Machine vision and planning applied on assembby line of multiple products

This article describes the use of Artificial Intelligence (IA) techniques applied in cells of a manufacturing system. Machine Vision was used to identify pieces and their positions of two different products to be assembled in the same productive line. This information is given as input for an IA planner embedded in the manufacturing system. Therefore, initial and final states are sent automatically to the planner capable to generate assembly plans for a robotic cell, in real time.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Risk/investment-driven transmission expansion planning with multiple scenarios

This study presents a new methodology based on risk/investment to solve transmission network expansion planning (TNEP) problem with multiple future scenarios. Three mathematical models related to TNEP problems considering multiple future generation and load scenarios are also presented. These models will provide planners with a meaningful risk assessment that enable them to determine the necessary funding for transmission lines at a permissible risk level. The results using test and real systems show that the proposed method presents better solutions compared with scenario analysis method. ©The Institution of Engineering and Technology 2013.

Transmission system expansion planning by an extended genetic algorithm

The paper presents an extended genetic algorithm for solving the optimal transmission network expansion planning problem. Two main improvements have been introduced in the genetic algorithm: (a) initial population obtained by conventional optimisation based methods; (b) mutation approach inspired in the simulated annealing technique, the proposed method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Excellent performance is reported in the test results section of the paper for a difficult large-scale real-life problem: a substantial reduction in investment costs has been obtained with regard to previous solutions obtained via conventional optimisation methods and simulated annealing algorithms; statistical comparison procedures have been employed in benchmarking different versions of the genetic algorithm and simulated annealing methods.

Multistage and coordinated planning of the expansion of transmission systems

In this paper, an efficient genetic algorithm (GA) is presented to solve the problem of multistage and coordinated transmission expansion planning. This is a mixed integer nonlinear programming problem, difficult for systems of medium and large size and high complexity. The GA presented has a set of specialized genetic operators and an efficient form of generation of the initial population that finds high quality suboptimal topologies for large size and high complexity systems. In these systems, multistage and coordinated planning present a lower investment than static planning. Tests results are shown in one medium complexity system and one large size high complexity system.

Efficient method for AC transmission network expansion planning

A combinatorial mathematical model in tandem with a metaheuristic technique for solving transmission network expansion planning (TNEP) using an AC model associated with reactive power planning (RPP) is presented in this paper. AC-TNEP is handled through a prior DC model while additional lines as well as VAr-plants are used as reinforcements to cope with real network requirements. The solution of the reinforcement stage can be obtained by assuming all reactive demands are supplied locally to achieve a solution for AC-TNEP and by neglecting the local reactive sources, a reactive power planning (RPP) will be managed to find the minimum required reactive power sources. Binary GA as well as a real genetic algorithm (RCA) are employed as metaheuristic optimization techniques for solving this combinatorial TNEP as well as the RPP problem. High quality results related with lower investment costs through case studies on test systems show the usefulness of the proposal when working directly with the AC model in transmission network expansion planning, instead of relaxed models. (C) 2010 Elsevier B.V. All rights reserved.

A Bilevel Approach to Transmission Expansion Planning Within a Market Environment

We present a bilevel model for transmission expansion planning within a market environment, where producers and consumers trade freely electric energy through a pool. The target of the transmission planner, modeled through the upper-level problem, is to minimize network investment cost while facilitating energy trading. This upper-level problem is constrained by a collection of lower-level market clearing problems representing pool trading, and whose individual objective functions correspond to social welfare. Using the duality theory the proposed bilevel model is recast as a mixed-integer linear programming problem, which is solvable using branch-and-cut solvers. Detailed results from an illustrative example and a case study are presented and discussed. Finally, some relevant conclusions are drawn.

Primary power distribution systems planning taking into account reliability, operation and expansion costs

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Effect of Coating Patterns with Spinel-Based Investment on the Castability and Porosity of Titanium Cast into Three Phosphate-Bonded Investments

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Branch and bound algorithm for transmission system expansion planning using a transportation model

A method for optimal transmission network expansion planning is presented. The transmission network is modelled as a transportation network. The problem is solved using hierarchical Benders decomposition in which the problem is decomposed into master and slave subproblems. The master subproblem models the investment decisions and is solved using a branch-and-bound algorithm. The slave subproblem models the network operation and is solved using a specialised linear program. Several alternative implementations of the branch-and-bound algorithm have been rested. Special characteristics of the transmission expansion problem have been taken into consideration in these implementations. The methods have been tested on various test systems available in the literature.

The effect of mold temperature on castability of CPTi and Ti-6Al-4V castings into phosphate bonded investment materials

Objective. The aim of this study was to evaluate the castability of CP titanium and Ti-6Al-4V alloy castings into Rematitan Plus investment at three different mold temperatures.Methods. A nylon mesh pattern (20 mm with 64 squares and wire of 0.7 mm in diameter) was used for the castability testing. Initially, an image of the wax pattern was obtained by means of a digital camera and the total extension of filaments (mm) was then measured, using the Leica Qwin image analysis system. The mesh sprued was placed in the Rematitan Plus investment material and the castings were made in a Discovery Plasma machine at three different mold temperatures: 430 degrees C (control group), 480 degrees C or 530'C. Ten castings were made for each temperature. The images of the castings were analyzed (Leica Qwin) and the castability index determined by the number of the completely cast segments as a percentage of the wax pattern. Data were analyzed by two-way ANOVA and Tukey's multiple comparison test (a = 0.05) using materials and temperatures as discriminating variables.Results. The Ti-6Al-4V alloy (60.86%) presented a better castability index than CP Ti (48.44%) (p < 0.000001). For CP Ti, the temperature of 530 degrees C (23.96%) presented better castability than at other temperatures, 480 degrees C (14.66%) and 430 degrees C (12.54%), with no difference between them (p < 0.001). For Ti-6Al-4V alloy, there was a statistically significant difference among the three temperatures: 530 degrees C (28.36%) > 480 degrees C (19.66%) > 430 degrees C (15.97%) (p < 0.002).Significance. Within the limitations of this study, the increase in the mold temperature of the Rematitan Plus investment resulted in a better castability index for both materials, and Ti-6Al-4V presented a better castability index than CP Ti. (c) 2005 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

A heuristic method for reactive power planning

An approach for solving reactive power planning problems is presented, which is based on binary search techniques and the use of a special heuristic to obtain a discrete solution. Two versions were developed, one to run on conventional (sequential) computers and the other to run on a distributed memory (hypercube) machine. This latter parallel processing version employs an asynchronous programming model. Once the set of candidate buses has been defined, the program gives the location and size of the reactive sources needed(if any) in keeping with operating and security constraints.

Relax and fix heuristics to solve one-stage one-machine lot-scheduling models for small-scale soft drink plants

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Influence of the final temperature of investment healting on the tensile strength and Vickers hardness of CP TI and TI-6AL-4V alloy

The aim of the work was to evaluate the influence of the temperature of investment healting on the tensile strength and Vickers hardness of CP Ti and Ti-6Al-4V alloy casting. Were obtained for the tensile strength test dumbbell rods that were invested in the Rematitan Plus investment and casting in the Discovery machine cast. Thirty specimens were obtained, fiftten to the CP Titanium and fifteen to the Ti-6Al-4V alloy, five samples to each an of the three temperatures of investment: 430°C (control group), 480°C and 530°C. The tensile test was measured by means of a universal testing machine, MTS model 810, at a strain of 1.0 mm/min. After the tensile strenght test the specimens were secctioned, embedded and polished to hardness measurements, using a Vickers tester, Micromet 2100. The means values to tensile tests to the temperatures 430°C, 480 and 530: CP Ti (486.1 - 501.16 - 498.14 -mean 495.30 MPa) and Ti-6Al-4V alloy (961.33 - 958.26 - 1005.80 - mean 975.13 MPa) while for the Vickers hardness the values were (198.06, 197.85, 202.58 - mean 199.50) and (352.95, 339.36, 344.76 - mean 345.69), respectively. The values were submitted to Analysis of Variance (ANOVA) and Tukey' s Test that indicate differences significant only between the materials, but not between the temperature, for both the materias. It was conclued that increase of the temperature of investment its not chance the tensile strength and the Vickers hardness of the CP Titanium and Ti-6Al-4V alloy.

Evolutionary multi-move path-relinking for transmission network expansion planning

This paper presents the application of a new metaheuristic algorithm to solve the transmission expansion planning problem. A simple heuristic, using a relaxed network model associated with cost perturbation, is applied to generate a set of high quality initial solutions with different topologies. The population is evolved using a multi-move path-relinking with the objective of finding minimum investment cost for the transmission expansion planning problem employing the DC representation. The algorithm is tested on the southern Brazilian system, obtaining the optimal solution for the system with better performance than similar metaheuristics algorithms applied to the same problem. ©2010 IEEE.