Measuring functional coupling in complex systems is an important task for good design practice, though historically it has been an art of subjective judgement. With the recent advancements in Deep Learning and Natural Language Processing, functional requirements (FRs) and design parameters (DPs), which are expressed as words and sentences, can be represented in a vector space. The sentence embedding model, BERT, was used in this paper to vectorize FRs and DPs, to calculate functional independence and to study how metrics for functional coupling measurement can be enhanced. It was found that semantic similarity among FRs and DPs, represented in vector space, could be used to compute quantitative values for metrics of functional independence. It was also found that design cases where coupling was unambiguous yielded the best results, while cases where laws of physics needed to define the FR-DP relationship did not transliterate well to the natural language used to express the FR-DP highlighted the limitations of the model in its current state. This study, however, demonstrates a great opportunity to develop a robust, fine-tuned design language representation model for accurately measuring functional independence as a part of our effort to enhance design intelligence.

The task of design feature transcription, or encoding the functional requirements and design parameters of a design, requires representing design data such that a machine can comprehend. Natural language processing, powered by deep neural networks trained on massive corpora of textual data, can map language into distributed vector representation space that machines can understand and retrieve. This work outlines how language models can be used to enhance early-stage design by separating the functional and physical domains, abstracting key functional requirements, and analysing systems to provide metrics for good design decision making, to facilitate a framework for hybrid intelligence.

Vibration energy harvesters based on the resonance of the beam structure work effectively only when the operating frequency window of the beam resonance matches with the available vibration source. None of the resonating MEMS structures can operate with low frequency, low amplitude, and unpredictable ambient vibrations since the resonant frequency goes up very high as the structure gets smaller. Bistable buckled beamenergy harvester is therefore developed for lowering the operating frequency window below 100Hz for the first time at the MEMS scale.This design does not rely on the resonance of the MEMS structure but operates with the large snapping motion of the beam at very low frequencies when input energy overcomes an energy threshold. A fully functional piezoelectric MEMS energy harvester is designed, monolithically fabricated, and tested. An electromechanical lumped parameter model is developed to analyze the nonlinear dynamics and to guide the design of the nonlinear oscillator based energy harvester.Multilayer beamstructurewith residual stress induced buckling is achieved through the progressive residual stress control of the deposition processes along the fabrication steps. Surface profile of the released device shows bistable buckling of 200μm which matches well with the amount of buckling designed. Dynamic testing demonstrates the energy harvester operates with 50% bandwidth under 70Hz at 0.5g input, operating conditions that have not been demonstrated byMEMS vibration energy harvesters before.

Engineering faces many wicked problems: irreducibly interdisciplinary with multiple competing objectives, and of such large scale and complexity that will require processes to deeply rely on human insights and power of computation. The resurgence of machine learning offers the possibility for new forms of human/computer collaboration where each fuels hybrid intelligence in complementary ways. A concept of virtual design assistant (VDA) is developed as a platform to bring the hybrid intelligence in solving complex design challenges. A deep learning-based abstraction process is developed to provide VDA a function to extract structured functional requirements from fragmental design specifications and customer needs.