Knowledge-based program generation approach for robotic manufacturing systems

The design of manufacturing systems in hazardous environments is complex, requiring interdisciplinary knowledge to determine which components and operators (human or robotic) are feasible. When conceptualizing designs, some options may be overlooked or unknowingly infeasible due to the design engineers’ lack of knowledge in a particular field or ineffective communication of requirements between disciplines. Computational design tools can help alleviate many of the problems encountered in this design task. We create a knowledge-based system (KBS) utilizing CLIPS to automate the synthesis of conceptual manufacturing system designs in radioactive environments. The KBS takes a high-level functional description of a process and uses FBS modeling to generate multiple designs with generic components retrieved from a database and low-level manufacturing task sequences. Using this approach, many options are explored and operator task compatibility is directly addressed. The KBS is applied to the design of glovebox processing systems at Los Alamos National Laboratory (LANL).

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Architectural Design and Open Innovation Symbiosis: Insights from Research Campuses, Manufacturing Systems, and Innovation Districts

Sustainability ◽

10.3390/su10124495 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4495 ◽

Cited By ~ 18

Author(s):

JinHyo Yun ◽

Xiaofei Zhao ◽

Tan Yigitcanlar ◽

DooSeok Lee ◽

HeungJu Ahn

Keyword(s):

Case Studies ◽

Open Innovation ◽

Architectural Design ◽

Manufacturing Systems ◽

Model Building ◽

Ford Motor Company ◽

Concept Model ◽

Knowledge Based ◽

Innovation Activities ◽

Systems Research

In the age of knowledge-based economies, open innovation has increasing importance. This study aimed to explore the architectural design approaches that can revitalize innovation activities in the era of knowledge-based economies. This paper investigated global case research campuses, manufacturing systems, and innovation districts where architectural design supports innovation activities. This study developed a research framework of architectural design for innovation and applied it in the selected case studies to generate insights. First, the research campuses selected as case studies included Panopticon, DGIST Education and Research Campuses, and Apple Park. Second, the open innovation of manufacturing system architecture was analyzed through the case studies of the Ford Motor Company, Toyota Motor Corporation, and Rolls-Royce Motor Cars. Third, this paper studied the clustered open innovation architectures of Macquarie Park, One North, and Strijp-S Innovation Districts. The findings revealed how tacit knowledge motivates open innovation through the design of manufacturing systems, research campuses, and innovation districts through real examples and mathematical or concept model building.

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A dual scheduling model for optimizing robustness and energy consumption in manufacturing systems

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405415625915 ◽

2016 ◽

Vol 232 (1) ◽

pp. 5-16 ◽

Cited By ~ 3

Author(s):

Joan Escamilla ◽

Miguel A Salido

Keyword(s):

Energy Consumption ◽

Manufacturing Systems ◽

Optimal Solution ◽

Combinatorial Problems ◽

Dual Model ◽

Scheduling Problems ◽

Huge Number ◽

Energy Aware ◽

Knowledge Based ◽

Manufacturing Problems

Manufacturing systems involve a huge number of combinatorial problems that must be optimized in an efficient way. One of these problems is related to task scheduling problems. These problems are NP-hard, so most of the complete techniques are not able to obtain an optimal solution in an efficient way. Furthermore, most of real manufacturing problems are dynamic, so the main objective is not only to obtain an optimized solution in terms of makespan, tardiness, and so on but also to obtain a solution able to absorb minor incidences/disruptions presented in any daily process. Most of these industries are also focused on improving the energy efficiency of their industrial processes. In this article, we propose a knowledge-based model to analyse previous incidences occurred in the machines with the aim of modelling the problem to obtain robust and energy-aware solutions. The resultant model (called dual model) will protect the more dynamic and disrupted tasks by assigning buffer times. These buffers will be used to absorb incidences during execution and to reduce the machine rate to minimize energy consumption. This model is solved by a memetic algorithm which combines a genetic algorithm with a local search to obtain robust and energy-aware solutions able to absorb further disruptions. The proposed dual model has been proven to be efficient in terms of energy consumption, robustness and stability in different and well-known benchmarks.

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