a review - Acta Technica Corviniensis

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Table 2: Ranges of variables selected for optimization Variable Lower Bound Upper Bound µ 0.015 0.03 R p (mm) 16,5 27,5 R D (mm) 3.5 5.5 P BH (MPa) 30 50 K fm (MPa) 120 148 c 2 2 Table 3: Parameters for Genetic Algorithm Population type Double vector Population size 500 Selection Roulette Mutation Use constraint dependent default Crossover Scatteret Reproduction, crossover fraction 0.8 Elitism 5 RESULTS AND DISCUSSION The strain-hardening exponent, strain-hardening coefficient and plastic strain ratio were obtained using a tensile test to construct the stress curve versus true strain. The plastic strain ratio (r) is obtained with the same test. The control method development in this work used genetic algorithm and finite element methods in order to develop an intellectualized control technique on the deep drawing made of cylindrical cup made from 3003 ASTM aluminum. The ranges of variables and parameters for Genetic Algorithm are selected in consultation with company professionals. A maximum drawing load of 500 KN requires the geometry of the workpiece in the workshop. The optimization process was effected and the proposed values for a drawing load is 163.19 KN, µ=0.015, Kfm = 120.004 MPa, DR = 371.002 mm, BHF = 33 MPa y Rp = 27.5 mm. The appropriate capacity press can be selected by knowing the drawing load. Working with the presses of higher capacities may lead to many types of defects such as cracks and tearing. To verify the proposed solution a new simulation was carried. At this time RP = 23-24 (mm) and Kfm = 142- 148 (MPa). The optimization process was effected and the proposed values for a drawing load is at this moment 195.43 KN, µ = 0.015, Kfm = 142.003 MPa, DR = 371.002 mm, BHF = 33 MPa y Rp = 24 mm. Optimization techniques provide a systematic method for determining the process parameters to achieve a specific objective. The present results show that the intelligent control in deep drawing of sheet metal can be successfully used in the field of optimization of parameters. Maximum drawing load and blank holder pressure are optimized which enables selection of proper capacity press. The other process parameters are also optimized using genetic algorithm and finite element methods. Punch load have a significant contribution on the product quality. Appropriate punch load and blank holder force evolved through a process results in controlling the thickness variations in a deep drawn part and thus the quality of the part. Therefore, the punch load criteria are chosen to objective function of 3003 ASTM aluminum in this study. CONCLUSIONS A method for the control of parameters in sheet metal using genetic algorithm and finite element methods has been presented and applied in the case 50 ACTA TECHNICA CORVINIENSIS – Bulletin of Engineering of cylindrical deep drawing. This method is based on the punch load as objective function criteria. The most important three process parameters were taken into consideration are the blank holder force, the die profile radius and the punch profile radius. The objective function to be minimized was a selection of the appropriate capacity of the press machine. Working with the presses of higher capacities may lead to many types of defects such as cracks and tearing. The main results are as follows: 1. The prediction scheme of the control of parameters in sheet metal was established based on genetic algorithm 2. The genetic algorithm prediction model was established, and the genetic algorithm structure and genetic algorithm parameters were designed. 3. A maximum drawing load of 500 KN requires the geometry of the workpiece in the workshop. The optimization process was effected and the proposed values for a drawing load is 163.19 KN. 4. There is 32.6 % reduction in the forming load. 5. Maximum drawing load is optimized which enables selection of proper capacity press. The other process parameters and geometry parameters are also take in to account. REFERENCES [1.] Aleksandrović, S. and Stefanović, M., "Significance and Limitations of Variable Blank Holding Force Application in Deep Drawing Proces," Tribology in industry, 27, pp. 48-54, 2005. [2.] Ayari, F., et al., "Parametric Finite Element Analysis of square cup deep drawing," Archives of Computational Materials Science and Surface, 1, pp. 106-111, 2009. [3.] Singh, D., et al., "Identification of Optimum Parameters of Deep Drawing of a Cylindrical Workpiece using Neural Network and Genetic Algorithm," World Academy of Science, Engineering and Technology, 78, 2011. [4.] Wifi, A. and Mosallam, A., "Some aspects of blankholder force schemes in deep drawing process," Journal of Achievements in Materials and Manufacturing Engineering, 24, 315-323, 2007. [5.] Monica, I., "Experimental Investigations and Numerical Simulation of the Semispherical Punch Process," Annals of Oradea University. Fascicle of Management and Technological Engineering, vol. XI, 2012. [6.] Reddy, P. V. R. R., et al., "Parametric Studies on Wrinkling and Fracture Limits in Deep Drawing of Cylindrical Cup " International Journal of Emerging Technology and Advanced Engineering vol. 2, 2012. [7.] Wifi, A. S., et al., "A Review of the Optimization Techniques Applied to the Deep Drawing Process " in 37th International Conference on Computers and Industrial Engineering Alexandria, Egypt, 2007. Year. [8.] Hsiao-Chu, T., et al., "Experimental Optimization of Deep Drawing Using Response Surface Methodology," Applied Mechanics and Materials, 121-126, pp. 1495- 1499, 2012. [9.] Schaeffer, L., Ed., Conformação de Chapas Metálicas. Porto Alegre. Rio Grande do Sul. Brazil: Imprensa Livre Editora Ldta, 2004, pp. Pages. 2004 [10.] Wagner, G., "Optimization potentials at the different levels of planning discrete technological processes," in 4th International Doctoral Students Workshop on Logistics, Otto von Guericke University. Magdeburg, 2011, pp. 109-112. Year 2013. Fascicule 2 [April–June]
1. M. B. I. REAZ ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ADVANCED SMART HOME IMPLEMENTATION 1. DEPARTMENT OF ELECTRICAL, ELECTRONIC AND SYSTEMS ENGINEERING, UNIVERSITI KEBANGSAAN MALAYSIA 43600, UKM, BANGI, SELANGOR, MALAYSIA ABSTRACT: Smart-home concept has been around for many years and played a very important part in the design and implementation of future houses. Early research focus on the control of home appliances but current trends are moving into a creation of self-thinking home. In the recent years many research projects were performed utilizing artificial intelligence tools and techniques. This article highlights research projects employing multi-agent system, action prediction, artificial neural network, fuzzy logic and reinforcement learning. It is found that the combination of tools and techniques are crucial for successful implementation. This article provides platform for future relative studies between different algorithms, architectures and serves as a reference point for developing more cutting edge smart home technologies. KEYWORDS: Smart home, multi-agent system, action prediction, artificial neural network, fuzzy logic, reinforcement learning INTRODUCTION In the early years, smart home is equipped with special structured wiring to enable occupants to remotely control or program an array of automated home electronic devices by entering a single command. This provide convenient interface for homeowner to remotely switch appliances on or off, arm a home security system, control temperature gauges, control lighting, program an entertainment system or home theater, and carry out other related tasks [1]. The current research projects focus more on the creation of intelligent home, a home that is able to control and make decision on its own, which follow the guidelines set by occupants directly and indirectly. Due to this trend, the artificial intelligence (AI) is greatly used in the recent smarthome research. AI is the ability of a computer to perform tasks such as reasoning and learning that human intelligence is capable of doing. In this article we will give a detailed illustration of how AI techniques are being used in the latest smart home projects. MULTIAGENT SYSTEMS FOR SMART HOME A multiagent system (MAS) is a system composed of several agents, capable of speedy mutual interaction between them [2, 3]. This can be message passing or generating changes in their common environment. The agents can be autonomous entities e.g. robots or software agents. MAS can consist of human agents as well. MAS allows the sub problems of a constraint satisfaction problem to be subcontracted to different problem solving agents with their own interests and goals. Each agent in MAS has a set of goals and capabilities. The agent also consists of an intelligent and reasoning unit. The responsibility of this particular unit is to implement a plan on how to use these capabilities in order to achieve the given goals. A multiagent architecture was developed by Jae Chul Moon and Soon in 2000, for intelligent home network services, which organize heterogeneous and distributed home devices effectively [4]. The architecture consists of several agent spaces that are interconnected to each other with very powerful and flexible model known as Tuple model, was proposed by Carriero and Gelertner in 1980’s, for supporting extremely dynamic communication and coordination patterns [5]. An agent autonomously handles a specific home device, a room-net or several other agents using the agent spaces and the space interconnect, in which status of all devices can be transparently accessed despite of the location of the agent. Due to the location transparency, an agent or a newly attached home device can easily get the information of another agent by taking or reading the information from the agent space as shown in Figure 1. The multiagent architecture of Jae and Soon is very novel since it provides flexible communication between the agents with asynchronous stream channel of IEEE1394 using event-driven middleware. It supports data transfer rates of up to 400Mbps and 800Mbps in 1394a and 1394b respectively. The only limitation is that the architecture does not support mobility of agent, which is actually the limitation of the Tuple model. In 2002, Cheng-Fa Tsai and Hang-Chang Wu (2002) came out with another multiagent architecture for home network services named as MASSIHN [6]. The architecture uses Java as its programming language © copyright FACULTY of ENGINEERING ‐ HUNEDOARA, ROMANIA 51
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