CitationRomano J-M, Gülçür M, Garcia-Giron A et al (2019) Mechanical durability of hydrophobic surfaces fabricated by injection moulding of laser-induced textures. Applied Surface Science. 476: 850-860. Rights ABSTRACT.The paper reports an investigation on the mechanical durability of textured thermoplastic surfaces together with their respective wetting properties. A range of laser-induced topographies with different aspect ratios from micro to nanoscale were fabricated on tool steel inserts using an ultrashort pulsed near infrared laser. Then, through micro-injection moulding the topographies were replicated onto polypropylene surfaces and their durability was studied systematically. In particular, the evolution of topographies on textured thermoplastic surfaces together with their wetting properties were investigated after undergoing a controlled mechanical abrasion, i.e.reciprocating dry and wet cleaning cycles. The obtained empirical data was used both to study the effects of cleaning cycles and also to identify cleaning procedures with a minimal impact on textured thermoplastic surfaces and their respective wetting properties. In addition, the use of 3D areal parameters that are standardised and could be obtained readily with any state-of-the-art surface characterisation system are discussed for monitoring the surfaces' functional response.

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CitationBaruffi F, Gülçür M, Calaon M et al (2019) Correlating nanoscale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and highspeed thermal imaging. Journal of Manufacturing Processes. 47: 367-381.Rights

This paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.