scholarly journals Integration of value and sustainability assessment in design space exploration by machine learning: an aerospace application

2020 ◽  
Vol 6 ◽  
Author(s):  
Alessandro Bertoni ◽  
Sophie I. Hallstedt ◽  
Siva Krishna Dasari ◽  
Petter Andersson
Keyword(s):  
Machine Learning ◽  
Product Development ◽  
Design Space Exploration ◽  
Design Space ◽  
Sustainability Assessment ◽  
Space Exploration ◽  
Future Design ◽  
Research Fields ◽  
Sustainable Product Development ◽  
Sustainable Product

The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

scholarly journals Benefits and obstacles of sustainable product development methods: a case study in the field of urban mobility

2017 ◽  
Vol 3 ◽  
Author(s):  
Rainer Stark ◽  
Tom Buchert ◽  
Sabrina Neugebauer ◽  
Jérémy Bonvoisin ◽  
Matthias Finkbeiner
Keyword(s):  
Product Development ◽  
Sustainability Assessment ◽  
Development Project ◽  
Passenger Cars ◽  
Design Engineers ◽  
Sustainable Products ◽  
Design For Sustainability ◽  
Sustainable Product Development ◽  
Sustainable Product ◽  
Mobility Solution

In the last few years, numerous approaches have been introduced for supporting design engineers in developing more sustainable products. However, so far, these efforts have not led to the establishment of a commonly acknowledged standard methodology for Sustainable Product Development (SPD). This brings into question the relevance of developing new methods and calls for more efforts in testing the available ones. This article provides a reflection about the benefits and obstacles of applying existing SPD approaches to a real product development project. It reports the results of a project aimed at developing a new mobility solution under the constraints of sustainability-related targets. This project has led to the development of a new pedelec concept, focusing on the substitution of small passenger cars with the help of three SPD methods – Design for Sustainability Guidelines, Product Sustainability Index, and Life Cycle Sustainability Assessment. These methods have proved to be generally beneficial, thanks to a combination of qualitative and quantitative perspectives. However, the multitude of criteria offered by the methods put forth difficulties in evaluating which sustainability aspects are relevant and therefore lead to higher effort for information retrieval analysis and decision processes. Furthermore, the methods still lack an integrated perspective on the product, the corresponding services and the overarching system.

scholarly journals CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2200
Author(s):  
Alireza Ghaffari ◽  
Yvon Savaria
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Automatic Synthesis ◽  
Software Libraries ◽  
Learning Software ◽  
High Level

Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms.

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