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Additive manufacturing systems are being deployed on a cloud platform to provide networked manufacturing services. This article explores the value of interconnected printing systems that share process data on the cloud in improving quality control. We employed an example of quality learning for cloud printers by understanding how printing conditions impact printing errors. Traditionally, extensive experiments are necessary to collect data and estimate the relationship between printing conditions vs. quality. This research establishes a multi-printer co-learning methodology to obtain the relationship between the printing conditions and quality using limited data from each printer. Based on multiple interconnected extrusion-based printing systems, the methodology is demonstrated by learning the printing line variations and resultant infill defects induced by extruder kinematics. The method leverages the common covariance structures among printers for the co-learning of kinematics-quality models. This article further proposes a sampling-refined hybrid metaheuristic to reduce the search space for solutions. The results showed significant improvements in quality prediction by leveraging data from data-limited printers, an advantage over traditional transfer learning that transfers knowledge from a data-rich source to a data-limited target. The research establishes algorithms to support quality control for reconfigurable additive manufacturing systems on the cloud. 

Molecular dynamics simulation of a thermoset network and the glass transition by heating and cooling.

 

Triboluminescence (TL) is a phenomenon of light emission induced by impact, stress, fracture, or an applied mechanical force. This phenomenon can be used to detect, evaluate, and predict mechanical failures in composites. In this report, we utilized manganese-doped zinc-sulphide (ZnS: Mn) and Polystyrene (PS) composite to fabricate a TL functional part via additive manufacturing. The morphology of the particles inside the polymer matrix were studied using scanning electron microscopy and micro CT scan. Thermoanalytical techniques such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were carried out to evaluate the thermal transitions and degradation of the composites. The mechanoluminescence performance of the printed samples is evaluated by three-point flexural test and observed to depend on processing conditions that can be utilized to achieve a strong light signal at different mechanical loads. The polymer composite fabrication and processing reduced particle size, enhanced particle dispersion, and altered the mechanical properties of the polymer to help increase the mechanoluminescence response up to 10 times in the 3D printed parts. The unique mechanoluminescence properties of 3D printed luminescent composite have great potential for structural monitoring applications. 

The demand for additively manufactured polymer composites with increased specific properties and functional microstructure has drastically increased over the past decade. The ability to manufacture complex designs that can maximize strength while reducing weight in an automated fashion has made 3D-printed composites a popular research target in the field of engineering. However, a significant amount of understanding and basic research is still necessary to decode the fundamental process mechanisms of combining enhanced functionality and additively manufactured composites. In this review, external field-assisted additive manufacturing techniques for polymer composites are discussed with respect to (1) self-assembly into complex microstructures, (2) control of fiber orientation for improved interlayer mechanical properties, and (3) incorporation of multi-functionalities such as electrical conductivity, self-healing, sensing, and other functional capabilities. A comparison between reinforcement shapes and the type of external field used to achieve mechanical property improvements in printed composites is addressed. Research has shown the use of such materials in the production of parts exhibiting high strength-to-weight ratio for use in aerospace and automotive fields, sensors for monitoring stress and conducting electricity, and the production of flexible batteries.