Xiao Shang P.Eng.

This full-sized DED system is equipped with a powerful 1kw IPG laser, two powder feeding hoppers, and a user-friendly interface. Materials available for printing includes 316L stainless steel, PH-17-4 stainless steel, Ni superalloys and more. It also supports in-situ monitoring with a CCD camera and a high-speed IR camera. Upgrading work is ongoing for fully autonomous closed-loop control.

AIDED - Accurate Inverse process optimization framework in laser Directed Energy Deposition

Associated with LEMAM @ UofT

Optimizing the processing parameter for DED, and in fact for AM in general is chanllenging but at the same time crucial. Being able to fast and accurately identifying the optimal processing parameters reduces the cost, materials waste, and more importantly, improves the build quality.
We use machine learning and computer vision methods to study the melt pool and melt track variations under various processing parameters in order to create a framework for the esay identification of optimal processing parameters.

Please check out the published paper:
Shang, X., ..., & Zou, Y. (2025). Accurate Inverse process optimization framework in laser Directed Energy Deposition. Additive Manufacturing

Application-specific materials-by-design is a long-standing challenge due to the need for capturing the complex process-microstructure-property (P-S-P) relations. The efficient identification of microstructures inversely from target mechanical properties is intractable because of the high dimensionality of the design space. So far, there has only been limited preliminary investigations on establishing surrogation P-S-P models with over-simplified material representations forwardly (as opposite to inverse design). For the arguably more important inverse design problem, there have been even fewer reports. In this work, we provide an end-to-end framework that tackles both forward prediction and inverse exploration to streamline materials-by-design. Using advanced deep-learning and genetic algorithm, our framework exhibits promising potential in cutting down the time needed from target mechanical properties directly to desired material microstructure.

Our poster won a poster prize award from Digital Discovery at the 2023 Accelerate Conference by the Acceleration Consortium!

Please check out the published paper:
Shang, X., Liu, Z., Zhang, J., Lyu, T., & Zou, Y. (2023). Tailoring the mechanical properties of 3D microstructures: A deep learning and genetic algorithm inverse optimization framework. Materials Today

Multifunctional materials (MFMs) exhibit multiple useful functions without complex joint mechanisms. Compared with soft MFMs, metallic MFMs are desired due to their superior mechanical properties and durability. However, the design and fabrication of MFMs are daunting tasks, requiring the organic integration of multiple metals and alloys during manufacturing processes. Additive manufacturing, especially directed energy deposition (DED), has the potential in this regard due to its capability to deposit multimaterials site-specifically. Through carefully programmed printing paths, novel applications arise and achieve functionalities unseen before. Here, we showcase the versatility of such techniques by four case studies of metallic MFMs: (i) tuning magnetic lifting force, (ii) securely and durably embedding information as material fingerprints, (iii) improving conventional ferrofluidic seals, and (iv) possessing programmable mechanical properties. This work demonstrates the possibilities of using DED to achieve multifunctionalities in metallic materials and provides enlightenment to a wide range of engineering applications.

Please check out the published paper:
Shang, X., ..., & Zou, Y. (2026). Additive Manufacturing of Multimetallic Materials to Achieve Multifunctionality. MetalMat

In this work, we employ the AM method to fabricate a new type of mesoscale HM: bi-metallic heterostructured steels (HSs), consisting of 316L and PH17–4 stainless steels that possess distinct tensile strength and ductility. By tuning the ratios and fractions of 316L/PH17–4, we achieve 27.3%, 11.5%, and 1.8% improvements in the tensile ductility, ultimate tensile strength, and yield strength over 316L, respectively. Such enhanced tensile properties are mainly attributed to (i) confined-necking co-deformation, where the necking of PH17–4 is confined and delayed by 316L, offering more work hardening to the whole sample, (ii) switching between necking-induced and shear-induced failure modes, and (iii) the HDI hardening effect. This study paves the way for understanding and designing bi-metallic HSs with improved tensile properties using AM methods.

Please check out the published paper:
Shang, X., ..., & Zou, Y. (2026). Confined necking and improved tensile ductility in heterostructured bi-metallic steels made by additive manufacturing. Acta Materialia

Bistable Auxetic Metamaterials (BAMs) are a class of monolithic perforated periodic structures with negative Poisson’s ratio. Under tension, a BAM can expand and reach a second state of equilibrium through a globally large shape transformation that is ensured by the flexibility of its elastomeric base material. However, if made from a rigid polymer, or metal, BAM ceases to function due to the inevitable rupture of its ligaments. The goal of this work is to extend the unique functionality of the original kirigami architecture of BAM to a rigid solid base material. We use experiments and numerical simulations to assess performance, bistability, and durability of rigid BAMs at 10,000 cycles. Geometric maps are presented to elucidate the role of the main descriptors of the BAM architecture. The proposed design enables the realization of BAM from a large palette of materials, including elastic-perfectly plastic materials and potentially brittle materials.

To find our more about this work:
Shang, X., Liu, L., Rafsanjani, A. et al. Durable bistable auxetics made of rigid solids. Journal of Materials Research 33, 300–308 (2018). https://doi.org/10.1557/jmr.2017.417. Full-text paper
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