Ant Colony Optimization
Ant Colony Optimization (ACO) is one of the population based meta-heuristic optimization methods for finding approximate solutions to discrete optimization problems. It has been derived from the foraging behavior or stigmergic communication- a form of indirect communication – natural ant colonies. ACO is basically a solution – construction heuristic. The procedure for solution construction is based on mutual interactions among elementary agents, called artificial ants. Any discrete optimization problem can be formulated as comprising of components derived from the domain. A solution to this problem is a certain combination of these components. The presence and absence of a component in a solution can be encoded by using a binary variable; where a value 1 means that the corresponding component is present in the solution and a value of 0 means that the corresponding component is absent. For example, the components of a minimum spanning tree problem are the edges present in the graph. The solution to the minimum spanning tree problem can be formulated as a string of binary variables corresponding to the edges in the graph. A value of 1 represents the corresponding edge being connected and a value of 0 represents the corresponding edge being disconnected.While solving a discrete optimization problem with ACO, the problem is formulated as a construction graph. The construction graph is a completely connected graph, where nodes in the graph represent the problem components and the edges represent the transition between the components. Ants move on the construction graph to generate a solution. They lay chemical substance, called pheromone, on the edges between the nodes of the graph, as they move along. The amount of pheromone deposited on the edges is a function of the quality of the solution that is produced. Ants’s solution construction consists of transitions from node to node in a step-by-step manner. These transitions are determined by a probabilistic selection rule, based on the value of pheromones deposited on the edges between the nodes by other ants. So using the information stored in pheromone intensity, ants traverse a path in the construction graph. This paths is a solution to te discrete optimization problem. Over a period of time, tha path that corresponds to the optimal solurion fortt he optimization problem gets high pheromone deposition. Any ant traversing the construction graph at this point will choose this path. In addition to pheromone intensity, some problem-spesific local heuristic are also used to guide the ants through the construction graph.
ACO has been successfully applied to a large number of combinatorial optimization problems, including travelling salesman problems; vehicle routing problems; and quadratic assignment problem. ACO also has been applied successfully to the scheduling problems, such as single machine problems; flow shop problems; and graph coloring problems.
(Panighrahi, B.K., Computational Intelligence in Power Engineering, Springer, 2010, pg.31-32)
Vertical Lift Storage Modules (VLSM)
These are also called vertical lift automated storage/retrieval system (VL-AS/RS). All of the preceding AS/RS types are designed around a horizontal aisle. The same principle of using a center aisle to access loads is used except that the aisle is vertical. Vertical lift storage modules, some with heights of 10 m (30 ft) or more, are capable of holding large inventories while saving valuable floor space in te factory.
(Groover, M.P. , Automation, Production Systems, and Computer-Integrated Manufacturing, 3rd Edition, pg. 324)
Ductile to Brittle Transition Temperature (DBTT)
The temperature below which a material behaves in a brittle manner in an impact test. The ductile to brittle switchover also depends on the strain rate. (pg214)
limited number of temperatures.
The ductile to brittle transition temperature is the temperature at which a material changes from ductile to brittle fracture. This temperature may be defined by the average energy between the ductile and brittle regions, at some specific absorbed energy, or by some characteristic fracture appearance. A material subjected to an impact blow during service should have a transition temperature below the temperature of the material’s surroundings.
Not all materials have a distinct transition temperature (figure 6-22). BCC metals have transition temperatures, but most FCC metals do not. FCC metals have high absorbed energies, with the energy decreasing gradually and, sometimes, even increasing as the temperature decreases. The effect of this transition may have contributed to the failure of the Titanic.
Bright Dipping
Bright dipping is used to produce highly reflective surfaces on non-ferrous metals, particularly copper and brass. To bright up copper, it is first dipped in sulphuric acid which acts to oxidize the surface. It is followed by a dip in nitric acid to dissolve the newly formed oxide on copper. By this process, the microscopic hills and valleys on the metal surface are leveled off, increasing the reflectivity of the metal. Bright dip is followed by thorough rinsing and wiped dry to avoid tarnishing. Proprietary solutions are available for various non-ferrous metals. Bright dipping is the most effective process of removing oxides from non-ferrous metals and gives a highly reflective surface which is necessary to have before plating the surfaces. A very thin layer of plating on a bright dipped surface will make the surface more attractive.
(Kaushish, J.P., Manufacturing Process,Eastern Economy Edition, pg.543)
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