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Time to market, 3D printing -- Research, Sintering -- Research, Product development -- Research, Metal products -- Research, and EOS GmbH

Additive Manufacturing (AM) brought new design possibilities and freedom to the product design process. However, as the existing design rules and design limitations do not apply to the AM, designers require new methods and tools to utilise the full potential of AM. The stated is especially noticeable in the design of metal products made with AM, particularly with the Direct Metal Laser Sintering (DMLS) process. The research proposes a methodological approach for analysing the existing DMLS products made with DMLS to extract design knowledge. The methodology is applied to the pool of DMLS from which 15 design principles are extracted that formalise design knowledge about DMLS. The design principles are intended to be used in the early design stages of the design process as a source of design knowledge and inspiration for conceptualising new and innovative products that will be made using DMLS. Keywords: AM models; design for additive manufacturing; design principles; direct metal laser sintering; metal AM
1 INTRODUCTION In recent years Additive Manufacturing (AM) gained popularity in both academia and industry as it evolved from rapid prototyping technology to everyday manufacturing technology used for the production [...]

Data warehousing/data mining, Algorithm, Aquatic resources -- Taiwan -- India, Droughts -- India -- Taiwan, Wheat industry -- Methods, Machine learning -- Methods, Data mining -- Methods, and Algorithms -- Methods

The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.