Assisting Decisions in Inventive Design of Complex Engineering Systems

Cavallucci, Denis; Fuhlhaber, Simon; Riwan, Alain

doi:10.1016/j.proeng.2015.12.409

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Inventive minds and engineering exceed exponentially this unpredicting creative thinking. (Godfrey 1998, Obermeyer 2017, Arciszewski 2018, Cavallucci 2015 In our case the agriculture machinery has initially very distinct specifications summarizing in: too heavy, to bulky, too late, too energy intensive, too much effort for a tiny result. In nature the harvest is simple, in time, no resources or energy is wasted.…”

Section: Introductionmentioning

confidence: 86%

The Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming

Serdaris¹,

Spinthiropoulos²,

Agrafiotis³

et al. 2018

JAS

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The poor Data Science support of agriculture brought us to our main idea of the research is to analyze all micro-works for every plant or tree. Then we proceed to specify targeted actions for harvest collection, micro spraying and hundreds similar simple actions. Initially we collect data from the farm. The airborne, land and underwater unmanned vehicles scan the field area with customized various sensors and cameras in various multi spectral modes. The result is minimum agro-chunk Four-Dimensional model. The unmanned vehicle on the field area receives target data. It is equipped with a general-purpose robotic arm, an absorbing bellow, a robotic pruner, a liquid spraying pipe, an underwater robotic arm and hundreds of others. It moves there and performs the commanded action. Action is flower or nuts collection, insect suction pruning and hundred more. All operations are high trainable by human intervention and the system stores its approach and logic for future action correction.

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Section: Introductionmentioning

confidence: 86%

The Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming

Serdaris¹,

Spinthiropoulos²,

Agrafiotis³

et al. 2018

JAS

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“…IDM Methodology [7] has further deepened and automated the treatment of problem networks. The problem analysis with IDM creates a problem graph.…”

Section: Further Research For Triz-based Problem Analysismentioning

confidence: 99%

Research of the Possibilities for Using and Linking TRIZ Methods with Systems Engineering

Bielefeld

Sizikov²,

Schlüter

2019

IFIP Advances in Information and Communication Technology

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Increasing complexity and global competition are forcing product development to develop faster products that are marketable and innovative. Thereby, key issues are increasing product functionality and user-friendliness. The significant engineering contradiction is: How can user-friendliness of the products be optimized for the customer without adversely affecting the functionality of the product? This requires a systematic consideration of the interactions between the customer and the product as well as the effects and interactions of the functions within a product. TRIZ provides a variety of methods to analyze and solve problems, incl. resolving of engineering contradictions. In addition, an interdisciplinary and transparent model can be created with Model-Based Systems Engineering (MBSE) to improve product understanding. The scope of this research is to combine the potential of MBSE with TRIZ in order to generate an optimal approach regarding implementation and networking from TRIZ function models in to a MBSE overall model. Based on the MBSE overall model both usability and system functionality (especially harmfull function) should be considered.

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“…Due to the complex and multidisciplinary nature of the manufacturing industry, how to adopt technological decisions properly in industrial engineering design is a critical concern to manufacturers [1]. In this context, the Theory of Inventive Problem Solving (TRIZ) is a framework that has been pervasively used for problem solving in the industry.…”

Section: Introductionmentioning

confidence: 99%

A Rule-Based Heuristic Methodology for Su-Field Analysis in Industrial Engineering Design

et al. 2022

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Industrial engineering design is a crucial issue in manufacturing. To meet the competitive global market, manufacturers are continuously seeking solutions to design industrial products and systems inventively. Su-Field analysis, which is one of the TRIZ analysis tools for inventive design problems, has been used to effectively improve the performance of industrial systems. However, the inventive standards used for engineering design are summarized and classified according to a large number of patents in different fields. They are built on a highly abstract basis and are independent of specific application fields, making their use require much more technical knowledge than other TRIZ tools. To facilitate the use of invention standards, in particular to capture the uncertainty or imprecision described in the standards, this paper proposes a rule-based heuristic approach. First, Su-Field analysis ontology and fuzzy analysis ontology are constructed to represent precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) reasoning and fuzzy reasoning are executed to generate heuristic conceptual solutions. Finally, we develop a software prototype and elaborate the resolution of “Auguste Piccard’s Stratostat ” in the prototype.

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Assisting Decisions in Inventive Design of Complex Engineering Systems

Cited by 7 publications

References 19 publications

The Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming

The Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming

Research of the Possibilities for Using and Linking TRIZ Methods with Systems Engineering

A Rule-Based Heuristic Methodology for Su-Field Analysis in Industrial Engineering Design

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