Product Fingerprints for the Evaluation of Tool/Polymer Replication Quality in Injection Molding at the Micro/Nano Scale

Replication processes for the manufacturing of micro/nano-structured components are characterized by a certain degree of precision and accuracy. The transcription loss, or replication fidelity, defines the geometrical and dimensional correspondence of micro/nano-structure from metal tool inserts into plastic patterned products. The employment of a vast spectrum of micro/nano-structured geometries calls for methodologies that can be used for the estimation of replication fidelity. This study presents a number of product fingerprints, which propose multiple ways to characterize micro/nano structures in replication technologies. Replication fidelity yielded values above 80% and up to 96% depending on the considered product fingerprints and their definition. Thereafter, a correlation of the product fingerprint with the process parameters was found to optimize the replication process. Measurement uncertainty accompanies the analysis of the product fingerprints, enabling a standardized, robust, and quantitative methodology for process learning, modeling, and optimization.

[1]  P. M. Kristiansen,et al.  Iso- and variothermal injection compression moulding of polymer micro- and nanostructures for optical and medical applications , 2015 .

[2]  Michael P Sealy,et al.  Energy based process signature for surface integrity in hard milling , 2016 .

[3]  H. N. Hansen,et al.  Injection and injection-compression moulding replication capability for the production of polymer lab-on-a-chip with nano structures , 2017 .

[4]  Michael Sylvester Packianather,et al.  Micro Injection Moulding Process Parameter Tuning , 2015 .

[5]  Matteo Calaon,et al.  Micro-Injection Moulding In-Line Quality Assurance Based on Product and Process Fingerprints , 2018, Micromachines.

[6]  Dario Loaldi,et al.  Manufacturing Signatures of Injection Molding and Injection Compression Molding for Micro-Structured Polymer Fresnel Lens Production , 2018, Micromachines.

[7]  Guglielmo Lanzani,et al.  Laser-Inscribed Glass Microfluidic Device for Non-Mixing Flow of Miscible Solvents , 2018, Micromachines.

[8]  H. N. Hansen,et al.  Characterisation and analysis of microchannels and submicrometre surface roughness of injection moulded microfluidic systems using optical metrology , 2012 .

[9]  D. B. Pedersen,et al.  Characterization of near-zero pressure powder injection moulding with sacrificial mould by using fingerprint geometries , 2020, CIRP Annals.

[10]  H. Meyer,et al.  Development of a Process Signature for Manufacturing Processes with Thermal Loads , 2018, Metallurgical and Materials Transactions A.

[11]  S. Valette,et al.  Factors influencing microinjection molding replication quality , 2017 .

[12]  Jeppe Revall Frisvad,et al.  Functionality characterization of injection moulded micro-structured surfaces , 2019, Precision Engineering.

[13]  Ping Guo,et al.  Structural coloration of metallic surfaces with micro/nano-structures induced by elliptical vibration texturing , 2017 .

[14]  Injection molded superhydrophobic surfaces based on microlithography and black silicon processing , 2012 .

[15]  M. Farzaneh,et al.  Micro-nanostructured polymer surfaces using injection molding: A review , 2017 .

[16]  Yang Zhang,et al.  Investigation of Product and Process Fingerprints for Fast Quality Assurance in Injection Molding of Micro-Structured Components , 2018, Micromachines.

[17]  Massimiliano Annoni,et al.  Surface footprint in molds micromilling and effect on part demoldability in micro injection molding , 2017 .

[18]  Thomas Guenther,et al.  Review on Fabrication Technologies for Optical Mold Inserts , 2019, Micromachines.

[19]  Wei Li,et al.  Microfluidic chip designs process optimization and dimensional quality control , 2015 .

[20]  Eckert.  Multi-Cycle Process Signature of Laser-Induced Thermochemical Polishing , 2019, Journal of Manufacturing and Materials Processing.

[21]  Yang Zhang,et al.  Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips , 2018 .

[22]  Mohsen A. Jafari,et al.  Online defect detection in layered manufacturing using process signature , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[23]  B. Whiteside,et al.  Investigation of the influence of vacuum venting on mould surface temperature in micro injection moulding , 2017 .

[24]  S. Dimov,et al.  Correlating nano-scale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and high-speed thermal imaging , 2019, Journal of Manufacturing Processes.

[25]  K. Nagato.  Injection Compression Molding of Replica Molds for Nanoimprint Lithography , 2014 .

[26]  T. Nielsen,et al.  Low-cost, durable master molds for thermal-NIL, UV-NIL, and injection molding , 2019, Nanotechnology.

[27]  Guido Tosello,et al.  Product/Process Fingerprint in Micro Manufacturing , 2019, Micromachines.

[28]  A. Kristensen,et al.  Injection moulding antireflective nanostructures , 2014 .

[29]  E. Brinksmeier,et al.  Underlying Mechanisms for Developing Process Signatures in Manufacturing , 2018, Nanomanufacturing and Metrology.