Precision Food Manufacturing by intelligent and flexible processing

Precision Food Manufacturing is emerging as a transformative paradigm for the food sector, enabling greater accuracy, repeatability, and design freedom across production chains. This PhD thesis explores how digitally-designed foods, advanced motion control, mathematical modelling, and robotic applications can converge to engineer food, shapes, and processes with unprecedented control. The work focuses on two main technological fronts: Robot-enabled and Processing Morphing Food, supported by experimental, mathematical modelling, machine learning and methodological advancements. The first part of the thesis investigated the robotic application in the food system, opening with an extensive review of robotics applications in the food industry that outlines technological gaps, highlighting all the opportunities and the strategic role of flexible automation enabling Precision Food Manufacturing An explored point in the thesis is the examination of unconventional robotic trajectories for mixing, implemented through a Delta Robot to generate complex 2D/3D patterns. Experimental results confirmed the importance of process parameters - i.e. mixing time and speed - in structuring emulsions, but also the trajectory geometry significantly affected the rheological properties and microstructure of oil-in-water emulsions. This work highlights the unexplored potential of trajectory-based process design in food manufacturing. The second part of the thesis focuses on Morphing Food. This technological application has been investigated demonstrating how programmable superficial grooves can drive predictable shape transformations during cooking in boiling water or in the oven. Starting from a manual grooving approach applied to pasta, the study quantifies how thickness, groove depth, and dehydration/rehydration kinetics influence bending behaviour. The obtained results were extended to a semi-automated system for grooves generation and baking in the oven, proving that an accurate control of the stamping process allows a more precise and predictable morphing. A Finite Element Model of heat and mass transfer further validates the physical mechanisms governing morphing and shows the potential of digital simulations as predictive tools for industry. Overall, the thesis provides scientific insights that advance the digitalization and automation of food manufacturing, offering concrete perspectives for industrial scaleup, improved sustainability, and enhanced product quality.