Cost optimization in machining Tool wear monitoring Tool wear monitoring Practical tool wear metrology Practical tool wear metrology Continuous optimization Continuous optimization Process stability monitoring Process stability monitoring Acoustic and vibration monitoring Acoustic and vibration monitoring LabVIEW based signal processing LabVIEW based signal processing

Sponsor: General Dynamics - OTS Coach: Dr. Tim Dalrymple Liaison Engineer: Mr. Keith Brown William Dressel (ISE) Kevin Pham (EE) Steven Stone (CSC) Sean Sullivan (ME) Phan Vu (ME)

MACHINELOGIC Pipe Coupling

 Minimize work piece cost Determine tool cost ○ Monitor wear and end of life ○ Implement practical metrology Determine machining cost Balance the process to minimize cost blackbetty420.com Project Goals & Objectives MACHINELOGIC

Project Goals & Objectives  Provide feedback to digital manufacturing framework Develop data acquisition system Automate data and error logging Monitor machine stability: chatter detection MACHINELOGIC

Minimizing Work Piece Cost C p = C fix + C m + C t C p = Cost per part C fix = Fixed cost associated with the cost of the material C m = Machining cost C t = Tooling cost related to tool life and tool change time T = C (v) p (f r ) q T = Tool life v = Cutting speed f r = Feed rate C, p, and q = Constants MACHINELOGIC

Minimizing Cost Procedure Rearrange Cost per part equation: Take partial derivatives: Optimal cutting speed: MACHINELOGIC

Determining Tool Life Through Flank Wear Width Microscope: Dino-Lite ® Wyko Profilometer Device Cost: $400 Device Cost: $180,000 MACHINELOGIC

Tool Wear Analysis Results MACHINELOGIC

Calculating Optimum Machining Parameters NominalSuggestedOptimal MACHINELOGIC

Machining Controller Solution INPUT SYSTEM OUTPUT Human Machine Interface Data Acquisition System Computer LabVIEW Analyze Data Human Machine Interface Log Data MACHINELOGIC Power Vibration Audio

OKUMA LC-40 Lathe Load Controls UPC CM100 MicrophoneKistler Accelerometer MACHINELOGIC

Prototype GUI MACHINELOGIC

Chatter Detection: Variance MACHINELOGIC

Chatter Detection: FFT  Model boring bar as fixed-pinned cylinder  Calculate natural frequency  1 st mode = 3047 Hz  Sample signal at 10 kHz  Nyquist frequency of 5 kHz MACHINELOGIC

Chatter Detection: FFT MACHINELOGIC

Signal Process Analog Filtering  Blue – Sampled Frequency  Red - Aliased Frequencies MACHINELOGIC

Sallen-Key Gain in passband: 1 Gain at cutoff (7kHz): 1/2 Design & Test: Low Pass Filter MACHINELOGIC

Low Pass Filter Results MACHINELOGIC

1 Based on 10 hour shift 2 Based on one shift per day, 235 work days per year Roughing Operations Optimized with Suggested Machining Parameters All Operations Optimized Return on Investment MACHINELOGIC

1 Based on 10 hour shift 2 Based on one shift per day, 235 work days per year Roughing Operations Optimized with Suggested Machining Parameters All Operations Optimized Return on Investment MACHINELOGIC

1 Based on 10 hour shift 2 Based on one shift per day, 235 work days per year Roughing Operations Optimized with Suggested Machining Parameters All Operations Optimized Return on Investment MACHINELOGIC

Conclusion  Cost savings achieved through higher cutting speeds  Limited by stability issues Data acquisition system can help address stability issues Data acquisition system can help address stability issues Acoustic data more suitable for detecting chatter Acoustic data more suitable for detecting chatter MACHINELOGIC

Recommendations for Future  Develop alternative for LabVIEW  Store logged data in database  Automatically handle chatter through lathe control panel  Continue tool wear analysis Automate tool wear measuring process  Continue power data analysis MACHINELOGIC

Questions?

Special Thanks to:  IPPD Program  General Dynamics  Dr. Dean Bartles  Dr. Keith Stanfill  Mr. Keith Brown  Dr. Tim Dalrymple  Dr. John Schueller  Mr. Gun Lee