Skip to Content
ProcessesProcesses
  • This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
  • Review
  • Open Access

17 September 2026

Industrial Rheology of Cosmetic Products: From Formulation and Microstructure to Scale-Up, Process Control, Quality, Machine Learning, Digital Twins, and Consumer Performance

,
,
,
,
and
1
Department of Chemical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
2
Cosmetica Laboratories Inc., Toronto, ON M1L 2M5, Canada
*
Author to whom correspondence should be addressed.
Processes2026, 14(18), 2971;https://doi.org/10.3390/pr14182971 
(registering DOI)
This article belongs to the Special Issue Algorithm-Driven Design and Control of Complex Fluid Processing

Abstract

Rheology is central to the design and industrial manufacture of cosmetic products, which are multicomponent soft materials whose performance depends on formulation, microstructure, processing history, temperature, shear, and time. Although apparent viscosity is widely used for quality control, a single-point measurement cannot fully describe yield behavior, viscoelasticity, thixotropic recovery, wall slip, extensional response, crystallization, or sensory performance. This review presents an industrial framework for applying rheology across the cosmetic-product lifecycle, from formulation and raw-material selection to scale-up, manufacturing, filling, release, stability, quality assurance, and consumer experience. Steady-shear, oscillatory, transient, nonlinear, extensional, tribological, powder-flow, and slurry measurements are discussed together with key methodological limitations. Particular attention is given to emulsification, polymer hydration, pigment and powder incorporation, deaeration, cooling, crystallization, and scale-up criteria, including tip speed, power input, circulation, heat transfer, and energy history. Rheological fingerprints are also examined for batch comparability, process analytical technology, statistical process control, computational fluid dynamics, machine learning, and digital twins. The review highlights links among formulation, microstructure, rheology, processing, stability, package performance, sensory response, and consumer outcomes and proposes a stage-based testing framework and research priorities for more robust cosmetic manufacturing.

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.