This book provides a comprehensive framework for process-induced programming in 4D printing, revealing how the additive manufacturing process itself imprints the shape-memory behavior of polymer structures. It connects material characterization, process mechanics, and predictive modeling into a single, coherent approach to making stimuli-responsive parts more reproducible and transferable.

4D printing extends conventional 3D printing by producing structures that transform on demand in response to stimuli such as heat. Yet the technology's central challenge is control: in fused filament fabrication (FFF), the directional deposition of thermoplastics introduces parameter-dependent pre-strains that drive the later shape change, but these are hard to predict, control, and reproduce. Without reliable models and process chains, functional behavior cannot be guaranteed, which remains the key barrier to industrial adoption.

Focusing on polylactic acid (PLA), the book first characterizes the thermomechanical, rheological, and viscoelastic behavior behind shape memory using DSC, DMA, rheometry, and shape-memory testing, and captures it in Cross-WLF and generalized Maxwell models. It then uses in-situ thermography and force sensing to link key process parameters (nozzle temperature, printing speed, layer height, and part cooling) to thermal history and mechanical programming. Finally, within a design-of-experiments framework, it develops two complementary predictive models: a statistical regression that delivers high accuracy for practical process design, and a physics-based shrinkage model, grounded in a Couette-flow analogy, that generalizes across conditions.

Rather than presenting a finished industrial solution, the book takes a deliberate step in that direction and clearly marks where further research and standardization are still needed to make 4D printing dependable at scale. Written for researchers, doctoral students, and engineers in additive manufacturing, polymer processing, and smart materials, it delivers both the experimental foundations and the modeling tools to move 4D printing closer to reliable, reproducible industrial application.

Ferdinand Cerbe is a mechanical engineer and was a doctoral researcher from April 2021 until June 2026 at the Institute of Mechanics and Adaptronics (IMA), Technische Universität Braunschweig, Germany, where he also completed his B.Sc. and M.Sc. in Mechanical Engineering. His research focuses on 4D printing — the additive manufacturing of polymeric shape-memory structures that transform in a controlled way when heated. In his doctoral dissertation, Process-Induced Programming in 4D Printing, he provides deeper insight into the programming mechanism during the printing process. His dissertation focuses on three main topics: Material Characterization, Process Mechanics and Predictive Modeling.

Since 2021 he has successfully completed the DFG-funded project on printed polymeric shape-memory structures at the IMA. His work has resulted in six peer-reviewed publications — in journals including Additive Manufacturing, Progress in Additive Manufacturing, Smart Materials and Structures and the Journal of Manufacturing and Materials Processing — and in presentations at national and international conferences, among them the ECCOMAS Congress 2024 in Lisbon and the 4D Materials Design and Additive Manufacturing Conference 2022, where he received the Best Research Presentation Award. He also reviews for Progress in Additive Manufacturing.