculations 3.5 Process Optimization 3.6 Conclusion i-Design Lab.
- 29. Conclusion • Information ofprocess-planning system • Design knowledge – Chapter#2 • Process knowledge – This Chapter (Chapter#3) • Process planning • Procedure that matches the knowledge of the processes with the requirements of the design • Process Capability • Decision logic i-Design Lab.