Modular design of mechatronic systems with function modeling

This paper presents a component-based framework for mechatronic systems modeling. The main concept of component-based approach is to create a model for common system's basic components, which can be reused as many times as required. These components can be then successively aggregated in order to have a model that sufficiently represents the whole system. The proposed framework is a hierarchical structure with three different layers: (1) basic components layer, (2) subsystem components layer, and (3) mechatronic system layer. The proposed framework has many advantages include: (I) reducing the development cost and time, (II) improving system maintainability and flexibility (III) enhancing system quality. A practical example is explained in order to interpret the proposed framework.

Automation in Construction, 2019

Modular prefabrication of mechanical, electrical and plumbing (MEP) systems has become more prevalent during the last decade with the growth of the prefabricated construction industry. However, it is currently accomplished only for smaller systems where integrated packaged units are applied in heating, ventilation and air conditioning (HVAC) and other building services installations. The term 'optimum modularity' is rarely accurately used in the industry due to lack of an efficient modularisation method. In this investigation, an automated efficient modularisation algorithm has been developed using an approach which combines fuzzy logic, Dependency Structure Matrix (DSM) and Hierarchical Clustering (HC) methods. The developed algorithm achieves minimum total installation cost by identifying the optimum number modules and module division points based on assembly cost and the handling cost of each module. It is shown that the optimum modularity for a system is highly dependent on the module dimensions and the module division point.

Seventh International Conference on Application of Concurrency to System Design (ACSD 2007), 2007

The software design is one of the most challenging tasks during the design of a mechatronic system. On one hand, it has to provide solutions to deal with concurrency and timeliness issues of the system. On the other hand, it has to glue different disciplines (such as software, control and mechanical) of the system as a whole. In this paper, we propose a model-driven approach to design the software part of a mechatronic system, which consists of two major parts: systematic modeling and correctness-preserving synthesis. The modeling stage is divided into four steps, which focus on different aspects (such as concurrency, multiple disciplines and timeliness) of the system respectively. In particular, we propose a set of handshake patterns to capture the concurrent aspect of the system. These patterns assist designers to build up an adequate top-level model efficiently. Furthermore, they separate the system into a set of concurrent components, each of which can be further refined independently. Subsequently, the multidisciplinary and realtime aspects of the system are naturally specified and analyzed in a series of refinements. After the important aspects of the system are specified and analyzed in a unified model, a software implementation is automatically synthesized from the model, the correctness of which is ensured by construction. The effectiveness of the proposed approach is illustrated by a complex production cell system.

IEEE/ASME Transactions on Mechatronics, 2000

A mechatronic system needs an integrated, concurrent, and system-based design approach due to the existence of interactions among its subsystems, and also the existence of interactions between the criteria involved in a realistic evaluation of a mechatronic product. This paper presents a systematic methodology for a detailed mechatronic design based on a mechatronic design quotient (MDQ). MDQ is a multicriteria index, reflecting a system-based evaluation of a mechatronic design, which is calculated using soft computing techniques, thereby accommodating interactions between criteria and human experience. A niching genetic algorithm is utilized to explore the huge search space raised due to concurrent and integrated design approach, with the aim to find the elite representatives of different possible configurations. To demonstrate the method, it is applied to an industrial fish cutting machine called the Iron Butcher-an electromechanical system that falls into the class of mixed or multidomain systems.

2014

There is long history of developing modelling systems in the fields like mechanics, electronics and control. Modelling a mechatronical system needs a sophisticated approach in modelling methodology especially at early stages of the design process. There are modelling tools in the market for mechatronics systems based on the general physical similarity principles of both mechanical and electrical components. It is well known that most of the later design constraints are designed into a product at the very first stages of the product development process. Therefore the concept design stage is of main interest though with strong links and sights to prospective end product.

This work was partially developed in the course of the Special Research Initiative 614-Self-optimizing Concepts and Structures in Mechanical Engineering-University of Paderborn, and was published on its behalf and funded by the Deutsche Forschungsgemeinschaft. 2 This work was partially developed in the project 'ENTIME: Entwurfstechnik Intelligente Mechatronik' (Design Methods for Intelligent Mechatronic Systems). The project ENTIME is funded by the state of North Rhine-Westphalia (NRW), Germany and the EUROPEAN UNION, European Regional Development Fund, 'Investing in your future'.

2009

Electronics World Wireless World, 2008

The author discusses the challenges of developing mechatronic systems. Most engineers are surprised to learn that the term mechatronics is nearly 40 years old. It was first used in1969 by Tetsuro Mori, an engineer at the Yaskawa Company, to describe a system composed of mechanical and electrical elements that is controlled by an embedded system (Fig. 1). In today's world it is rare to find electromechanical devices without some kind of embedded system. The intelligence from an embedded system delivers enhanced performance, reduced energy consumption, better reliability, and safer operation, which are key differentiators and value drivers for a piece of equipment.