Macro to nano control in plastics molding David Kazmer, PE, PhD Professor, University of Massachusetts Lowell October 31 st, 2008

Great Things for Akron Goodyear Headquarters to stay Prof. Kennedy’s 100 patents Dean Cheng to National Academy of Engineering Polymer Engineering is vital

Is U.S. Manufacturing in Decline?

U.S. Manufacturing Productivity

Manufacturing Competitiveness Manufacturers need 1.5% annual productivity gains to remain competitive Cost Category Typical Plant Overseas Plant Automated Plant Direct materials (resin, sheet, fasteners, etc.) Indirect material (supplies, lubricants, etc.)0.03 Direct labor (operators, set-up, supervisors, etc.) Indirect labor (maintenance, janitorial, etc.) Fringe benefits (insurance, retirement, vacation, etc.) Other manufacturing overhead (rent, utilities, machine depreciation, etc) Shipping (sea, rail, truck, etc.) “Landed” product cost

Manufacturing Competitiveness DLH Industries Canton, OH Fawer Visteon Changchun, China ObsoleteCompetitive

Some Manufacturing Research Macro control –Real time polymer melt pressure control Nano control –Polymer self-assembly with a functionalized substrate

The Molding Process

Conventional Molding Barrel Heaters Reciprocating Screw Check valve Injection Cylinder Clamping Cylinder Operator Interface Stationary Platen Moving Platen Mold Pellets Polymer Melt Process Controller Hydraulic Power Supply Clamping Unit Injection Unit Tie Rods Limited control –Static mold geometry –Open loop process w.r.t. polymer –So use simulation to optimize design

Dynamic Feed System to control polymer melt in real time –Sensors to monitor pressure –Movable valve to adjust flow restriction –Servo control of valve position from closed loop controller

Dynamic Feed

Two primary issues –Cost Pressure transducers for feedback control Hydraulic servovalves or large servomotors Increased size of mold components –Reliability Pressure transducer longevity & drift Hydraulic hoses & cylinders –Too much control energy

Self-Regulating Valve Design Two significant forces: –Top: control force –Bottom: pressure force Forces must balance –Pin moves to equilibrium –Melt pressure is proportional to control force –Intensification factor related to valve design –With high intensification ratio, able to: »Use low cost pneumatic or motors »Eliminate pressure transducers & controller

3D Flow Analysis

Pin Positioning

Scaling Laws

Validation All validation was performed with a two cavity hot runner mold –Mold Masters Ltd (Georgetown, Ontario) Mold produced binder separators –1.8 mm thick by 300 mm long –10 g weight Three control schemes investigated –Convention molding –Open loop control –Closed loop control with pressure feedback

Open Loop Pressure Control

Process Sensitivities Conventional Molding Open Loop Melt Valve Use of valves reduced both machine sensitivity (main effects) and intra-run variation (whiskers)

Product Consistency Significant increase in process capability index

Flexibility Example Switch mold inserts to make different cavities –Varying sizes & thicknesses Use pressure valve to control weights & size

Large Cavity Control Adjustments 2, 5, & 6 made for large cavity –More melt flow and cavity pressure

Small Cavity Control Adjustments 1, 3, 4, & 6 made for small cavity –High melt flow rate but lower maximum pressure

Pressure (MPa) Time (s) Pressure Profile Phasing The filling of each cavity may be offset in time By offsetting pressures, the moment of maximum clamp force is offset Slight extensions in cycle time can yield drastic reductions in clamp tonnage

Machine Optimization Machine requirements can be greatly reduced by optimizing and decoupling each zone       

Summary The concept of adding degrees of freedom to polymer processing is very powerful –Real-time melt control is one example –Many other examples exist

Some Manufacturing Research Macro control –Real time polymer melt pressure control Nano control –Polymer self-assembly with a functionalized substrate

Flory-Huggins Free Energy The bulk free energy  i : lattice volume fraction of component i –  ij : interaction parameter of i and j –m i : degree of polymerization of i –R : gas constant –T : absolute temperature Phase diagram of ternary blends

Unguided Template directed assembly Highly ordered structures Polymer A Polymer B Template Guided Polymer Assembly

Fundamentals The total free energy of the ternary system (Cahn-Hilliard equation), –F : total free energy –f : local free energy –C i : the composition of component i –  i  : the composition gradient energy coefficient

The mass flux, J i is: –C i : Composition of component i –M i : is the mobility of component i –  i : is the chemical potential of component i This leads to a system of 4 th order PDEs: Mass Flux Fundamentals

Numerical Method Discrete cosine transform method for PDEs – and are the DCT of and – is the transformed discrete Laplacian,

Simulation Parameters

Validation Experiments Chemically heterogeneous substrate on Au surface –Ebeam lithography followed by self-assembly of alkanethiol monolayer –Hydrophylic strips covered by 11-Amino-1-undecanthiol (NH 2 ) –Hydrophobic strips covered by 1-octadecanethiol (ODT) Ternary system of polymers used –Polyacrylic acid (PAA): Negative static electrical force –Polystyrene (PS): Hydrophobic –Dimethylformamide (DMF): Solvent, on the order of 98% volume Experimental procedure –Polymer solution placed on substrate by pipette –6 minutes quiescence at room temperature and low humidity –Polymer solution spin coated at varying RPM for in 30 seconds

Validation Experiments Investigated factors –Spin coating RPM: 100, 3000, and 7000 RPM –Pattern substrate width: 100 to 1000 nm –PS/PAA ratio: 30/70, 50/50, 70/30 –PAA molecular weight: 2k, 50k, 450 k Image acquisition –Field emission scanning electron microscopy (JEOL 7401) –Atomic force microscopy (non-contact mode, Veeco NanoScopella) –Fourier transform analysis (PSIA, v. 1.5) Model parameters then tuned by inspection of experimental and simulation results

Evolution of Domain Size, R –The domain size, R(t), is proportional to t 1/3

Phase Separation with Solvent Evaporation L z =L 0 -  L·exp(-a*t), where t is the time, a is a constant, and L z is the thickness of the film at time t, and L 0 is the thickness at t=0 Polymer 1Polymer 2Solvent Time

Determination of M and  After comparison of the simulation and the experimental results  M=3.63· m 5 /(J*s)   =1.82·10 -7 J/m Experimental condition: Spin coating speed: 3000 rpm Time: 30 seconds Solvent w%: 99% PS/PAA (weight) : 7:3 Characteristic length, R=0.829  m Experiment

Different Pattern Strip Widths  The simulation results generally matches the experimental behavior  The pattern size has to match the intrinsic domain size

Different PS:PAA Weight Ratios  The volume ratio of PS/PAA has to match the functionalized pattern area ratio

Effects of PAA Molecular Weight  The molecular weight of PAA will affect the shape of the Flory-Huggins local free energy  Smaller molecular weight results in a more compatible pattern

Summary  3D simulation for ternary polymer system is established  The evolution mechanism is investigated, with the evolution of the domain proportional to t 1/3  The condensed system has a faster agglomeration pace.  Simulation is validated by the experimental results  Parameters are estimated, such as the mobility and gradient energy coefficient.  Effects of experimental factors are investigated.  The numerical results matches the experimental results in general, and the model can be used to assist the experiment and theoretical work.  Incorporation with high rate plastics manuacturing is the next focus.

Conclusions  The United States is no longer the R&D super power  US R&D was 30% of global R&D in 1970  US R&D is now only 10% of global R&D  These facts do not indicate that the US in in decline, but rather that the rest of the world has made progress  Manufacturing will remain a vital source of wealth creation  Competitive advantages are still evolving  Natural and human resources  Logistical access to end-users  US manufacturers must continue focused R&D  New product innovation  Process productivity improvements  Employee recruitment, growth, & retention

Acknowledgements Melt Control Research Dynisco, Synventive, Mold-Masters National Science Foundation (grant #NSF )‏ Simulation of Polymer Self Assembly Centre of High rate Nano-manufacture at UMass Lowell National Science Foundation (grant #NSF )‏ Prof. Isayev and the University of Akron

The Effects of the Rotation Speed The faster rotation speed results in a smaller R value, due to the effects of the faster solvent evaporation

Validation with the Experiments -- with the Patterned Substrate Measure of the compatibility parameter, C s Experiment: SEM images are compared with the template patterns Simulation: Comparison of result pattern and substrate template are compared element by element s 1 (k) - the parameter in the surface energy expression for polymer one S k - the quantitative representation of the substrate attraction., and the greater the better

Determination of Controlling Factors Huggins Interaction parameter,  –  12,C : critical interaction parameter. c>c 12,C for spinodal decomposition to occur. –Determines the miscibility of the polymer pair –Bigger D. P., easier phase separation – –  i : solubility parameter of component I –The difficulties to obtain accurate solubility parameters.

Determination of Controlling Factors Gradient energy coefficient,  –a : Monomer size, the affecting radius of van de Waals force –Determines the domain size and interface thickness – –D: Diffusivity –Determines the kinetics of the phase transaction. The values of k and D are estimated by benchmarking with the experimental results, as shown later.