Search for: All records

Laser powder bed fusion (LPBF) enables fabrication of complex metal components but remains limited by residual stress accumulation and part deformation. Most existing scan sequence generation strategies for LPBF rely on heuristic rules or empirical optimizations that are suboptimal, difficult to generalize across geometries, and insensitive to the underlying physics of the problem. The SmartScan framework was developed to overcome these limitations through model-based and optimization-driven scan sequence generation. SmartScan 1.0 employed a thermal model to optimize temperature uniformity, leading to significant reductions in residual stress and distortion compared to state-of-the-art heuristic approaches. However, its formulation ignored the mechanical aspects of residual stress and deformation. To address this deficiency, a preliminary study introduced SmartScan 2.0 (Pre) which utilized a decoupled linear thermomechanical formulation for scan sequence optimization for 2D geometries. Building on this foundation, this paper proposes SmartScan 2.0 based on a sequentially coupled linear thermoelastic model that simultaneously solves temperature and displacement fields to minimize thermally induced elastic deformation in 3D geometries. The computational efficiency of SmartScan 2.0 is enhanced through nondimensional scaling. Experimental validation on 3D LPBF specimens shows that SmartScan 2.0 achieves up to 69.0% reduction in residual stress and 17.4% reduction in deformation relative to SmartScan 1.0, and up to 60.6% reduction in residual stress and 12.8% reduction in deformation compared with SmartScan 2.0 (Pre). This work establishes the superiority of scan sequence optimization using coupled linear thermomechanical models over the existing thermal-only or decoupled thermomechanical approaches, without significantly sacrificing computational efficiency. 

The structural flexibility of industrial robot arms makes them vibrate when they are commanded to move at fast operation speeds. Among the control strategies, feedforward control stands out as an interesting approach to suppress vibration since it does not create stability issues and works for repeating and non-repeating tasks. Currently, the state-of-the-art feedforward controller dedicated to suppressing residual vibration in robot arms is time-varying input shaping (TVIP). However, TVIP falls short in trajectory tracking tasks since the method adds delays in the commands creating errors in tracking and thereby contouring trajectories. Therefore, this paper proposes the use of an alternate feedforward method, known as the filtered B-splines (FBS) approach, to suppress vibration in six DOF robots while maintaining tracking accuracy. Since time-varying FBS (TVFBS) requires full frequency response functions (FRFs), compared to only natural frequencies and damping ratios for TVIP, we propose a framework for estimating the FRFs of serial kinematic chain 6-degree-of-freedom robots. Residual vibration reduction experiments and trajectory tracking experiments, in which the dynamics of a UR5e collaborative robot change considerably, were carried out to validate the model prediction framework. TVFBS reduced the end-effector vibration by 87% while improving tracking performance in both the y (22%) and z (29%) directions. On the other hand, TVIP worsened the tracking performance (-683.43% for the y and -662.37% for the z direction) despite the excellent vibration reduction (98%). Hence, TVFBS demonstrated significantly better tracking performance than TVIP while retaining comparable vibration reduction. 

Frog-leg robots are widely used for wafer-handling in semiconductor manufacturing. A typical frog-leg robot uses a magnetic coupler to achieve contactless transmission of motion between its driving motors, which operate at atmospheric pressure, and its end effector (blade) which operates within a vacuum chamber. However, the magnetic coupler is a lowstiffness transmission element that induces residual vibration during fast motions of the robot. Excessive residual vibration can cause collisions between the fragile wafer carried by the robot and cassette, hence damaging the wafer. While this problem could be solved by slowing down the robot, it comes at the cost of reduced productivity, which is undesirable. Therefore, this paper reports a preliminary investigation into input shaping (a popular vibration compensation technique) as a tool to reduce residual vibration of a frog-leg robot during high-speed motions. Two types of motions of the robot are considered: rotation and extension. A standard input shaper is shown to be very effective for mitigating residual vibration caused by rotational motion but is much less effective for extensional motion. The rationale is that the resonance frequencies of the robot are constant during rotation but they vary significantly during extension, hence reducing the effectiveness of standard input shaping. This necessitates the use of more advanced input shapers that can handle varying resonance frequencies to mitigate residual vibration during extensional motion in future work. 

The future of intelligent manufacturing machines involves autonomous selection of process parameters to maximize productivity while maintaining quality within specified constraints. To effectively optimize process parameters, these machines need to adapt to existing uncertainties in the physical system. This paper proposes a novel framework and methodology for feedrate optimization that is based on a physics-informed data-driven digital twin with quantified uncertainty. The servo dynamics are modeled using a digital twin, which incorporates the known uncertainty in the physics-based models and predicts the distribution of contour error using a data-driven model that learns the unknown uncertainty on-the-fly by sensor measurements. Using the quantified uncertainty, the proposed feedrate optimization maximizes productivity while maintaining quality under desired servo error constraints and stringency (i.e., the tolerance for constraint violation under uncertainty) using a model predictive control framework. Experimental results obtained using a 3-axis desktop CNC machine tool and a desktop 3D printer demonstrate significant cycle time reductions of up to 38% and 17% respectively, while staying close to the error tolerances compared to the existing methods. 

Abstract Over the last few decades, globalization has weakened the US manufacturing sector. The COVID-19 pandemic revealed import dependencies and supply chain shocks that have raised public and private awareness of the need to rebuild domestic production. A range of new technologies, collectively called Industry 4.0, create opportunities to revolutionize domestic and local manufacturing. Success depends on further refinement of those technologies, broad implementation throughout private companies, and concerted efforts to rebuild the industrial commons, the national ecosystem of producers, suppliers, service providers, educators, and workforce necessary to regain a competitive, innovative manufacturing sector. A recent workshop sponsored by the Engineering Research Visioning Alliance (ERVA) identified a range of challenges and opportunities to build a resilient, flexible, scalable, and high-quality manufacturing sector. This paper provides a strategic roadmap for regaining US manufacturing leadership by briefly summarizing discussions at the ERVA-sponsored workshop held in 2023 and providing additional analysis of key technical and economic issues that must be addressed to achieve dynamic, high-value manufacturing in the USA. The focus of this presentation is on discrete manufacturing of production of structural components, a large subset of total manufacturing that produces high-value inputs and finished products for domestic consumption and export. 

Frog-legged robots are commonly used for silicon wafer handling in semiconductor manufacturing. However, their precision, speed and versatility are limited by vibration which varies with their position in the workspace. This paper proposes a methodology for modelling the pose-dependent vibration of a frog-legged robot as a function of its changing inertia, and its experimentally-identified joint stiffness and damping. The model is used to design a feedforward tracking controller for compensating the pose-dependent vibration of the robot. In experiments, the proposed method yields 65–73% reduction in RMS tracking error compared to a baseline controller designed for specific poses of the robot.