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This study proposes an effective thermal control for plastic injection molding (polymer: Santoprene 8211-45 with density of790kg/m3, injection pressure: 1400psi (9,652,660 Pa)) in a laminated die. For this purpose, a comprehensive control strategy is provided to cover various themes. First, a new method for determining the optimal sensor locations as a prerequisite step for modeling and controller design is introduced. Second, system identification through offline and online training with finite element analysis and neural network techniques are used to develop an accurate model by incorporating uncertain dynamics of the laminated die. Third, an additive feedforward control by adding direct adaptive inverse control to self-adaptive PID is developed for temperature control of cavity wall (cavity size: 52.9 x 32.07 x 16.03 mm). A verification of designed controller's performance demonstrates that the proposed strategy provides accurate online temperature tracking and faster response under thermal dynamics with various cycle-times in the injection mold process. [ABSTRACT FROM AUTHOR]

Controlled Environment Agriculture holds promise as a way to intensify current agricultural production systems while limiting pressures on land, water, and energy resources. However, its use has not yet been widely adopted, partly because the engineering design considerations and associated challenges are not well known. This is even more apparent for aeroponics, where the additional cost and complexities in controlling atomisation have yet to establish an advantage in scale over simpler hydroponic systems. To shed light on these considerations and challenges, an instrumented aeroponic system was prototyped to create a quantitative growth model for various species of leafy greens. As the first consideration, pressure swirl atomisers were paired with a diaphragm-type pressure tank to supply the necessary pressures needed for effective atomisation. Secondly, the nutrient solution was mixed on demand from Reverse Osmosis water, and the concentrated nutrient stock was then pumped into the pressure tank using a positive displacement pump. A bamboo-based substrate that allowed germination and extended vegetative growth was supported on a stainless-steel mesh and PVC frame acting as a grow tray. Finally, a camera microservice platform was developed using a computer vision pixel-based segmentation method to quantify plant growth. [ABSTRACT FROM AUTHOR]