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Additive manufacturing (AM) is rapidly becoming a local manufacturing modality in fabricating complex, custom-designed parts, providing an unprecedented form-free flexibility for custom products. However, significant variability in part geometric quality and mechanical strength due to the shortcomings of AM processes has often been reported. Presently, AM generally lacks in situ quality inspection capability, which seriously hampers the realization of its full potential in delivering qualified practical parts. Here, we present a monitoring approach and a periodic structure design for developing test artifacts for in situ real-time monitoring of the material and bonding properties of a part at fiber/bond-scale. While the production method used in current work is filament based, the proposed approach is generic as defects are always due to materials in a bonding zone and their local bonding attributes in any production modality. The artifact design detailed here is based on ultrasonic wave propagation in phononic coupons consisting of repeating substructures to monitor and eventually to assess the bond quality and placement uniformity--not only for geometry but also for defect states. Periodicity in a structure leads to the dispersion of waves, which is sensitive to geometric/materials properties and irregularities. In this proof-of-concept study, an experimental setup and basic artifact designs are described and off-line/real-time monitoring data are presented. As a model problem, the effects of printing speed on the formation of stop bands, wave propagation speeds and fiber placement accuracy in samples are detected and reported. [ABSTRACT FROM AUTHOR]

This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives. [ABSTRACT FROM AUTHOR]