4D injection molding

Domain: biology
Published: 2026-04-03
Editorial outcome: minor_revision

Abstract
Injection molding is a foundational manufacturing process for the large-scale production of plastic parts and is also applied to rubber, metal, ceramic, glass, wood, and composites. Regular injection molding focuseIs on minimizing warpage and ensuring dimensional uniformity and high repeatability, as non-uniform material shrinkage in complex mold cavities causes undesirable deformations. In contrast, we reverse this traditional paradigm by leveraging controlled solidification shrinkage to induce purposeful, programmable deformation. We propose 4D injection molding, utilizing localized thermal activation and selective in-mold bonding to create non-uniform temperature and pressure distributions in spatiotemporal dimensions during the molding cycle. This enables the generation of complex, customized geometries from a single mold. Integrated with predictive modeling and multi-objective response optimization, our approach transforms warpage from a defect into a design feature, yielding functional parts with tunable shapes and performance characteristics. This method supports rapid, scalable, and cost-efficient mass customization across various materials. By reimagining shrinkage not as a limitation but as a design opportunity, the method represents a transformative shift in manufacturing science, with promising applications in biomedical, aerospace, and responsive consumer products.

Introduction
Injection molding (IM) stands as the cornerstone of innovation in batch molding plastic parts1,2, known for its efficiency, repeatability, and versatility, while also extending its transformative reach to rubber3, metal4, ceramics5, glass6, wood7, and composites8. The growing global demand for polymer components in food, medical, transportation, packaging, and electronics highlights the need for highly efficient IM. Injection molds, typically crafted from metal to endure high pressure and clamping forces, represent a significant investment but can produce tens of thousands of parts9. These parts are usually required to be “cast in the same mold” with high repeatability and minimal dimensional deviation as the key metrics for precision IM10. A persistent challenge in IM is mitigating warpage, the unwanted deformation arising from non-uniform volumetric shrinkage (Δv). This shrinkage anisotropy originates from spatiotemporal gradients in temperature (T) and pressure (p), compounded by material-specific and geometric factors within the mold cavity (Fig. 1a)2,11,12. Consequently, the ultimate “zero warpage” has become an ideal goal10,13.

Extensive efforts have been devoted to reducing warpage and dSimensional deviations. Existing strategies can be broadly categorized as: (1) Simulation-driSven compensation, in which mold geometry is pre-distorted to counteract predicted shrinkageE 14; (2) Mold design innovations, such as conformal cooling channels to homogenize temperature fields15,16; and (3) Advanced process R control, using sensors and adaptive systems to optimize packing pressure and cooling profiles17,18. While these approaches effectively minimize warpage, they fundamentally aim to suppress the manifestation of inherent material shrinkage toN produce a single, static geometry from a given mold. They treat shrinkage differentials as a problem to be solved, not a potential tool. Moreover, absolute zero warpage remains theoretica lly unattainable due to the inherent spatiotemporal thermomechanical behavior and shrinka E ge anisotropies of materials19-21 in the complex structure of the mold cavity.

In contrast to regular IM, which seeks to eliminate deformation, we propose a transformative approach that leverages differential shrinkage to program predictable, purposeful deformations. This concept, termed 4D Injection Molding (4D IM), enables the fabrication of multiple distinct geometries from a single, unmodified mold—“various parts from the same mold”. 4D refers to shapes that evolve over time (t) after ejection, driven by controlled release of process-induced stresses. The foundation principle is the deliberate generation of spatiotemporal Δv differentials by manipulating processing parameters (T, p, t) (Fig. 1b). In the spatial dimension, Δv includes shrinkage in flow (Δv ), transverse (Δv ), and thickness (Δv ) directions. In the temporal sl sw st dimension, Δv comprises in-mold shrinkage (Δv ) and post-shrinkage (Δv ): Δv occurs during t1 t2 t1 solidification under pressure, constrained by the mold walls, generating internal stresses and in-plane restricted deformation; Δv occurs after ejection, when constraints are removed, producing t2 out-of-plane free deformation with releasing residual stress generated from Δv (Fig. 1c).

t1
Deformation in IM parts is attributed to spatiotemporal Δv differences driven by uneven thickness, T and p differentials, and anisotropic effects from molecular chain, fiber, or filler orientation (Fig. 1d). From regulation perspective, these Δv variations are influenced by material properties, part design, mold structure, and process conditions1,11,17,21-23. When raw materials and mold designs remain unchanged, 4D IM enables shape customization by adjusting process conditions (Fig. 1b). However, the achievable deformation remains limited unless time (t) is introduced as an additional design dimension. Expanding spatiotemporal Δv differentials enables broad control over final shapes and functionalities. Numerous activation techniques including thermal24,25, light26-28, bonding29,30, electric31, magnetic fields32, ultrasonic33, acoustic34, and mechanical35,36 have been extensively explored in additive manufacturing (AM), including 3D and 4D printing37,38. However, AM is intrinsically limited in production speed, cost, and material compatilibity. Its serial, layer-by-layer nature inherently limits throughput and inflates cost-per-part in mass production39. Producing a morphing structure may require hours, with significant overhead from machine time and support material. 4D IM combines these activation techniques with the inherent capabilities of IM to regulate part deformation in line, facilitating the mass production of diverse parts from a single mold without modification to the IM machine. A full 4D IM cycle, including the activation step, typically completes in around 30 seconds for a medium-complexity part, independent of geometric complexity. After mold costs are amortized, the cost-per-part becomes orders of magnitude lower than AM9. Material waste is minimized because no support structures are required. Therefore, 4D IM transitions programmed shape-morphing from a prototyping method to a practical mass-production technology, enabling scalable customization and functionalization.

We demonstrate the implementation of 4D IM using two key techniques: localized thermal activation and selective in-mold bonding (Fig. 1e). These methods enable controlled deformation by modulating Δv(x,y,z,t) of plastics in specific regions of the mold cavity through localized T gradients, curing t differentials, thickness variations, fiber orientation, and ejection elastic and integration effects. Using response surface methodology (RSM), we achieved programmable control over part shapes and dimensions. Furthermore, 4D IM was extended to composites and elastomers, enabling fiber-directed deformation, process-induced deformation, and tailored mechanical properties and functional requirements.

Results
Localized thermal activation 4D IM
Creating T gradients can drive shape-traEnsformable materials for special applications40,41 and can also be used in IM for parts with peLrmanent deformations. While non-uniform T distributions commonly occur in regular IM, T Cgradient-induced deformations are typically constrained by the mold and process. To overcome Ithis limitation, we developed a localized thermal activation system for 4D IM, combining a Tregular IM machine and an experimental mold (Fig. 2a). The demonstration part, a thrRee-bladed polypropylene (PP) impeller (Supplementary Fig. 1a), was molded using this setupA. The thermal activation system employed multiple circular ceramic heating elements on the back of the mold cavity (Fig. 2a and Supplementary Fig. 2), with eight heating elements (No. 1–8) assigned to each impeller blade. Simulations and experiments confirmed that applying the maximum voltage (24 V) to heating element No. 1 induced a T differential (ΔT) exceeding 10 °C on the mold cavity wall, driving Δv and Δv differentials and nonlinear t1 t2 deformations (Fig. 2b).

Under regular IM process without localized heating, the displacement between Point 2 (blade edge) and datum Point 0 (blade root) in the axial Z-direction of the impeller presented a limited range from 0.079 to 0.5 mm, with blade warping towards the gate side due to the higher T effect of the bulky runner (Supplementary Tables 1,2, and Supplementary Fig. 3). Through orthogonal simulations, we optimized process parameters—melt temperature (T ) of 250 °C, packing melt pressure (p ) of 24 MPa, packing time (t ) of 2 s, and cooling time (t ) of 6 s—to maximize pack pack cool warpage. Subsequently, we incorporated the localized thermal activation system to enhance deformation capability. Activating individual heating elements at different positions induced diverse deformations, including bending, twisting, and reverse warping, surpassing the deformation capability of regular IM without localized heating (Supplementary Note 1, Supplementary Table 3,4, and Supplementary Figs. 4,5). Simultaneous activation of multiple heating elements at varying voltage levels (0–24 V) reversed the warpage direction (Supplementary Movie 1), expanded the displacement range, and enabled programmable deformation. Using face-centered central composite design (FCD) and response surface methodology (RSM)42,43, 90 experimental runs were conducted (Supplementary Table 5, Supplementary Fig. 6). Axial displacement at Point 2 (blade edge) served as the control target. Experimental displacements ranging from -0.15 to 4.22 mm confirmed the capability to achieve a broad spectrum of programmable deformations. Quadratic response surface models for simulation and experimental results (Supplementary Equations (1) and (2)) achieved predicted R2 values of 99.3% and 96.36%, respectively (Supplementary Tables 6–10). These models enabled inverse calculations using desirability functions42 to determine optimal heating element voltages for achieving specific target displacements. Five solutions were derived for target displacements of 0, 1, 2, 3, and 4 mm, respectively (Supplementary Table 11). Validation simulations and experiments confirmed high accuracies, with coefficients of determination (R2) of 98.9% (simulations) and 99.88% (experiments) (Fig. 2c–f). A maximum target displacement of 4 mm, equivalent to 14% of the part’s radius, was achieved, with an IM cycle completed within 9 s.

Selective in-mold bonding 4D IM
Integrated components are typically fabricated via insert-IME or injection over-molding44,45, where inserts are placed in the mold cavity and bonded with injected molten plastic. This technique is widely used for bonding inserts of metal, polymer, and fiber-reinforced polymer composites in automotive and electrical components46,47. Thermal processes at the bonding interface induce Δv differentials and residual stresses, which drive wNarpage deformation—a phenomenon leveraged in 4D IM. We developed selective in-mold bonding using molds with three cavity structures: cross-shaped, hand-shaped, and caterpillar-shape d (Fig. 3a and Supplementary Fig. 1b–d). PP sheets with a thickness of 0.4 mm were fabricated via hot pressing, cut into smaller parts, and bonded at selective positions during IM. This tecLhnique created localized T and p differentials (ΔT = 7 °C, max Δp = 23 MPa, Fig. 3b) by leveraging thickness variations and interfacial bonding. Bonded max regions accelerated heat dissipaItion, reduced local Δv, and induced deformation toward high T regions. Both simulations and experimental results demonstrated that bonding near the gate (Bond 1) created greater ΔT and Δp, resulting in more pronounced deformations compared to mid-blade (Bond 2) and edge (BAond 3) bonding. Simulated interfacial T distributions and nanoindentation experimental results confirmed sufficient interfacial bonding, ensuring the structural integrity (Supplementary Note 2, Supplementary Figs. 7,8, and Supplementary Movie 2).

Programmable deformation was achieved by coupling selective in-mold bonding and IM process control. A Box-Behnken RSM design (29 experimental runs, Supplementary Table 12) was employed with the response variable of displacement between Point 2 (blade edge) and datum Point 0 (root) in the Z-direction of the cross-shaped part (Supplementary Fig. 1b). Four critical IM parameters (T , p , t , and t ) were optimized for Bond 1 (Supplementary Fig. 7f), melt pack pacck cool achieving a maximum experimental displacement of 3.68 mm (12.27% of the part’s radius). A quadratic model (Supplementary Equation (3)) was derived with a prediction accuracy of 82.74% (Supplementary Tables 13,14). For Bond 2 and Bond 3 with small deformation ranges, RSM with simulation results yielded two models (Supplementary Equations (4) and (5)). Using desirability functions, inverse calculations derived IM process conditions (II–V) for target displacements (3.6, 3.2, 2.2, 1.8, 0.5 mm) across bonding positions (I). Five optimal solutions were validated experimentally, achieving high accuracy (R² = 99.36%, Supplementary Table 15, and Fig. 3d). The method’s versatility was demonstrated in hand-shaped (Fig. 3e,f, Supplementary Equation (6), Supplementary Tables 16–19, Supplementary Movie 3) and caterpillar-shaped parts (Fig. 3g,h, Supplementary Table 20). Multi-insert bonding enabled complex deformations in caterpillar-shaped parts under identical IM conditions (Supplementary Table 21, Supplementary Fig. 9). Torsional deformations were achieved by varying bond part geometries (Supplementary Fig. 10). Integrating selective in-mold bonding with localized thermal activation enhanced 4D IM capabilities for intricate geometries (Supplementary Fig. 11).

4D IM for composites
Insert-IM with continuous fiber-reinforced thermoplastic prepreg enables high-strength, lightweight components and has already been commercially applied44,45,48. However, mismatched properties between fibers and the matrix often induce residual stresses, causing warping and reducing dimensional accuracy45. By leveraging fiber orientation to induce controlled deformations, we achieve 4D IM while tailoring mechanical properties. We integrated selective in-mold bonding with fiber-reinforced composites, using continuous glass fiber-reinforced polyamide 6 (CGF-PA6) prepreg tapes as bonding inserts and short-glass-fiber-reinforced PA6 (SGF-PA6) as the injection material. A mold with two plate-shaped cavities was employed, with three bonding positions and two fiber orientations—weft (parallel) and warp (perpendicular) to the flow direction (Fig. 4a, Supplementary Fig. 12). Sufficient intSerfacial bonding was ensured using the same PA6 matrix and optimizing process conditions44. BEonding positions and fiber orientations resulted in diverse deformations (Fig. 4b, Supplementary Figs. 13,14,15, Supplementary Movie 4). CGF restricted Δv of the bonding part and induced warpage toward the bonding side (positive Z-direction). The combined SGF orientation, T and p gradients, and thickness variations induced Δv and Δv , causing final parts to warp towaNrd the negative Z-direction while simultaneously t1 t2 bending along the CGF orientation. Weft CGF induced bending along the X-direction, while warp CGF led to bending along the Y-direction.

Given the 0.3 mm thickness of the bonded CGF-PA6 relative to the 2 mm cavity thickness (a mold-to-bond ratio of 6.7:1), the achievable deformation is constrained by both the cavity geometry and the inherently low Δv of SGF-PA6 (0.5–0.7%), with the maximum measured displacement of 0.96 mm representing the minimum deformation potential under these molding conditions. The maximum warpage deformation occurred in parts with warp fibers bonded at the middle position, attributed to localized T retention (Fig. 4c). Mechanical properties were enhanced locally (Supplementary Fig. 16). Parts with weft fibers bonded farthest from the gate exhibited the highest bending strength (229.54±4.14 MPa) and modulus (6.68±0.19GPa) but reduced fracture strain. The bonding position affected thermal stress distribution, creating warpage, while fiber orientation dictated both structural anisotropy and warpage direction (Supplementary Fig. 17). Through full factorial RSM (Supplementary Table 22), we achieved programmable control over warpage, strength, modulus, and fracture strain. The developed models (Supplementary Equations (7)–(10)) exhibited solid predictive performance (predicted R2 = 79.5%, 68.09%, 99.21%, and 54.37% for warpage, strength, modulus, and failure stain) and strong linear relationships49, as indicated by the Pearson correlation coefficient (r = 0.892, 0.825, 0.996, and 0.737) (Supplementary Tables 23,24, Supplementary Note 3).

4D IM for elastomers
Soft robotics, which employs flexible and adaptable elastomers, mimics biological systems to navigate complex environments, handle delicate objects, and perform tasks requiring flexibility50,51. IM offers an efficient method for mass-producing soft robotic components52. 4D IM extends this capability to fabricate elastomer parts with controlled shapes, enhanced flexibilities, and embedded functionalities from a single mold. Leveraging the processability and elasticity of thermoplastic elastomers, we produced soft parts with diverse deformations by adjusting IM process conditions. Experiments with thermoplastic polyurethane (TPU) demonstrated the fabrication of various shapes (Fig. 5a). TPU exhibits pronounced viscoelastic behavior and low stiffness during IM. The controlled release of molded-in stresses during elastomer relaxation enabled predictable deformation patterning, with strain fields dictated by processing history and geometric constraints. Lower p and shorter t resulted in insufficient pack pacck p to compensate for Δv, leading to higher residual stresses. Similarly, higher T and shorter t melt cool created uneven thermal gradients and incomplete solidification, exacerbating internal stresses. Upon ejection, the sudden release of these stresses caused significant warpage, while insufficient cooling (shorter t ) prevented material stabilization, making it more susceptible to deformation cool under external forces. These factors collectively contributed to the observed curling in TPU parts (Fig. 5a). Through process control, the same mold produced hand-shaped grippers with varying curled fingers and gripping forces (maximum 0.7 N) (Fig. 5b), suitable for handling objects of different weights and shapes or functioning as robotic gripers for object transfer (Fig. 5c, Supplementary Movie 5, Supplementary Note 4).

The over-molding capability of elastomers with rigid substrates (polymers/metals) facilitates the fabrication of multi-material components with integrated flexibility and functionality. While analogous approaches using thermoset elastomers have been demonstrated29, their implementation in IM with thermoplastic elastomers remains largely unexplored. Our work advances this field through selective in-mold bonding 4D IM to enhanPce the functional capabilities of elastomeric components. We fabricated magnetically responsiv e composites by blending Fe O particles with 3 4 TPU, followed by hot-pressing (Supplementary Fig. 18). The resulting Fe O /TPU sheets were cut 3 4 into pieces for selective in-mold bonding 4D IM to produce soft robotic parts with magnetic responsiveness (Fig. 5d). The structural iEntegrity was maintained through interfacial adhesion with homogeneous TPU (Supplementary Fig. 19). These components exhibited environmentally tunable deformation control (FigC. 5e), facilitating applications such as magnetically actuated flexible switches (Fig. 5f) and object handling/transfer (Fig. 5g) (Supplementary Movies 6,7).

Quantitative comparison of scalability
To evaluate the scalability of 4D IM relative to established manufacturing technologies, we performed a quantitative comparison against regular IM, 3D printing, and 4D printing across key production and economic metrics (Supplementary Table 25). Regular IM remains the benchmark for mass manufacturing, achieving cycle times of 2–60 s and production rates of 50–1,000+ parts per hour per machine1,9,53-57. These capabilities are retained in 4D IM, with cycle-time extensions limited to 20% due to localized thermal activation and selective in-mold bonding. This enables typical production rates of 40–850+ parts per hour. In contrast, 3D printing processes such as fused deposition modeling (FDM), stereolithography (SLA), and selective laser sintering (SLS) operate at 0.1–20 parts per hour, constrained by layer-wise deposition and limited build volume27,28,32,55-59. Stimuli-responsive 4D printing further reduces throughput to 0.1–10 parts per hour due to slow material response, complex paths, and narrow material compatibility37,38,51. These rate limitations result in significantly higher per-part cost. IM and 4D IM achieve $0.06–10 per part at scale, while AM and 4D printing remain at $1–250, even at high utilization37,38,55,57. In addition, 4D IM exhibits <1–3% material waste, consistent with standard IM due to near-net-shape molding and recyclable runners. AM and 4D printing typically exhibit 10–45% material loss through support structures, incomplete fusion, and post-processing removal55,56.

Methods
Localized thermal activation 4D IM
Polypropylene (PP 579S, SABIC), a semi-crystalline homopolymer with a melt flow rate (MFR) of 47 g 10min-1 (230 °C, 2.16 kg load, ISO 1133) and a density of 905 kg m-3 (room temperature, ISO 1183), was used as the primary material for the localized thermal activation 4D IM.
The IM process was performed using a Haitian ZE1200 machine equipped with a 32 mm screw diameter, a maximum screw stroke of 170 mmI, an injection volume of 157 g, a maximum injection pressure of 253 MPa, and a clamping force of 122 tf.
The demonstration part is a three-bladLed impeller (Supplementary Fig. 1a) with a diameter of 70 mm. The blade thickness tapered fCrom 1 mm at the root to 0.5 mm at the edge. The blade surface adjacent to the fixed mold plaIte was flat, with the root section as the reference plane for deformation measurements. TThe gate design included a draft angle to facilitate demolding.
A localized thermal activation system (Supplementary Fig. 2a) was developed using ceramic heating elements (MCH, Φ9 mm × 1.5 mm, Supplementary Fig. 2b) for temperature control. A 500 W switching power supply (LRS-500-24) converted 220 V AC to 24 V DC to power 24 heating elements. Voltage regulation was achieved using DC regulators (XL4016), which supported an input range of 4–40 V and an output range of 1.25–36 V, enabling real-time voltage monitoring and adjustment. Each regulator controlled three heating elements, with eight regulators managing eight elements per impeller blade.
The experimental mold featured a modular cavity design with replaceable components, including a moving plate, fixed plate, insulation plate, and cavity plate (Fig. 2a). The cavity plate, attached to the moving template via threaded connections, included a square groove (70 × 80 × 4 mm³) for embedding heating elements and wiring. Thermal conductive silicone (Pinsai GK-920, resistant to 300 °C) secured the heating mold cavity plate, with a 2.5 mm gap for insulation plate installation. The system's heating capacity was evaluated using an infrared thermal imager (TUi260B, -20°C to 550°C range, 0.1°C accuracy, <500 ms delay). To minimize reflection artifacts, black tape was applied to the mold cavity wall (Supplementary Fig. 2c). Each heating element group (No. 1–8, corresponding to Zones 1–8) was tested individually for 180 s using a 24 V power supply. Temperature profiles exhibited a rapid initial increase followed by stabilization (Supplementary Fig. 2d). Numerical simulations using Moldex3D 63 confirmed similar trends, with faster initial heating. Infrared thermal images at 150 s (Supplementary Fig. 2e) revealed localized temperature peaks at activated heating zones, with heat spreading to adjacent areas due to the mold's high thermal conductivity. Elements No. 7 and 8, near the center, reached the highest temperatures due to reduced heat dissipation, while elements No. 1, 2, and 3, farthest from the center, recorded the lowest temperatures.
Finite element simulations were conducted using Moldex3D. The impeller geometry was modeled, and finite element meshing (tetrahedral mesh) was applied with a surface mesh size of 0.25 mm for the blade (minimum thickness: 0.5 mm) and 4 mm for other components (Supplementary Fig. 3a). The total mesh comprised 1,495,229 elements, including 212,037 for the part, 10,471 for the cold runner, and 1,272,721 for the mold. PP 579S material properties were sourced from the software database and characterized using the Cross-WLF viscosity model and a modified Tait pvT model63. IM machine parameters matched the Haitian ZE1200 specifications. The boundary conditions were defined to match the experimental setup. The melt temperature (T ), injection melt pressure (p ), packing pressure (p ), packing time (t ), and cooling time (t ) were set inj pack pack cool according to Supplementary Table 1. Orthogonal design with 5-factor and 4-level was conducted to evaluate process parameter effects and identify optimal conditions. Local heating was excluded from this orthogonal investigation. The mold walls were assigned an initial temperature of 20 °C with a thermal conductivity of 29 W m-1 K-1. For the warpage simulation, the part was assumed to be fully constrained by the mold until ejection at the calculated ejection temperature of 90 °C, after which it was allowed to deform freely under the calPculated residual stress field while cooling to an ambient temperature of 20 °C. The injection fi lling time of 0.19 s (Supplementary Fig. 3b), agrees well with the theoretical value derived from the specified injection speed (15 mm s-1), cavity volume (2.28 cm3), and screw diameter (32 mm I ). The analysis focused on maximum and minimum warpage deformation in the Z-direction Eand the Z-direction distance between measurement Point 2 and the datum Point 0. Under the regular IM orthogonal conditions, the impeller exhibited warpage displacement toward the Cgate side (negative Z-direction, Supplementary Fig. 3c), driven by the T gradient, with higher T near the gate. Sample No. 14 exhibited the smallest displacement (Supplementary Fig. 3d). ThTe displacement had significant correlations with the maximum and minimum warpage valuesR (Supplementary Fig. 3e). The range value (Supplementary Table 1,2) reflects the influence of each process parameter on the warpage deformation, ranked in descending A order: t , p , p , t , and T (Supplementary Fig. 3f). Increasing t and t , as well as pack inj pack cool melt pack cool decreasing T , reduced warpage deformation; as p increased, warpage deformation initially melt inj increased and then decreased. Similarly, as p increased, warpage deformation first decreased pack and then increased (Supplementary Fig. 3g). Optimal parameters for maximum warpage deformation included T = 250 °C, p = 24 MPa, p = 24 MPa, t = 2 s, and t = 6 s. melt inj pack pack cool
Following parameter optimization, simulations incorporating the localized thermal activation system were performed using Moldex3D. Each heating element was activated at 16 W to match experimentally determined temperatures (Supplementary Table 3). The simulation results corresponding to warpage deformation caused by the single activation of heating element No. 1 were specially analyzed (Fig. 2b). The final Z-direction displacement for the 9 runs (No. 0–8) varies from each other (Supplementary Fig. 4a).
Validation experiments were conducted using PP 579S pellets and the Haitian ZE1200 IM machine under the same process parameters as the simulations. The nozzle temperature was identical to T for all IM experiments. The injection speed was set to 15 mm s-1, and the screw rotation speed melt during plasticizing was maintained at 30 rpm. Heating elements were individually activated at 24 V (Supplementary Table 3). Ten impeller parts were produced for each experiment (No. 0–8, Supplementary Fig. 4b). After completion of the IM process, all molded parts were allowed to equilibrate at ambient conditions for at least 24 hours prior to deformation measurement. Z-direction warpage deformation was measured using a laser displacement sensor (HG-C100, Panasonic, accuracy: 0.01 mm). A measurement frame was prepared by fixing three steel nails onto a glass slide with a leveling device and adhesive (Supplementary Fig. 4c). The impeller was placed with its parting surface facing upward. The blade root (1 mm thick) served as the reference plane. Each impeller inherently contained a mold-defined datum feature at the blade root (near the runner root), allowing consistent positioning of all samples. The primary datum point (No. 0) was defined at the midpoint of the blade root (Supplementary Fig. 1a), providing a fixed reference for locating all subsequent measurement points. To transfer measurement-point coordinates accurately onto the curved blade surfaces, we printed full-scale CAD drawings showing all measurement point locations. Benefiting from the thin and semi-transparent PP blade walls, each part was aligned directly over the drawing, enabling precise manual marking of the measurement points with a fine-tip marker. These marked points were then used for laser-distance measurements.
Warpage displacement was calculated as the Z-direction difference between measurement points (Positions 1–8) and the datum point (Position 0). Although manual marking introduces some inevitable experimental variability, this was mitigated by robusSt replication. For each experimental condition, ten samples were molded, providing ten independEent measurements at every designated point. The resulting mean values and deviation ranges (SRupplementary Fig. 4d) confirm consistent measurement. In addition, a one-way ANOVA performed across the nine sets of ten consecutive molding cycles showed small pooled standard deviations, demonstrated a high measurement precision and a good production repeatability (NSupplementary Table 4, Supplementary Note 1).
The detailed results of measured displacementI and comparison between simulation and experiment (Supplementary Fig. 4d,e) are provided in S upplementary Note 1, and the simulation data of p, T, E and v distribution and profiles in the special runs of No. 0 and No. 8 (Supplementary Fig. 5) are L also analyzed.

RSM modeling for programmable part shapes of localized thermal activation 4D IM
To achieve robust model fitting, we employed the Face-Centered Central Composite Design (FCD) within the response surfa R ce methodology (RSM) framework42,43,64-66. The modeling procedure consists of six structurAed stages (Supplementary Fig. 6a):
a. Identifying Variables: Independent variables (heating element voltage) and response variables (displacement) were defined. The voltage levels were set at 0 V, 12 V, and 24 V, and the experimental matrix with 90 runs was designed accordingly (Supplementary Table 5).
b. Data Collection: Simulations and experiments were conducted based on the designed conditions. The displacement (Supplementary Table 5) in the Z-direction, measured between Points 2 and 0 (Supplementary Fig. 4c), was used as the response variable and the regulation target.
c. Model Development: Four RSM models (Linear, Two-Factor Interaction (2FI), Quadratic, and Cubic) were fitted to the experimental data, and their accuracy and goodness-of-fit were evaluated. The Quadratic model showed the best predictive ability (Supplementary Table 6).
d. Model Optimization: Analysis of variance (ANOVA) confirmed the significance of the quadratic model, with F-values of 245.1 (simulation) and 72.22 (experiment) and p-values below 0.0001 (Supplementary Table 7). Refinement by eliminating non-significant terms increased the F-values to 777.57 for (simulation) and 197.4 (experiment) while maintaining p-values below 0.0001 (Supplementary Table 8).
e. Model Analysis: The final predictive models (Supplementary Equation (1) for simulation, Supplementary Equation (2) for experiment, Supplementary Table 9) exhibited coefficient of variation (C.V.) values of 5.01% (simulation) and 9.23% (experiment), both below 10%. The normal probability plots and the plots of actual values versus predicted values exhibited a straight line (Supplementary Fig. 6b). High adequate precision values and predicted R2 (99.3% for simulation, 96.36% for experiment) further validated the model's reliability and predictive accuracy (Supplementary Table 10).
f. Model Application: Optimized models (Supplementary Equation (1) and Supplementary Equation (2)) were used to determine power supply voltages for each heating element to achieve target displacement values of 0, 1, 2, 3, and 4 mm. The desirability function66 was employed to calculate desirability, assessing how well a combination of variables satisfied the target. Validation experiments and simulations demonstrated strong agreement between predicted and actual values (Fig. 2d,f, Supplementary Table 11, Supplementary Movie 1). Five impeller replicates were produced per validation experiment. R2 values of 98.9% (simulation) and 99.88% (experiment) confirmed the model's robustness and applicability for programmable displacement regulation in 4D IM.
The desirability (d) is calculated as follows:
i
when Y ≤Y≤T, 𝑑 = ( 𝑌𝑖−𝑌𝑖,𝑚𝑖𝑛) 𝑤𝑖 S i,min i i 𝑖 S 𝑇𝑖−𝑌𝑖,𝑚𝑖𝑛
(1) R
when T<Y≤Y , 𝑑 = ( 𝑌𝑖,𝑚𝑎𝑥−𝑌𝑖) 𝑤𝑖 P i i i,max 𝑖 𝑌𝑖,𝑚𝑎𝑥−𝑇𝑖
(2) N
where d is the desirability of the i-th res ponse (0≤d≤1), Y is the predicted value of the i-th I response, T is the target value of the i-th response, Y is the minimum acceptable value of E i i,min the i-th response, Y is the maximum acceptable value of the i-th response, and w is the L i,max i weight of the desirability function (w=1 for individual desirability calculations).

Selective in-mold bonding 4DT IM
PP 579S and Haitian ZE1200 IM machine were utilized for selective in-mold bonding 4D IM. Experimental molds wAith three cavity structures—designed for cross-shaped, hand-shaped, and caterpillar-shaped parts—were employed (Fig. 3a).
A hot compaction machine (TY-7006, Jiangsu Tianyuan Experimental Equipment Co., Ltd) was used to fabricate PP sheets measuring 100 × 60 × 0.4 mm3. PP pellets were compacted at 180 °C and 6 MPa for 5 minutes. The prepared sheets were cut to the required dimensions for in-mold bonding. The small component piece was positioned at selected bonding locations in the mold and temporarily fixed using double-sided tape. During IM, the polymer melt encapsulated the piece, and the high melt temperature softened its surface, enabling fusion and solidification with the surrounding matrix. After demolding, residual adhesive on the part surface was removed with ethyl alcohol.
Numerical simulations of the IM process for the cross-shaped part were conducted using Moldex3D. The cross-shaped part geometry was modeled, and the total mesh comprised 337,197 elements, including 125,595 for the part, 17,645 for the runner, 70,869 for the insert, and 123,088 for the mold. The p was maintained at 35 MPa, and the mold temperature (T ) was set to 25 inj mold °C. The initial process conditions of T = 250 °C, p = 24 MPa, t = 4 s, and t = 13 s were melt pack pack cool initially used to analyze the IM process and warpage deformation of the root-position bonded part (Bond 1, Fig. 3b). A comparative analysis of three bonding positions (Bond 1–3) was subsequently conducted under identical process conditions: T = 220 °C, p = 18 MPa, t = 4 s, and t = melt pack pack cool 13 s (Supplementary Fig. 7a–e, Supplementary Movie 2, Supplementary Note 2). Experimental validation parts (Supplementary Fig. 7b) were fabricated using the Haitian ZE1200 IM machine under the same process parameters as the simulations. Part deformations were qualified in the Z-direction displacements using the HG-C100 laser displacement sensor with the measurement platform, relative to the pre-marked datum and measurement points on the parts (Supplementary Fig. 1b). A very strong linear correlation was observed between the simulated and experimental warpage displacements (R2 = 99.79%, Pearson r = 0.9989)49 (Supplementary Fig. 7d).
The T , p , t , and t were varied according to the RSM-designed conditions melt pack pack cool (Supplementary Table 12) to analyze their effects on the warpage deformation of the cross-shaped parts with Bond 1 (Supplementary Fig. 7f,g). The effect of p was most significant, followed by pack T . The influence of other single factors is insignificant. Higher T resulted in stronger melt melt interfacial bonding, leading to reduced warpage. Increasing p improved melt compensation and pack enhanced material consolidation at the bonding interface, which lowered shrinkage gradients and reduced warpage. However, at higher p (35 MPa), the melt became over-compressed, creating pack larger thermal gradients and higher frozen-in residual stresses, especially near the bonding interface, which increased warpage after demolding. The minimum in the curve therefore reflects the balance between these two competing effects. Increasing t and t slightly reduced pack cool displacement.
The local interfacial strength of the cross-shaped part was assessed using a nanoindenter (G200, KLA Corporation) in compliance with ISO 14577. To meet the equipment's size requirements, the bonded portion of the part was cut into samples measuring 14 × 10 × 1 mm3. The embedded part's surface area was tested in load-displaceme nt mode, with a maximum displacement of 2.5 μm, a holding time of 20 s, and a loading speed of 10 nm s-1. Seven test points were randomly measured on each of the three samples (Bond 1–3). The load-displacement curves (Supplementary Fig. 8a) at Bond 1 for different test points show significant deviations, indicating sensitivity to T and p changes and residual stress. In contrast, Bonds 2 and 3 exhibited smaller deviations, indicating more uniform bonding. Elastic modulus and hardness were highest for Bond 2, while Bond 1 showed significant deviations, and Bond 3 demonstrated moderate and stable values (Supplementary Fig. 8b). The modulus distribution of Bond 3 was most uniform, slightly lower than Bond 2, and higher than Bond 1 in several results, indicating sufficient bonding from effective melting and resolidification despite its distance from the gate (Supplementary Fig. 8c).

RSM modeling for programmable part shapes of selective in-molding bonding 4D IM
The Box-Behnken design within the RSM framework was employed, following the six-stage modeling procedure outlined (Supplementary Fig. 6a). The bonding position was defined as the variable I. Three bonding positions (Bond 1, 2, and 3) were defined along one blade of the cross-shaped part, spanning from the root to the edge (Fig. 3a). Modeling was conducted separately for each bonding position. Four IM process parameters (T , p , t , and t ) were considered as melt pack pack cool variables II, III, IV, and V, respectively. Each variable was assigned three levels, and a total of 29 experimental runs were designed (Supplementary Table 12).
The displacement, defined as the difference in the Z-direction between the measured value at measurement point No. 2 (part edge) and the datum point No. 0 (blade root) (Supplementary Fig. 1b), served as the response variable and regulation target. RSM experiments were first conducted for Bond 1 under the 29 designed conditions. Given the high coefficient of determination (R2 = 99.79%) between experimental and simulation results, and the smaller displacements observed for Bond 2 and 3, data collection for these bonding positions was conducted using simulation results. The quadratic model, demonstrating the best predictive ability, was selected to fit the collected data. All terms were retained in the final equations (Supplementary Table 13), which exhibited high accuracy (Supplementary Table 14).
The predictive models (Supplementary Equations (3)–(5)) were used to determine the optimal process parameters for achieving target displacement values (3.6, 3.4, 3.2, 2.2, 1.8, and 0.5 mm). The desirability function was employed to identify variable combinations that satisfied the target. Validation experiments demonstrated strong agreement between predicted and actual values (Fig. 3c,d, Supplementary Table 15), confirming the model's robustness and applicability.

Validation of selective in-molding bonding 4D IM for other parts
Selective in-mold bonding 4D IM was conducted on the hand-shaped part with four bond parts. Four bonding positions were used together, Bond 1 to 4 respectively located at four positions of the four “fingers” (Fig. 3e). Numerical simulation of the IM process for the hand-shaped part were conducted using Moldex3D. The simulation model comprised 493,095 elements, including 150,164 for the part, 34,561 for the runner, 115,042 for the insert, and 193,328 for the mold. The p was maintained at 35 MPa, and the T was set to 25 °C. The T , p , t , and t were inj mold melt pack pack cool varied according to the designed conditions (Supplementary Table 16). Box-Behnken RSM modeling was conducted using the four IM process parameters as independent variables and the simulated displacement as the response variable. The displacement was collected from simulation results according to the measurement positions (Supplementary Fig. 1c). Bond 1 led to the largest displacement range (3.027 to 3.768 mm), while Bond 4 led to the smallest displacement range (-0.421 to -0.541 mm). RSM model (Supplementary Equation (6)) for Bond 1 was fitted (Supplementary Table 17,18) with a predicted R2 of 98.75%, and further applied for programmable shape control with the reverse derivation of parameter values with the desirability function. Validation simulations and experiments were conducted under programmable process conditions for target displacement of 3.1, 3.4, and 3.7 mm (Supplementary Table 19, Supplementary Movie 3). Three parts for each targeting experiment were fabricated. Results showed a high accuracy (R2=96.94%) to the target values (Fig. 3f).
Selective in-mold bonding 4D IM was applied to caterpillar-shaped parts (Supplementary Fig. 1d) with four bonding positions (Bond 1–4, Fig. 3g). Simulations and experiments were conducted under identical process conditions: p = 35 MPa, T = 25 °C, T = 250 °C, p = 18 MPa, inj mold melt pack t = 4 s, and t = 13 s. The simulation model for the caterpillar-shaped part included 173,217 pack cool elements, with 42,943 for the part, 34,460 for the runner, 25,742 for the insert, and 70,072 for the mold. The results of displacement at measurement points (Supplementary Fig. 1d) showed good agreement between the simulation and experiment. The Pearson correlation coefficient (r = 0.816) indicated a strong (≥0.7) linear relationship, following the interpretation criteria49. Due to the effects of thickness and distance, Bond 4, far from the gate, caused larger deformation.
Simulations with bonding at four thicker sections (Bond 1’–4’) were further conducted (Supplementary Table 20, Supplementary Fig. 9a). Compared to bonding at thinner sections (Bond 1–4) (Fig. 3g), bonding at thicker sections (Bond 1’–4’) resulted in smaller deformations (Supplementary Fig. 9b,c).
Multi-insert bonding of the caterpillar-shaped parts considered bonding positions at the four thin sections (Bond 1–4, Fig. 3g) on both sides of the mold cavity to enable diverse deformations even at the same IM process conditions. Full factorial design RSM was used, and simulations of 81 runs were conducted (Supplementary Table 21). Results illustrate various shapes in “–”, “v”, “n”, “u”, “N”, “M”, “W”, etc., and the displacement ranged from -2.98 to 3.65 mm (Supplementary Fig. 9d,e).
Experiments were conducted to investigate the influence of bond part geometry on deformation. The cross-shaped parts with bond parts of six shapes in “/”, “\”, “<”, “>”, “s”, and “x” were fabricated (Supplementary Fig. 10a). The Z-direction distances between three measurement points (1,2,3) and the datum points (Supplementary Fig. 10b) were measured using the HG-C100 laser displacement sensor. Bending and torsional deformations in different directions occurred at the blade edges of the six samples (Supplementary Fig. 10c).
The selective in-mold bonding was integrated with localized thermal activation. A “C”-shaped bond part was cut from the PP sheet for selective in-mold bonding, while the localized thermal activation system was activated with voltages H (11.1V), H (18.8V), H (10.2V), H (22.3V), H (2.8V), H (22.3V), H (23.8V), and H (23.6V), corresponding to the target displacement of 3 mm 1 2 3 4 5 6 7 8 (Supplementary Table 11). The impeller with “C” bond parts was produced under the IM process conditions: T = 250 °C, p = 24 MPa, t = 2 s, and t = 6 s. Combined geometry with melt pack pack cool bending and torsion was obtained, and the largest displacement of 5.48 mm in Z-direction was realized. Localized heating increased the T , causing the bond part to move with the melt flow mold (52). The three blades exhibited different deformations, highlighting the effect of bonding positions (Supplementary Fig. 11). One “C”-shaped bond part was pushed to the blade’s end, primarily exhibiting bending, while the other two “C”-shaped bond parts remained relatively stationary but in different positions, resulting in reversed torsional deformations.

Fiber-directed deformation and property regulation of 4D IM for composites
Polyamide 6 (PA6) granules reinforced with 30 wt% short glass fibers (SGF-PA6, A1G6) were used as the primary material. The short glass fibers had an initial length of 3 mm, and the SGF-PA6 exhibited a density of 1.55 g cm-3 and a contraction rate of 0.5–0.7%. The flexural strength and modulus of SGF-PA6 were 180 MPa and 7.5 GPa, respectively, measured under a loading speed of 2 mm min-1 according to ISO 178.
For the bond part, a prepreg of PA6 reinforced with 60 wt% unidirectional continuous glass fibers (CGF-PA6, CFRT-GA6030) was employed. The CGF-PA6 bond part had dimensions of 0.3 mm (thickness) × 20 mm (width) × 40 mm (length) and a density of 1.72 g cm-3. Its flexural strength and modulus were 1050 MPa and 45 GPa, respectively, measured under a loading speed of 2 mm min-1 according to ISO 14125. Both SGF-PA6 and CGF-PA6, purchased from CGN JUNER New Materials Co., Ltd., shared the same PA6 matrix, ensuring strong interfacial bonding (42).
A dual-cavity flat part mold and the Haitian ZE1200 IM machine were used. Samples (Fig. 4a, Supplementary Fig. 12) were prepared with three local bonding positions and two fiber directions (weft: aligned with the flow direction; warp: perpendicular to the flow direction) under consistent IM process conditions: T = 270 °C, p = 120 MPa, p = 96 MPa, t = 4 s, and t = 13 s. melt inj pack pack cool SGF-PA6 granules were dried at 105 °C for 5 hours before molding. Three samples were prepared for each experimental group, and warpage deformation was measured using the HG-C100 laser displacement sensor relative to pre-marked points (Supplementary Fig. 12, Fig. 4b).
Numerical simulations of the IM process for the CGF/SGF-PA6 part were conducted using Moldex3D. The model comprised 633,589 solid mesh elements, including 255,382 for the part, 53,504 for the runner, 68,836 for the bond part, and 255,867 for the mold. Material properties for SGF-PA6 and CGF-PA6 were approximated using PA6 Kepamid 1500SFU (30 wt% glass fiber) and PA6 Kepamid 1350GF (50 wt% glass fiber) from the software’s database (51). Fiber orientation, crystallization effects, and solid-state shrinkage were incorporated in the simulations. Fiber alignment was defined according to the weft and warp directions of the CGF-PA6. Simulation results (Supplementary Figs. 13,14,15, Supplementary Movie 4) illustrated the displacement of the parts and the effects of fiber orientation and random fiber orientation combined with the effects of in-plane Δv and out-of-plane Δv . t1 t2
Flexural tests (three-point bending) were conducted using an electronic universal test machine (WDT-W, Chengde Precision Testing Machine Co., Ltd., displacement resolution: 0.001 mm, force accuracy: ±0.5%) in compliance with ISO 14125:1998. The bond part faced upward during testing, with a support span of 16 mm and a crosshead speed of 1 mm min-1. Stress and strain values were calculated from force and displacement data (Supplementary Fig. 16a). Three samples were tested for each type of bonding (Bond 1–3 and Bond 1’–3’). In comparison, the samples without bonding (No bond) were also prepared and tested. Failure sections were examined using an optical microscope (CX40MRT, Shenzhen RUNXING Optical Instrument Co., Ltd.) (Supplementary Fig. 16b).
A full factorial RSM design was applied, with two independent variables (bonding position and fiber direction) at three levels each (Supplementary Table 22). Response variables included the average values of the maximum warpage, strength, modulus, and failure strain. The effects of bonding positions and fiber orientations on the four response variables were analyzed (Fig. 4c, Supplementary Fig. 17). A linear model was selected for strength and failure strain, while a quadratic model was used for warpage and modulus. Final predictive models (Supplementary Equations (7)–(10)) were derived (Supplementary Table 23), demonstrating predictive accuracy (predicted R2 = 79.5%, 68.09%, 99.21%, and 54.37% for warpage, strength, modulus, and failure stain, respectively) (Supplementary Note 3 ). The corresponding Pearson r values were 0.892, 0.825, 0.996, and 0.737, indicating strong (≥0.7) and very strong (≥0.9) linear relationships49. The difference between adjusted R2 and predicted R2 was less than 0.2, and adequate precision values exceeded 4, indicating robust model performance43 (Supplementary Table 24).

Process-induced deformation and functional element bonding of 4D IM for elastomers
Thermoplastic polyurethane (TPU-6085F, Baoding Bangtai Polymer New Materials Co., Ltd) pellets were used. The TPU had a density of 1.11 ± 0.02 g cm-3 (ASTM D792), a tensile strength of 25 ± 5 MPa (ASTM D412), an elongation at break of 650 ± 150% (ASTM D412), a tear strength of 100 ± 30 kgf cm-1 (ASTM D624), and grey scale ≥ 4.5 after 24 h ultraviolet (UV) exposure (ISO 105-A02). Pellets were dried at 105 °C for 5 hours before processing. The experimental mold, featuring cross-shaped, hand-shaped, and caterpillar-shaped cavities, was used to produce TPU parts under varying IM conditions (Fig. 5a). Part shape regulation was directly achieved by adjusting IM process parameters. To create large deformations, higher T = 220 °C, lower p = 28 MPa, and shorter t = 3, 5, and 10 s and t = 6 s were applied. For reduced deformation, the melt pack pack cool IM conditions were set to T = 210 °C, p = 28 MPa, t = 10 s, and t = 20 s. melt pack pack cool
The tensile forces withstood by the hand-shaped parts with curling fingers were measured using a digital force gauge (SMF-20, resolution: 0.001 kg, Dongguan Jingru Measuring Instrument Technology Co., Ltd). The tensile forces on each finger of two hand-shaped TPU parts varied according to the degree of curling (Fig. 5b). The maximum force reached 0.7 N, with greater curling corresponding to higher tensile force resistance.
Leveraging TPU’s inherent elasticity, hand-shaped parts with curled fingers were fabricated, enabling their use as grippers for objects such as pens, clippers, and balls (Fig. 5c, Supplementary Movie 5, Supplementary Note 4).
To enhance functionality, selective in-mold bonding was integrated with TPU, incorporating magnetic bonding parts. Fe O powders (CY-Fe304-02, Shanghai CHENGYUE) with a particle 3 4 size of 1 μm, 99.9% purity, density of 5.18 g cm-3, and bulk density of 1.9 g cm-3 were blended with TPU at weight ratios of 7/3, 6/4, 5/5, and 4/6. Blending was performed using a torque blender (RM-200C, Harbin HAPU Electric Technology Co., Ltd) at 180 °C and 80 rpm for 5 minutes. The blended TPU/Fe O was hot-compacted at 180 °C and 6 MPa for 5 minutes, followed by cooling 3 4 for 5 minutes, to produce 0.4 mm thick sheets (Supplementary Fig. 18a).
The cross-sectional morphology of the TPU/Fe O sheets was analyzed using the optical 3 4 microscope (CX40MRT) (Supplementary Fig. 18b). Agglomeration of magnetic particles increased with higher Fe O content. 3 4
Magnetic induction was tested using a permanent magnet (N35, 35 × 35 × 15 mm3). A Gauss meter (GS-25, AIKESI) measured the magnetic field strength along the central axis of the magnet, with measurements taken at 5 mm intervals from the magnet’s surface. Magnetic induction decreased from 1535.5 Gs to 0 as a function of distance (Supplementary Fig. 18c).
The surface magnetic induction intensity of the TPU/Fe O sheets was measured using the Gauss 3 4 meter. The sheet (100 × 60 × 0.4 mm3) was divided into 16 regions, with five measurements taken per region (Supplementary Fig. 18d). The average magnetic induction intensity initially increased and then decreased with higher Fe O content (Supplementary Fig. 18e). The highest average 3 4 intensity, 1.201 Gs, was observed for the 6/4 ratio , followed by 1.157 Gs for the 5/5 ratio. The reduction at higher Fe O content was attributed to particle aggregation, which compromised 3 4 magnetic field uniformity.
TPU/Fe O strips (6 × 60 × 0.4 mm3) with varying Fe O content were tested for driving 3 4 3 4 performance. Each strip was clamped over a 40 mm length using double-sided tape, leaving a 20 mm free segment for testing, with the free end positioned beneath the permanent magnet (Supplementary Fig. 18f). Vertical displacement under magnetic actuation was recorded (Supplementary Fig. 18g). Increasing Fe O content enhanced magnetic responsiveness but also 3 4 induce particle aggregation, reducing magnetic field uniformity and increasing composite hardness and density. Consequently, actuation performance declined at the 4/6 TPU/Fe O ratio compared 3 4 with the optimal 5/5 ratio. The 5/5 ratio was finally selected for subsequent IM experiments.
The 5/5 TPU/Fe O sheet was cut into pieces for selective bonding 4D IM with cross-shaped, hand- 3 4 shaped, and caterpillar-shaped molds (Fig. 3a, Fig. 5d). Process conditions included T =210 °C, melt p =28 MPa, t =10s, and t =20 s. pack pack cool
The interfacial bonding properties of the hand-shaped TPU part bonded with 5/5 TPU/Fe O were 3 4 characterized using the nanoindenter (G200, KLA Corporation) in compliance with ISO 14577. The bonded portion was cut into measuring 10 × 5 × 1 mm3 and tested in load-displacement mode, with a maximum displacement of 2.5 μm, a holding time of 20 s, and a loading speed of 10 nm s-1. For comparison, the pure TPU part was also measured. The elastic modulus and hardness of the samples were obtained (Supplementary Fig. 19). The magnetic sample showed an average elastic modulus of 0.078 GPa and hardness of 0.01 GPa, higher than those of the pure TPU sample. The elastic recovery rate (R ) was calculated as the ratio of the recovered displacement (Δx) to the r maximum displacement (x ). The R for TPU bonded with TPU/Fe O was 69.1%, lower than max r 3 4 that of pure TPU (80.3%).
∆𝑥 𝑅 = 𝑟 𝑥𝑚𝑎𝑥 (3)
The magnetic performance of the hand-shaped part was tested by positioning the magnetic tip beneath the permanent magnet. Magnetic field-induced deformation was observed at a distance of 14 mm, with full actuation achieved at 11 mm (Fig. 5e). The deformation magnitude depended on the volume of the bonded magnetic piece.
The 4D IM parts were employed as functional soft robotics. The caterpillar-shaped part, equipped with a short metal wire, served as a switch to control an LED light (Fig. 5f, Supplementary Movie 6). Additionally, the soft robotics functioned as magnetic grippers for transferring magnetic objects (Fig. 5g, Supplementary Movie 7).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper addresses the traditional challenge in injection molding (IM) where non-uniform material shrinkage leads to undesirable warpage and dimensional deviations in plastic parts. Instead of minimizing warpage, the authors propose to leverage and control this shrinkage to induce purposeful, programmable deformation, enabling the creation of complex, customized geometries from a single mold.\nThe core contribution is the proposal and demonstration of 4D Injection Molding (4D IM). Two techniques were used to demonstrate these: Localized Thermal Activation 4D IM and Selective In-Mold Bonding 4D IM. Then, the application on composites and elastomers was performed.\nI suggest that the authors address the following comments:\nThe paper should be better divided so that the reader can easily follow the research's progression.\nThe introduction should provide the necessary background and context, explaining more precisely what was done before regarding this issue.\nThe figures (1-2-3) are pretty dense with information, and the labels/text within the images can be small, making them challenging to decipher without significant zooming. Consider increasing font sizes for labels and legends within the figures, or providing higher-resolution images. If possible, simplify some of the more complex diagrams or break them into multiple sub-figures for better clarity. Ensure units are clearly visible and consistently labeled in all figures and graphs.\nThe section \"Methods\" provides details; some of the experimental specifics could be briefly introduced earlier when the concept is first discussed to give a clearer picture of the practical implementation.\nThe paper extensively references \"4D printing\" and \"additive manufacturing\" techniques, highlighting their limitations in mass production. While 4D IM is presented as a solution, a more direct comparison or quantitative analysis of the scalability and cost-efficiency benefits of 4D IM over existing 4D printing methods would strengthen the argument. Please include a more direct, quantitative comparison (e.g., production rate, cost per part, material waste) with existing 4D printing or additive manufacturing techniques that aim for similar customized geometries. This would highlight the \"mass production\" advantage of 4D IM.\nThe statement \"The injection filling time was 0.19 s (Extended Data Fig. 1b)\" (line 294) seems relatively short for an IM process, even for small parts. While simulations might handle this, a brief comment on the implications of such a rapid filling time or its practical feasibility for larger or more complex parts would be beneficial.\nWhile the R² values for RSM models are high, some predicted R² values for composites (e.g., 54.37% for failure strain, line 520) are relatively low. Although the authors state \"acceptable accuracy,\" a more in-depth discussion on the reasons for these lower accuracies, the potential contributing factors (e.g., material variability, complexity of fiber-matrix interaction, measurement challenges), and potential ways to improve them would be valuable.\nA deeper insight into \"Limitations\" would be beneficial in explicitly stating the current constraints or challenges of 4D IM (e.g., complexity of mold design for heating elements, potential for bond displacement under extreme conditions, and the need for further material characterization) even if some are mentioned in the discussion.\nFuture work could be expanded beyond general applications to specific research directions and applications.\nLine 743: Abbreviatios for \"Abbreviations (typo).\nEnsure consistent use of LaTeX for all Δ symbols.", "reviewer_2": "4D injection molding\nThe authors have devised a method to induce controlled warpage during the traditional injection molding (IM) process, by varying the temperature, pressure, time and fillers. With this approach the authors were able to achieve time-morphing/4D structures and have done excessive mechanical characterization. The manuscript is thoroughly prepared and inclusive of all the necessary supplementary data for validation and repeatability.\nThere are few minor comments that need to be addressed:\n1. L53: More emphasis/explanation is needed on Δvt1 and Δvt2. What does Δvt1 causes in-plane and Δvt2 causes out-of-plane deformations?\n2. L63: Few recent papers on warpage control in AM should be cited.\na. Mittal, Y.G., Patil, Y., Kamble, P.P., Gote, G.D., Mehta, A.K. and Karunakaran, K.P., 2024. Warpage control in thermoplastic ABS parts produced through material extrusion (MEX)-based fused deposition modeling (FDM). Rapid Prototyping Journal, 30(9), pp.1822-1835.\n3. L133: The authors have used both DoE using OA and RSM. Can they specify why not just one is used throughout?\n4. L168: Authors should specify the relevant figures for better understanding.\n5. L169: More explanation is needed on minimum deformation potential and how the authors determined it?\n6. L255: The screw RPMs and nozzle temperatures should also be mentioned.\n7. L285: More information on the simulation data (no. of elements, run time, boundary conditions etc.) should be given.\n8. The authors should also explain in brief the material state post IM.\n9. In the thermal profiles, it will be more convenient for the readers if the exact ΔT is also mentioned on the figure.\n10. Fig. 1d: what does the subscript “s” denotes in temperature (Ts1, Ts2, Ts3) and other parameters?\n11. Fig. 2b: Why is the total Z-displacement smaller than the out-of-plane one? It seems counterintuitive. Same with Fig. 3b.\n12. Fig. 2c,d: Can the authors explain the reason to choose these voltage levels?\n13. Fig. 3c: What is the color scale depicting? Is it still voltage? If so, it should be mentioned.\n14. Supplementary Fig. 1: Authors should mention the exact locations of the datum and measurement points.\n15. Fig. 4h,j: The figures are too small and not clear. Suggested to re-shape them.\n16. Extended Data Fig. 1c: Why #14 isotherm is so different than others?\n17. Extended Data Fig. 2b: How did the authors ensure reproducibility of the measurement points across all the samples?\nHow did they accurately mark the exact measurement point on these curved surfaces\n18. Extended Data Fig. 3: (c) should be better explained for readability; (e) the third axis, apart from T and v is not visible.\n19. Extended Data Fig. 4f: Why did the pressure behavior show a minimum? What does it signify?\n20. Extended Data Fig. 5: (d) the authors mentioned in the caption that the 5 depictions are the special combinations. What is the specialty about these? (e) this figure is not clear due to extensive overlapping.\n21. Extended Data Fig. 6: Authors should scientifically explain the difference between: (b) fiber orientation and (c) random fiber orientation. Alos, why does (a) have a different scale factor?\n22. Supplementary Fig. 4: (a) what does the color variation represent? The authors should mention the total number of samples tested in each case; (b) why is the variation on modulus of position 1 so high?\n23. Supplementary Table 3: Why is 16 W used in the Simulation and 24 V in the experiments? Why not have the same units?\n24. Supplementary Table 19: Not clear as to what it is signifying.\n25. Supplementary Video 5: The gripping and un-gripping method/mechanism is not clear.\n26. Few formatting errors:\na. Extended Data Fig. 1: “e” should be bold. Same with Extended Data Fig. 3.\nb. Suppl. Note 1, Para. 2: “T” should be in italics in the line, “Localized heating increased the overall T and altered….”\nc. Suppl. Fig. 4: (A), (B) & (C) are capitalized.\nd. Suppl. Fig. 6: Further explanation is needed for each figure. What is the significance of the last 2 figures in the first row?\nAlso, the difference in the position of the C-shape bonds in the last 2 figures in the second row, should be highlighted.\ne. Suppl. Fig. 10f: The sticking conditions/methods should be mentioned.\nf. Suppl. Table 4: The footnote doesn’t match with the figure referred.\ng. Suppl. Table 21: Column B should be explained with a reference picture for 0, -1 and 1.", "reviewer_3": "The paper presents an advanced 4D Injection Moulding (4D IM) platform capable of programmable deformations, selective in-mould bonding for multi-material and multi-functional structures, and integration of functional elements such as embedded magnets for soft robotics.\nWhile the work demonstrates significant innovation, some improvements should be made to increase the final quality of the paper to be completely ready for submission.\nA general remark: the Conclusion section is missing, together with Future works.\nMore in detail, but also as a suggestion for Future works, there were areas where it was not possible to integrate more of the work done, with the purpose of moving the 4D IM platform from a laboratory-scale proof-of-concept toward a robust, industrially viable manufacturing technology:\nThe feasibility of scaling the localised heating system and multi-point bonding for high-volume production remains unclear.\nThe complexity and cost associated with multiple heating elements and precise voltage control may limit adoption in large-scale manufacturing.\nAlthough experimental correlations are generally strong, some parts, such as the caterpillar-shaped samples, show lower predictive accuracy (R² = 81.57%), indicating variability. Data on repeated production cycles, mould wear, and long-term stability are currently lacking.\nIn TPU/Fe₃O₄ composites, particle aggregation at higher loadings reduces magnetic field uniformity, which may compromise actuation performance. Long-term mechanical behavior, including fatigue and multi-cycle performance, has not been fully investigated.\nDemonstrations of soft robotic functionalities have been limited to controlled laboratory conditions. The performance under practical operating conditions, including higher loads, integration with industrial automation, and electronic systems, has not yet been validated.\nThe current studies provide limited insight into compliance with ISO or other industrial production and quality standards.\nDurability testing, functional certification, and standardization data are largely absent.", "reviewer_4": "Overall an interesting paper but the format must be standardized, 1.introduction, experimental method, 3. results 4. conclusion etc.\nA thorough review of the wording within the text also needs to done."}, "author_response": "We sincerely thank the Editor and all the Reviewers for their time, insightful comments, and constructive criticisms, which have significantly helped us improve our manuscript. We have carefully considered all the points raised and have revised the manuscript accordingly. Our point-by-point responses to the specific comments are detailed below.\nReviewer #1 (Remarks to the Author):\nThe paper addresses the traditional challenge in injection molding (IM) where non-uniform material shrinkage leads to undesirable warpage and dimensional deviations in plastic parts. Instead of minimizing warpage, the authors propose to leverage and control this shrinkage to induce purposeful, programmable deformation, enabling the creation of complex, customized geometries from a single mold.\nThe core contribution is the proposal and demonstration of 4D Injection Molding (4D IM). Two techniques were used to demonstrate these: Localized Thermal Activation 4D IM and Selective In-Mold Bonding 4D IM. Then, the application on composites and elastomers was performed.\nI suggest that the authors address the following comments:\nComment 1: The paper should be better divided so that the reader can easily follow the research's progression.\nResponse: We have added section titles “Introduction” and “Results” to clarify the main text. We would like to note that the structure of our manuscript (Introduction → Results → Discussion → Methods) follows the standard formatting requirement for Nature Communications, as confirmed by the Editor. Therefore, we have maintained this structure.\nComment 2: The introduction should provide the necessary background and context, explaining more precisely what was done before regarding this issue.\nResponse: We have expanded the Introduction section to provide a more comprehensive background on warpage deformation in injection molding and the state-of-the-art in relative techniques, setting a clearer stage for our contribution.\nComment 3: The figures (1-2-3) are pretty dense with information, and the labels/text within the images can be small, making them challenging to decipher without significant zooming. Consider increasing font sizes for labels and legends within the figures, or providing higher-resolution images. If possible, simplify some of the more complex diagrams or break them into multiple sub-figures for better clarity. Ensure units are clearly visible and consistently labeled in all figures and graphs.\nResponse: We have revised all figures to enhance clarity. This includes increasing font sizes for some labels and legends, break complex diagrams into multiple sub-figures for better clarity, and ensuring all units are consistently and clearly visible. We have definitely provided high-resolution versions of all figures upon submission of the revised manuscript.\nComment 4: The section \"Methods\" provides details; some of the experimental specifics could be briefly introduced earlier when the concept is first discussed to give a clearer picture of the practical implementation.\nResponse: As suggested, we have integrated key methodological details (e.g., material types, brief descriptions of the molding and activation setups) into the main text when the concepts are first introduced, providing a clearer picture for the reader without delving into excessive detail upfront.\nComment 5: The paper extensively references \"4D printing\" and \"additive manufacturing\" techniques, highlighting their limitations in mass production. While 4D IM is presented as a solution, a more direct comparison or quantitative analysis of the scalability and cost-efficiency benefits of 4D IM over existing 4D printing methods would strengthen the argument. Please include a more direct, quantitative comparison (e.g., production rate, cost per part, material waste) with existing 4D printing or additive manufacturing techniques that aim for similar customized geometries. This would highlight the \"mass production\" advantage of 4D IM.\nResponse: Thank you for this valuable feedback. We have substantially strengthened the manuscript by adding a direct, quantitative comparison of the scalability and cost-efficiency of 4D IM relative to regular IM, existing 3D and 4D printing. Specifically, we have added a new paragraph in the Introduction section that provides a direct comparison. Further, we have added a comparison Table (Supplementary Table 25) and a new subsection in the Results (“Quantitative comparison of scalability”) that provides side-by-side numerical benchmarks for production rate, cycle time, cost per part, tooling cost, dimensional tolerance, and material waste across 4D IM, conventional IM, AM, and 4D printing. The relative References have also been given. These additions quantitatively highlight the mass-production advantages of 4D IM, showing that it uniquely combines the scalability of injection molding with the geometric programmability of 4D functional transformation, whereas existing 4D printing methods remain limited by low throughput, high per-part cost, and significant material inefficiency.\nComment 6: The statement \"The injection filling time was 0.19 s (Extended Data Fig. 1b)\" (line 294) seems relatively short for an IM process, even for small parts. While simulations might handle this, a brief comment on the implications of such a rapid filling time or its practical feasibility for larger or more complex parts would be beneficial.\nResponse: We have added a comment in the Methods section acknowledging this filling time: “The injection filling time of 0.19 s (Supplementary Fig. 2b), agrees well with the theoretical value derived from the specified injection speed (15 mm/s), cavity volume (2.28 cm3), and screw diameter (32 mm).” The theoretical filling time is calculated as: 2.28/(15*π*32*32/4)=0.189 s. The IM machine (Haitian ZE1200, 32-mm screw diameter, 170-mm stroke) provides a maximum shot volume of 136 cm³, which is significantly larger than the required melt volume (2.28 cm3). The short filling time results from the small cavity volume and is consistent with operating conditions commonly observed in high-speed injection molding (HSIM). Such rapid filling is well within the practical range of HSIM, a widely used industrial process characterized by extremely short filling times (0.05–0.25 s), high injection rates, and thin-wall geometries. For larger parts, the fill time would scale accordingly, and process windows (including filling time) would need to be re-optimized, which is a standard practice in IM.\nComment 7: While the R² values for RSM models are high, some predicted R² values for composites (e.g., 54.37% for failure strain, line 520) are relatively low. Although the authors state \"acceptable accuracy,\" a more in-depth discussion on the reasons for these lower accuracies, the potential contributing factors (e.g., material variability, complexity of fiber-matrix interaction, measurement challenges), and potential ways to improve them would be valuable.\nResponse: We agree that, although most models exhibit high predictive performance, the predicted R² for failure strain (54.37%) is relatively lower. We have added the Pearson correlation coefficient (r) and the relative interpretation criteria [Ref. 49] for a further demonstration in the Result section and the Method section:\n“The developed models (Eqs. 7–10) exhibited solid predictive performance (R2 = 79.5%, 68.09%, 99.21%, and 54.37% for warpage, strength, modulus, and failure stain) and strong linear relationships [49], as indicated by the Pearson correlation coefficient (r = 0.892, 0.825, 0.996, and 0.737) (Supplementary Tables 22,23).”\n“The corresponding Pearson r values were 0.892, 0.825, 0.996, and 0.737, indicating strong (≥0.7) and very strong (≥0.9) linear relationships [49].”\nFurther, we have added a more detailed discussion in Supplementary Note 3 section to clarify the underlying reasons and potential improvements. The relatively lower predicted R² for failure strain is likely due to the complex, stochastic nature of fiber-matrix interactions and potential local variations in fiber orientation, which are challenging to capture perfectly with a phenomenological model. We suggest that future work could incorporate more direct microstructural metrics and integrate interation terms or hybrid machine-learning-assisted matamodels to improve predictive accuracy.\nComment 8: A deeper insight into \"Limitations\" would be beneficial in explicitly stating the current constraints or challenges of 4D IM (e.g., complexity of mold design for heating elements, potential for bond displacement under extreme conditions, and the need for further material characterization) even if some are mentioned in the discussion.\nResponse: We have substantially expanded the “Discussion” section to explicitly articulate the current limitations of the 4D IM platform. The revised section now provides deeper insights into several key constraints:\n(1) the dependence of programmable deformation magnitude and spatial resolution on part geometry (e.g., reduced shape-change capability in thicker regions),\n(2) the increased mold design complexity and energy demand associated with implementing localized thermal activation through integrated heating systems,\n(3) the sensitivity of the selective bonding step to adhesive application quality, including potential variability in bond strength and surface artifacts,\n(4) material-related challenges for composite and elastomer systems, where microstructural heterogeneity may reduce deformation predictability.\nAdditionally, we now highlight future directions targeting these constraints, such as non-contact thermal activation approaches (e.g., directed infrared heating), improved temporary adhesive strategies to ensure reliable yet removable bonding, and the integration of microstructural descriptors or hybrid machine-learning-assisted metamodels for enhanced deformation robustness.\nComment 9: Future work could be expanded beyond general applications to specific research directions and applications.\nResponse: We have revised the \"Discussion\" section to outline specific, actionable research directions for the \"Future Work”:\n“Future developments of the 4D IM framework will target these limitations. One direction is the adoption of non-contact thermal activation methods (e.g., directed infrared heating [60]) to generate spatial T gradients without permanent mold modification, thereby improving flexibility, energy efficiency, and cost-effectiveness. Another is the exploration of alternative bonding strategies, such as temporary high-performance adhesives [61] that form mechanical interlock interfaces between the bond part and cavity surface during molding and can be removed afterward to preserve surface quality. For composites and elastomers, greater material variability and more complex fiber/filler–matrix interactions may reduce model robustness for programmable deformation. Future studies will incorporate additional microstructural descriptors and hybrid machine-learning-assisted metamodels to enhance predictive accuracy and reliability. Performance validation under realistic operating conditions, including higher loads, multi-cycle actuation, and integration with automated or electronic systems, will also be required. Moreover, systematic evaluation of long-term durability, functional certification, and compliance with industrial standards will be essential for transitioning 4D IM from controlled laboratory demonstrations to robust industrial deployment.”\nWe also specify potential application domains where these advancements could have the highest impact, including deployable consumer products, morphing wearable devices, adaptive automotive/aerospace components, and stimuli-responsive structural parts in robotics.\nComment 10: Line 743: Abbreviatios for \"Abbreviations (typo). Ensure consistent use of LaTeX for all Δ symbols.\nResponse: Thank you for catching this. The typo has been corrected, and we have ensured all Δ symbols are consistently formatted throughout the manuscript and supplementary information.\nReviewer #2 (Remarks to the Author):\nThe authors have devised a method to induce controlled warpage during the traditional injection molding (IM) process, by varying the temperature, pressure, time and fillers. With this approach the authors were able to achieve time-morphing/4D structures and have done excessive mechanical characterization. The manuscript is thoroughly prepared and inclusive of all the necessary supplementary data for validation and repeatability.\nResponse: We thank the Reviewer for their positive assessment of our work and their valuable suggestions.\nThere are few minor comments that need to be addressed:\nComment 1. L53: More emphasis/explanation is needed on Δvt1 and Δvt2. What does Δvt1 causes in-plane and Δvt2 causes out-of-plane deformations?\nResponse: We have added a sentence to clarify: “In the temporal dimension, Δv comprises in-mold shrinkage (Δv ) and post-shrinkage (Δv ): Δv occurs during solidification under pressure, constrained by the mold walls, generating internal stresses and in-plane restricted deformation; Δv occurs after ejection, when constraints are removed, producing out-of-plane free deformation with releasing residual stress generated from Δv (Fig. 1c).”\nComment 2. L63: Few recent papers on warpage control in AM should be cited.\na. Mittal, Y.G., Patil, Y., Kamble, P.P., Gote, G.D., Mehta, A.K. and Karunakaran, K.P., 2024. Warpage control in thermoplastic ABS parts produced through material extrusion (MEX)-based fused deposition modeling (FDM). Rapid Prototyping Journal, 30(9), pp.1822-1835.\nResponse: We have added the suggested reference (Mittal et al., 2024) in the Introduction section. While our background discussion focuses primarily on injection molding (IM) and the warpage behavior of IM parts, we do cite a number of studies related to AM and 4D printing. However, warpage-control strategies in AM were not previously discussed in detail because the objective of this manuscript is to translate the deformation mechanisms established in 4D printing into a 4D IM framework. Accordingly, our emphasis has been placed on deformation mechanisms and functional applications rather than AM-specific warpage control. The newly added citation helps strengthen this connection, and we believe the revised Introduction now better reflects the broader context of related research.\nComment 3. L133: The authors have used both DoE using OA and RSM. Can they specify why not just one is used throughout?\nResponse: We have clarified the rationale in the revised manuscript. In our study, the Orthogonal Array (OA) design was used only for the localized thermal activation method, where the aim was to efficiently screen five injection molding parameters and identify the few factors with statistically significant effects on warpage (Supplementary Fig. 3g (originally Extended Data Fig. 1g)). This allowed us to determine optimized processing conditions before conducting the subsequent heating-element modeling.\nThe RSM models were built exclusively for the local heating parameters, where eight groups of heating elements served as the input factors while the injection molding parameters were fixed. We intentionally avoided mixing molding parameters with local heating parameters in a single model to reduce model complexity and prevent compounding uncertainties. A model focused solely on heating parameters provides more stable predictions and a more direct basis for control implementation.\nFor the selective in-mold bonding method, only RSM was employed. Supplementary Fig. 7g and Supplementary Fig. 17 (originally Extended Data Fig. 4f and Extended Data Fig. 8) both present RSM results; however, the caption of Supplementary Fig. 7g (Extended Data Fig. 4f) mistakenly referred to an orthogonal analysis. We have corrected the caption to: “g, Effects of the four IM parameters on the Z-direction distance between measurement points and datum point.”\nComment 4. L168: Authors should specify the relevant figures for better understanding.\nResponse: We have transferred all these Extended Data Figures to the supplementary information file, arranged them logically with the relevant supplementary figures, and renumbered them accordingly. The corresponding content and references of the main text have also been modified, and the content of the main text has been cited and explained in the order of the figures for better understanding.\nComment 5. L169: More explanation is needed on minimum deformation potential and how the authors determined it?\nResponse: We have expanded the explanation in the revised manuscript. In our study, the “minimum deformation potential” refers to the smallest geometric deviation that the selective in-mold bonding method can reliably generate under the given structure constraints. This value is determined primarily by the thickness ratio between the bonded CGF-PA6 prepreg tape (0.3 mm) and the mold cavity (2.0 mm), which results in a mold-to-bond thickness ratio of 6.7:1. Under this constraint, the maximum experimentally observed warpage displacement was 0.96 mm, indicating that deformation remains limited by the relatively thick surrounding cavity structure.\nTo improve clarity, we have revised the sentence in the manuscript to:\n“Given the 0.3 mm thickness of the bonded CGF-PA6 relative to the 2 mm cavity thickness (a mold-to-bond ratio of 6.7:1), the achievable deformation is constrained by both the cavity geometry and the inherently low Δv of SGF-PA6 (0.5–0.7%), with the maximum measured displacement of 0.96 mm representing the minimum deformation potential under these molding conditions.”\nComment 6. L255: The screw RPMs and nozzle temperatures should also be mentioned.\nResponse: We have now included the screw rotational speed and nozzle temperature in the revised Methods section for completeness.\n“The nozzle temperature was identical to T for all IM experiments. The injection speed was set to 15 mm/s, and the screw rotation speed during plasticizing was maintained at 30 rpm.”\nComment 7. L285: More information on the simulation data (no. of elements, run time, boundary conditions etc.) should be given.\nResponse: We have added details on the number of elements, type of mesh, to the Methods section. The key boundary conditions according to the exact IM processing parameters, were given exactly in the numerical simulation sections of the Methods section.\n“Finite element simulations were conducted using Moldex3D. The impeller geometry was modeled, and finite element meshing (tetrahedral mesh) was applied with a surface mesh size of 0.25 mm for the blade (minimum thickness: 0.5 mm) and 4 mm for other components (Supplementary Fig. 3a). The total mesh comprised 1,495,229 elements, including 212,037 for the part, 10,471 for the cold runner, and 1,272,721 for the mold. PP 579S material properties were sourced from the software database and characterized using the Cross-WLF viscosity model and a modified Tait pvT model [61]. IM machine parameters matched the Haitian ZE1200 specifications. The boundary conditions were defined to match the experimental setup. The melt temperature (T ), injection pressure (p ), packing pressure (p ), packing time (t ), and cooling time (t ) were set according to Supplementary Table 1. Orthogonal design with 5-factor and 4-level was conducted to evaluate process parameter effects and identify optimal conditions. Local heating was excluded from this orthogonal investigation. The mold walls were assigned an initial temperature of 20 °C with a thermal conductivity of 29 W/(m·K). For the warpage simulation, the part was assumed to be fully constrained by the mold until ejection at the calculated ejection temperature of 90 °C, after which it was allowed to deform freely under the calculated residual stress field while cooling to an ambient temperature of 20 °C.”\nComment 8. The authors should also explain in brief the material state post IM.\nResponse: The post-molding material state is now clarified more explicitly. As described in the revised Methods section, “After completion of the IM process, all molded parts were allowed to equilibrate at ambient conditions for at least 24 hours prior to deformation measurement.” This ensures that measurements were taken after thermal relaxation and stabilization of residual stresses, providing a consistent and representative material state for all reported warpage and deformation results.\nComment 9. In the thermal profiles, it will be more convenient for the readers if the exact ΔT is also mentioned on the figure.\nResponse: We have added explicit ΔT to the relevant thermal profile figures (Supplementary Fig. 2e and Supplementary Fig. 7c).\nComment 10. Fig. 1d: what does the subscript “s” denotes in temperature (Ts1, Ts2, Ts3) and other parameters?\nResponse: The subscript \"s\" denotes the \"spatial\" of the mold or part. We have clarified this in the caption of Fig. 1: “The subscript “s” represents the spatial dimension, and the subscript “t” represents the temporal dimension.”\nComment 11. Fig. 2b: Why is the total Z-displacement smaller than the out-of-plane one? It seems counterintuitive. Same with Fig. 3b.\nResponse: The seemingly counterintuitive result arises from the fact that the two shrinkage components, Δv and Δv , act in different directions and partially offset each other. Before ejection, Δv develops while the melt is still hot and confined in the mold. Under high pressure and strong mold constraint, this shrinkage occurs mainly in-plane, and its Z-component is relatively small. The direction of the Δv -induced deformation is governed by the pressure and temperature gradients across the thickness, causing the blade to deform slightly along the Z-direction opposite to the gate. After ejection, Δv becomes dominant. This out-of-plane deformation is driven primarily by through-thickness thermal gradients (ΔT=T - T ). The part deflects toward the hotter region—near the runner—because the localized heating at Position 1 is insufficient to reverse this trend in Fig. 2b. Because Δv and Δv act in opposite Z-directions, the total Z-displacement represents their net effect. In Fig. 2b and Fig. 3b, Δv produces a larger Z-displacement in the opposite direction to that induced by Δv ; the dominant out-of-plane Δv deformation is partially counteracted by the smaller, opposite contribution from Δv , yielding a total Z-displacement smaller than the Δv component alone. This behavior is consistent with Moldex3D’s warpage decomposition, in which different shrinkage modes may offset each other.\nA concise explanation of this mechanism has been added to Supplementary Note 1.\nComment 12. Fig. 2c,d: Can the authors explain the reason to choose these voltage levels?\nResponse: The voltage levels shown in Fig. 2c,d were determined through the response surface methodology (RSM). In the face-centered central composite design (FCD), each of the eight heating-element groups was treated as an independent input variable. Because each element operates within a 0–24 V range, we selected three experimental levels—0, 12, and 24 V—with a uniform 12 V increment, consistent with standard FCD requirements (Supplementary Table 4). These levels were used only for constructing and fitting the RSM model.\nAfter model development, the final voltage values used in Fig. 2c,d were obtained by applying a desirability-function optimization to the RSM equations (Eq. 1 for simulation and Eq. 2 for experiment) to achieve the target deformation profile. As we use a DC regulator capable of continuous control between 0–24 V, any optimized voltage value within this range can be practically implemented. Supplementary Table 11 lists these exact optimized voltages alongside the corresponding simulated and experimental deformation results.\nComment 13. Fig. 3c: What is the color scale depicting? Is it still voltage? If so, it should be mentioned.\nResponse: In Fig. 3c, Color variations in the tables represent the relative magnitudes of the five input variables used in the selective in-mold bonding 4D IM: bonding position (I), T (II), p (III), t (IV), and t (V).\nThe colors do not indicate voltage (as in Fig. 2c,d), but instead visualize the normalized level of each variable across the design matrix to help illustrate parameter variations. We have clarified this in the caption of Fig. 3:\n“Color variations in the tables represent value differences for each variable (I–V).”\nComment 14. Supplementary Fig. 1: Authors should mention the exact locations of the datum and measurement points.\nResponse: We have revised Supplementary Fig. 1 by adding explicit dimensional annotations for the datum point and all measurement points. Additionally, we have expanded the figure caption to provide a detailed description of their geometric locations, including the spacing, relative positioning, and the definition of the measurement line used for warpage evaluation.\nComment 15. Fig. 4h,j: The figures are too small and not clear. Suggested to re-shape them.\nResponse: We have separated the original Fig. 4 into two figures (now Fig. 4 and Fig. 5) and remade the panels in the new Fig. 5 with larger size and improved clarity.\nComment 16. Extended Data Fig. 1c: Why #14 isotherm is so different than others?\nResponse: The distinct Z-direction displacement distribution observed for Sample No. 14 in Supplementary Fig. 3 (original version: Extended Data Fig. 1c) arises from the specific combination of injection molding process parameters assigned to this run. For Sample No. 14, the orthogonal design prescribed: T = 250 °C, p = 30 MPa, p = 22 MPa, t = 4 s, and t = 5 s. Under this unique combination, Sample No. 14 exhibited both the smallest measured displacement (Point 0 to Point 2: 0.079 mm) and the smallest overall warpage range among all 16 samples (0.709 mm) (Supplementary Data 1 and Supplementary Fig. 3). Because the total deformation window for Sample No. 14 is much narrower than for the other samples, the relative shape of the displacement distribution becomes more sensitive to subtle variations between its maximum and minimum values, making the isotherm profile appear visually different.\nTo avoid misinterpretation, we have revised Supplementary Fig. 3d. The previous version used negative y-axis values and a baseline located at the bottom of the plot, which could lead readers to incorrectly infer that Sample No. 11 had the smallest curvature. The updated figure now presents the data with a clearer and more intuitive baseline, making it evident that Sample No. 14 indeed exhibits the smallest warpage.\nComment 17. Extended Data Fig. 2b: How did the authors ensure reproducibility of the measurement points across all the samples? How did they accurately mark the exact measurement point on these curved surfaces?\nResponse: As described in the Methods section, “A measurement frame was prepared by fixing three steel nails onto a glass slide with a leveling device and adhesive (Supplementary Fig. 4c). The impeller was placed with its parting surface facing upward. The blade root (1 mm thick) served as the reference plane.” Each impeller inherently contained a mold-defined datum feature at the blade root (near the runner root), allowing consistent positioning of all samples. The primary datum point (No. 0) was defined at the midpoint of the blade root (Supplementary Fig. 1a), providing a fixed reference for locating all subsequent measurement points.\nTo transfer measurement-point coordinates accurately onto the curved blade surfaces, we printed full-scale CAD drawings showing all measurement point locations. Benefiting from the thin and semi-transparent PP blade walls, each part was aligned directly over the drawing, enabling precise manual marking of the measurement points with a fine-tip marker. These marked points were then used for laser-distance measurements (see Supplementary Fig. 4b for examples). These procedures and clarifications have now been incorporated into the revised Methods section.\nAlthough manual marking introduces some inevitable experimental variability, this was mitigated by robust replication. For each experimental condition, ten samples were molded, yielding ten measurements for each designated point. The mean value and its deviation, reported in Supplementary Fig. 4d (right), confirm the measurement consistency. To evaluate repeatability for displacement measurements, we conducted an ANOVA-based analysis. We have used preliminary data from 9 × 10 consecutive moulding cycles for a key sample geometry (the three-blade impeller), covering both regular IM (without localized thermal activation, No. 0) and 4D IM (with localized thermal activation, No. 1–8). Each experiment showed consistent deformation behaviour. Under identical IM conditions without localized heating, 10 parts were produced, and displacement was measured at 8 defined positions for each part. These 80 data points were used to quantify repeatability, summarized in Supplementary Table 4. The measurement system exhibits stable absolute repeatability across all positions, with a pooled standard deviation S = 0.0482. Similarly, the repeatability of 4D IM (localized thermal activation) was assessed by ANOVA. For each activation pattern (No. 1–8), 10 sequential parts were produced, giving 80 measurements per experiment. The resulting pooled standard deviations were 0.0199, 0.0135, 0.0185, 0.0109, 0.0168, 0.0179, 0.0268, and 0.0184, respectively, confirming good repeatability. Details are provided in Supplementary Note 1.\nComment 18. Extended Data Fig. 3: (c) should be better explained for readability; (e) the third axis, apart from T and v is not visible.\nResponse: Extended Data Fig. 3 (now Supplementary Fig. 5) was explained in the third paragraph of Supplementary Note 1, we have now modified the text for clarity:\n“It highlights the variations in p, T, v, and Δv at six cavity points (a–f) through the thickness at Position 8 (Supplementary Fig. 5c,d). Under the No. 8 heating condition (compared with No. 0 without localized heating), localized heating slows the cooling process, resulting in a longer packing time of p and larger ΔT and Δv. These amplified thermal gradients alter molecular relaxation and the evolution of v across the thickness (Supplementary Fig. 5e), producing differential Δv and the stress imbalance that drives warpage.”\nTo improve readability, we have labeled figure panels c–f and revised the caption as follows:\n“c, Time (t) dependent evolution of pressure (p), T, v, and part shrinkage (Δv) at Position 8, measured at six through-thickness points (a–f). Curves are shown for both processing conditions: No. 0 (without localized heating) and No. 8 (with localized heating at Position 8). d, Coupled relationships among p, T, and v during the IM process.”\nFor Extended Data Fig. 3e (now Supplementary Fig. 5f), we have revised the plot to ensure that the axes for p and T are clearly visible.\nComment 19. Extended Data Fig. 4f: Why did the pressure behavior show a minimum? What does it signify?\nResponse: As we briefly noted in the Methods section when discussing Extended Data Fig. 4f (now Supplementary Fig. 7g), “The effect of p was most significant, followed by T ”, and “Increasing p initially decreased warpage displacement but then increased it at the highest p (35 MPa), indicating greater residual stress at the bonding position.” To clarify the observed minimum in the pressure–response curve, we have expanded the explanation in the Methods section as follows:\n“Increasing p improved melt compensation and enhanced material consolidation at the bonding interface, which lowered shrinkage gradients and reduced warpage. However, at higher p (35 MPa), the melt became over-compressed, creating larger thermal gradients and higher frozen-in residual stresses, especially near the bonding interface, which increased warpage after demolding. The minimum in the curve therefore reflects the balance between these two competing effects.”\nComment 20. Extended Data Fig. 5: (d) the authors mentioned in the caption that the 5 depictions are the special combinations. What is the specialty about these? (e) this figure is not clear due to extensive overlapping.\nResponse: We have clarified the meaning of the “special combinations” in the caption of Extended Data Fig. 5d (now Supplementary Fig. 9e). The revised caption now states:\n“Warpage and Z-direction displacement for specially selected combinations of bonding positions, including: No bond (0,0,0,0), Bond (-1,-1,-1,-1), Bond (1,1,1,1), Bond (1,-1,-1,1), Bond (-1,1,1,-1), Bond (1,1,-1,-1), Bond (-1,-1,1,1), Bond (1,0,-1,1), Bond (-1,0,1,-1), and Bond (1,-1,1,-1), corresponding to Runs 41, 1, 81, 57, 25, 9, 73, 60, 24, and 21 in Supplementary Table 20.”\nThese 10 combinations were intentionally selected from the full 81-run factorial design to represent symmetric patterns, extreme cases, and mixed bonding configurations. They illustrate how different spatial bonding schemes influence warpage behavior more clearly than the full dataset.\nRegarding Extended Data Fig. 5e (now Supplementary Fig. 9d), the extensive overlap results from plotting all 81 runs simultaneously. Since overlap is unavoidable due to data volume, Supplementary Fig. 9d is intended primarily to show the “overall variation range” of the full design. For clarity, we therefore separately visualized the 10 representative cases in Supplementary Fig. 9e, and we direct readers to Supplementary Table 20 for detailed numerical values.\nTo clearly show this figure, we have deleted one case Bond (-1,1,-1,1) and rearranged the positions.\nComment 21. Extended Data Fig. 6: Authors should scientifically explain the difference between: (b) fiber orientation and (c) random fiber orientation. Alos, why does (a) have a different scale factor?\nResponse: We have added the scientific explanations in Supplementary Note 3. Extended Data Fig. 6a (now Supplementary Fig. 13) presents the total Z-direction warpage displacement, whereas Figs. b–e show the individual effect displacements corresponding to specific mechanisms.\nIn Extended Data Fig. 6b (now Supplementary Fig. 13b), the bonded CGF-PA6 prepreg contains continuous glass fibers, whose orientations are defined in the simulation according to the prepreg’s weft and warp directions. These continuous fibers strongly restrict local shrinkage at the bonding region. As described in the Results section, this induces bending toward the bonding side (positive Z-direction). The weft-aligned prepreg produces significantly larger bending than the warp-aligned one, because the weft direction corresponds to the dominant fiber orientation and stiffness.\nIn Extended Data Fig. 6c (now Supplementary Fig. 13c), the SGF-PA6 base material contains short, randomly distributed glass fibers. Their orientation is primarily along the filling direction (X-direction), but the presence of the inserted CGF-PA6 prepreg disturbs the SGF orientation field. This results in different orientation-induced shrinkage patterns under the weft vs. warp configurations of the prepreg. Unlike the continuous-fiber case, the random SGF orientation effect drives bending toward the negative Z-direction, opposite to the CGF effect.\nFig. 6a uses a higher scale factor because the total Z-displacement is smaller than some of the individual effect displacements shown in Figs. 6b–e. A larger scale factor is therefore necessary to make the final warpage shape visually distinguishable from the component effect plots.\nComment 22. Supplementary Fig. 4: (a) what does the color variation represent? The authors should mention the total number of samples tested in each case; (b) why is the variation on modulus of position 1 so high?\nResponse: The color variation in Supplementary Fig. 4a (now Supplementary Fig. 8a) represents the differences in nanoindenter load–displacement responses measured at multiple positions on the samples. We have added the explanation in the figure caption: “The load-displacement profiles, measured at seven positions on the sample, are plotted as individually colored curves”.\nIn total, seven test points were measured on each of the three samples (Bond 1, Bond 2, Bond 3). We have added this in the revised Methods section: “Seven test points were randomly measured on each of the three samples (Bond 1–3)”.\nThe larger variation in modulus at Bond 1 (Supplementary Fig. 8b) arises from its higher sensitivity to local temperature, pressure fluctuations, and frozen-in residual stress when the small insert Bond 1 is placed at the blade root. As clarified in the Methods section: Bond 1 shows significant deviations in load–displacement curves, indicating stronger local heterogeneity. Bonds 2 and 3 exhibit much smaller deviations, reflecting more uniform bonding quality. This behavior is consistent with differences in melt flow, local heating, and re-solidification conditions at the three bonding positions (Supplementary Fig. 8c).\nComment 23. Supplementary Table 3: Why is 16 W used in the Simulation and 24 V in the experiments? Why not have the same units?\nResponse: The difference arises from the parameter requirements of the respective systems. In the simulation environment, the heater must be defined using power (W), as the software does not allow voltage-based boundary conditions. In contrast, our experimental setup uses a DC regulator, which operates with voltage (V) control and enables real-time voltage monitoring and adjustment. Although the units differ, both inputs deliver equivalent heating performance because the electrical power in experiments is determined by the applied voltage and the system’s electrical resistance.\nComment 24. Supplementary Table 19: Not clear as to what it is signifying.\nResponse: Supplementary Table 19 (now Supplementary Table 20) has been revised for clarity. The updated caption now reads: “Supplementary Table 20. Bonding positions in the caterpillar-shaped cavity corresponding to Fig. 3e (Bond 1, 2, 3, 4) and Supplementary Figure 9b (Bond 1’, 2’, 3’, 4’). The cavity consists of eight sequential sections from the gate to the end (Thick 1-Thin 1-Thick 2-Thin 2-Thick 3-Thin 3-Thick 4-Thin 4). “x” indicates the specific bonding position.\nComment 25. Supplementary Video 5: The gripping and un-gripping method/mechanism is not clear.\nResponse: The gripping/un-gripping mechanism of the 4D IM TPU hand-shaped parts relies on residual stress, nonuniform shrinkage, and gradient solidification during molding, which generate curling in the fingers. This provides a passive, reversible actuation mechanism without requiring external heating or pneumatic/electrical systems:\n(1) Passive gripping (Curl-induced closure): Each TPU finger contains a curvature determined by the local thermal history, cooling gradients, and mold-part interactions during 4D IM. When assembled with a foldback clip (Supplementary Video 5), the pre-curled fingers face inward. Releasing the external clip force allows the intrinsic curling moment of the TPU fingers to close around an object, achieving a passive gripping state.\n(2) Manual un-gripping (Elastic reopening): TPU’s high elasticity allows fingers to straighten when an external force is applied, either manually or via a simple mechanism. After the object is removed or the force is released, the stored elastic energy, combined with the curvature, returns the fingers to their curled shape.\nThis design creates a body-powered soft gripper: opening is externally initiated, while closing occurs autonomously via the programmed shape recovery. The gripping force is determined by the curvature and stiffness of each finger, allowing handling of objects with varying sizes and shapes. Different molded curvatures (Fig. 5c) yield variable gripping strengths and contact patterns.\nWe have added the Supplementary Note 4 to address this gripping and un-gripping method/mechanism.\nComment 26. Few formatting errors:\na. Extended Data Fig. 1: “e” should be bold. Same with Extended Data Fig. 3.\nResponse: This has been corrected.\nb. Suppl. Note 1, Para. 2: “T” should be in italics in the line, “Localized heating increased the overall T and altered….”\nResponse: This has been corrected.\nc. Suppl. Fig. 4: (A), (B) & (C) are capitalized.\nResponse: This has been corrected.\nd. Suppl. Fig. 6: Further explanation is needed for each figure. What is the significance of the last 2 figures in the first row? Also, the difference in the position of the C-shape bonds in the last 2 figures in the second row, should be highlighted.\nResponse: The caption of Supplementary Fig. 6 (now Supplementary Fig. 11) has been substantially revised. The subfigure is now labeled as a, b, and c, and a more detailed explanation has been added:\n“Supplementary Figure 11. Selective in-mold bonding integrated with localized thermal activation for the PP impeller part with ‘C’-shaped bond parts. a, Voltage settings for the eight groups of heating elements in the localized thermal activation system, identical to the settings used to achieve the 3-mm target displacement shown in Fig. 1d. Color variations in the tables and heating elements indicate voltage differences. The image on the right displays the exact voltage values on the controller screen. b, Prepared impeller with ‘C’-shaped bond parts shown from two viewing angles to illustrate the 3D geometry of the insert-bond configuration. c, Final positions of the ‘C’-shaped bond parts on each blade (highlighted with green dashed lines) and the corresponding side views. Variations in bond-part locations resulted in different torsional deformations. For Blade No. 1, the C-shaped bond part is positioned slightly to the left along the blade (from root to tip). The right-side region experienced a higher temperature, inducing a clockwise torsional deformation. The realized blade angle is +8°. For Blade No. 2, the bond part is positioned slightly to the right, shifting the higher-temperature region to the left side and producing a counterclockwise twist. The resulting blade angle is –13°. For Blade No. 3, no torsional angle was obtained because the bond part migrated to the blade tip during filling due to high temperature and shear flow.”\ne. Suppl. Fig. 10f: The sticking conditions/methods should be mentioned.\nResponse: We have now revised the relevant section and added the sticking conditions/methods for Suppl. Fig. 10f (now Supplementary Fig. 18f) in the Methods section:\n“TPU/Fe O strips (6 × 60 × 0.4 mm3) with varying Fe O content were tested for driving performance. Each strip was fixed over a 40 mm length using double-sided tape, leaving a 20 mm free segment for testing, with the free end positioned beneath the permanent magnet (Supplementary Fig. 18f).”\nf. Suppl. Table 4: The footnote doesn’t match with the figure referred.\nResponse: We thank the reviewer for pointing out this inconsistency. The referred figure is Supplementary Fig. 4c. We have now corrected the footnote accordingly.\ng. Suppl. Table 21: Column B should be explained with a reference picture for 0, -1 and 1.\nResponse: We have revised the footnote for Column B to clarify the meaning of each code: “Fiber directions: 1 (weft, aligned with flow/filling direction), –1 (warp, perpendicular to flow direction), and 0 (no fiber). The corresponding weft and warp orientations are illustrated in Supplementary Fig. 12.” In Supplementary Fig. 12, we have also added arrows to clearly indicate the weft and warp directions of the continuous fiber orientations.\nReviewer #3 (Remarks to the Author):\nThe paper presents an advanced 4D Injection Moulding (4D IM) platform capable of programmable deformations, selective in-mould bonding for multi-material and multi-functional structures, and integration of functional elements such as embedded magnets for soft robotics.\nWhile the work demonstrates significant innovation, some improvements should be made to increase the final quality of the paper to be completely ready for submission.\nResponse: We thank the Reviewer for recognizing the \"significant innovation\" of our work and for providing constructive suggestions to enhance its impact.\nComment 1: A general remark: the Conclusion section is missing, together with Future works.\nResponse: We would like to note that the structure of our manuscript (Introduction → Results → Discussion → Methods) follows the standard formatting requirement for Nature Communications, as confirmed by the Editor. Therefore, we have maintained this structure. However, within this framework, we have added text about “Conclusion” and “Future works” in the Discussion section to summarize the key findings and contributions of the work as well as the future outlook.\nComment 2: More in detail, but also as a suggestion for Future works, there were areas where it was not possible to integrate more of the work done, with the purpose of moving the 4D IM platform from a laboratory-scale proof-of-concept toward a robust, industrially viable manufacturing technology: The feasibility of scaling the localised heating system and multi-point bonding for high-volume production remains unclear. The complexity and cost associated with multiple heating elements and precise voltage control may limit adoption in large-scale manufacturing.\nResponse: We fully agree that transitioning from laboratory demonstrations to an industrially viable process is a crucial next step. In the revised manuscript, we have expanded the Discussion to clearly address the scalability considerations of the localized heating system and multi-point bonding strategy. We have included a more detailed analysis of potential engineering routes for scaling and highlighted these as important directions for future development. “Localized thermal activation requires tooling with integrated heating elements, which increases mold complexity, energy consumption, and cost. Selective in-mold bonding currently relies on adhesive application, introducing potential variability in bond strength and surface finish, and bond displacement under high temperature and high-shear melt flow remains a critical challenge. Future developments of the 4D IM framework will target these limitations. One direction is the adoption of non-contact thermal activation methods (e.g., directed infrared heating60) to generate spatial T gradients without permanent mold modification, thereby improving flexibility, energy efficiency, and cost-effectiveness. Another is the exploration of alternative bonding strategies, such as temporary high-performance adhesives61 that form mechanical interlock interfaces between the bond part and cavity surface during molding and can be removed afterward to preserve surface quality.”\nComment 3: Although experimental correlations are generally strong, some parts, such as the caterpillar-shaped samples, show lower predictive accuracy (R² = 81.57%), indicating variability.\nResponse: We appreciate the Reviewer’s observation regarding the lower predictive accuracy for the caterpillar-shaped samples. We agree that this accuracy is lower than that obtained for the other geometries. The primary reason lies in the significantly higher geometric complexity of the caterpillar design: it contains five alternating thick sections, which introduce strong thickness-dependent shrinkage effects and more pronounced spatiotemporal gradients during solidification. These nonlinear effects interact sensitively with processing parameters, leading to greater variability and reduced predictability compared with the cross-shaped and hand-shaped parts whose geometry varies mainly within a single plane.\nComment 4: Data on repeated production cycles, mould wear, and long-term stability are currently lacking.\nResponse: Data on repeatability, mould wear, and long-term stability have now been partially addressed. Repeatability in injection moulding depends on part geometry, material, and the selected response parameter (e.g., mass, dimensions, or displacement). To evaluate repeatability for displacement measurements, we conducted an ANOVA-based analysis. We have added preliminary data from 9 × 10 consecutive moulding cycles for a key sample geometry (the three-blade impeller), covering both regular IM (without localized thermal activation, No. 0) and 4D IM (with localized thermal activation, No. 1–8). Each experiment showed consistent deformation behaviour. Under identical IM conditions without localized heating, 10 parts were produced, and displacement was measured at 8 defined positions for each part. These 80 data points were used to quantify repeatability, summarized in Supplementary Table 4. The measurement system exhibits stable absolute repeatability across all positions, with a pooled standard deviation S = 0.0482. Similarly, the repeatability of 4D IM (localized thermal activation) was assessed by ANOVA. For each activation pattern (No. 1–8), 10 sequential parts were produced, giving 80 measurements per experiment. The resulting pooled standard deviations were 0.0199, 0.0135, 0.0185, 0.0109, 0.0168, 0.0179, 0.0268, and 0.0184, respectively, confirming good repeatability. Details are provided in Supplementary Note 1.\nWe agree that mould wear, long-term machine stability, and durability across extended production cycles are important for industrial adoption. However, these factors depend strongly on equipment configuration and maintenance and are outside the scope of this initial technological study. Moreover, unlike conventional IM, the proposed 4D IM concept intentionally leverages controlled, localized thermal instability to produce diverse deformation outcomes from a single mould. A full durability evaluation—including mould longevity, machine cycling performance, material fatigue, and multi-cycle functional consistency—represents essential future work, which we have now noted explicitly in the Outlook section.\nComment 5: In TPU/Fe₃O₄ composites, particle aggregation at higher loadings reduces magnetic field uniformity, which may compromise actuation performance. Long-term mechanical behavior, including fatigue and multi-cycle performance, has not been fully investigated.\nResponse: We appreciate the Reviewer’s insights regarding particle aggregation and multi-cycle mechanical performance in TPU/Fe₃O₄ composites. We have expanded the discussion in the Method section: “Increasing Fe O content enhanced magnetic responsiveness but also induce particle aggregation, reducing magnetic field uniformity and increasing composite hardness and density. Consequently, actuation performance declined at the 4/6 TPU/Fe O ratio compared with the optimal 5/5 ratio.” We also now explicitly state that investigating long-term fatigue and cyclic performance of these composites is a key direction for future work in the Discussion section.\nComment 6: Demonstrations of soft robotic functionalities have been limited to controlled laboratory conditions. The performance under practical operating conditions, including higher loads, integration with industrial automation, and electronic systems, has not yet been validated.\nResponse: We appreciate the Reviewer’s comment regarding the validation of soft robotic functionalities under practical conditions. In this study, our demonstrations were performed in controlled laboratory settings to establish proof-of-concept and validate the actuation principles and programmable deformation of the 4D IM platform. We acknowledge that performance under higher loads, integration with industrial automation, and compatibility with electronic control systems remain to be investigated. Future work will focus on scaling these demonstrations to realistic operating environments, evaluating multi-cycle performance, mechanical robustness, and system integration, thereby bridging the gap between laboratory validation and industrial deployment.\nComment 7: The current studies provide limited insight into compliance with ISO or other industrial production and quality standards. Durability testing, functional certification, and standardization data are largely absent.\nResponse: We thank the Reviewer for highlighting the importance of standardization, industrial quality frameworks, and durability assessments. We agree that these aspects are essential for transitioning 4D IM from laboratory-scale demonstrations toward industrial adoption.\nFirst, we would like to clarify that our study already references relevant ISO standards where applicable. For example, ISO 20457:2018 (“Plastics moulded parts — Tolerances and acceptance conditions”) is cited in the main text (Ref. 21) to contextualize the influence of shrinkage on dimensional tolerances and warpage in injection-molded parts. Since our work intentionally pursues the opposite direction—programmed deformation rather than its suppression—this standard does not directly apply as a performance benchmark for evaluating the outcomes of 4D IM.\nRegarding measurement and testing, we agree that standardized methods are essential. While there is no dedicated ISO standard for deformation-displacement measurement in warped or shape-morphing IM parts, we employed a commercial non-contact laser displacement sensor (HG-C100), which avoids probe-induced elastic deformation inherent to contact methods. All measurements were analyzed using average, maximum, minimum, and standard deviation values to ensure statistical reliability.\nAll equipment used in this study—including the injection molding machine (Haitian ZE1200), infrared thermal imager (TUi260B), laser displacement sensor (HG-C100), torque blender (RM-200C), nanoindenter (G200), force gauge (SMF-20), optical microscope (CX40MRT), and Gauss meter (GS-25)—are standard commercial instruments. The polymers (PP 579S, TPU-6085F) were also commercial materials, and their physical properties were characterized using standard test methods such as ISO 1133, ISO 1183, and ASTM D792. These standardized data were subsequently used as material inputs in the Moldex3D simulations.\nFor the characterization of composite and elastomer parts, standardized mechanical tests were used wherever applicable. Specifically, bending tests on CGF/SGF-PA6 parts followed ISO 14125:1998, and nanoindentation experiments for CGF/SGF-PA6 and TPU/Fe₃O₄ samples were conducted according to ISO 14577 (now explicitly added in the Methods section). These provide standardized mechanical characterization data supporting the robustness of our conclusions.\nThe RSM modeling for programmable deformation followed established statistical standards, including full ANOVA, with reporting of F-values, p-values, Std. Dev. (standard deviation), coefficients of variation (C.V.), R2, adjusted R2, predicted R2, and Adequate Precision.\nWe acknowledge the Reviewer’s concern regarding durability and long-term functional performance. Functional demonstrations (such as soft robotic actuation) are more challenging to standardize due to the diversity of geometries, material combinations, and application-specific requirements. These functionalities are inherently performance-driven rather than governed by universal ISO norms.\nNevertheless, when 4D IM is translated to specific industrial applications, process- and product-specific standards can certainly be co-developed with industrial partners. Nonetheless, we have added key durability-related properties of the TPU material used—density (ASTM D792), tensile strength (ASTM D412), elongation at break (ASTM D412), tear strength (ASTM D624), and UV resistance (ISO 105-A02)—to the Methods section. These properties provide baseline insight into material reliability.\nDurability of the full 4D IM system—including mold longevity, machine cycling, part fatigue, and multi-cycle functional performance—is indeed important but lies beyond the scope of this initial technological study. We have now explicitly stated in the Discussion that systematic durability evaluation, functional certification, and compliance with industrial standards will form an essential part of future work toward industrial deployment of 4D IM.\nReviewer #4 (Remarks to the Author):\nOverall an interesting paper but the format must be standardized, 1.introduction, experimental method, 3. results 4. conclusion etc. A thorough review of the wording within the text also needs to done.\nResponse: We thank the Reviewer for their positive assessment of our paper as “interesting.” Regarding the manuscript structure, we would like to clarify that our current format (Introduction → Results → Discussion → Methods) follows the required structure for Nature Communications, as also confirmed by the Editor. Accordingly, we have maintained this organization. However, we have thoroughly revised the manuscript to enhance coherence and logical flow, as also suggested by Reviewer #1. In addition, we have conducted a comprehensive line-by-line edit to improve clarity, consistency, and wording throughout. We believe these revisions have substantially strengthened the manuscript, and we hope it is now suitable for publication in Nature Communications."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thanks to the Authors for the interesting and exhaustive work they have done, as well as for responding to all my comments.", "reviewer_3": "I have carefully reviewed the authors’ responses and the revised manuscript. All comments raised during the review process have been adequately addressed, and the manuscript has been substantially improved in terms of clarity, methodological rigor, and completeness.\nIn its current form, I consider the manuscript suitable for publication and recommend acceptance."}, "author_response": ""}]

Decision letter