The avoidance of obstacles placed in the workspace of the robot is a problem which makes controlling them more difficult. The known avoidance methods used for the robots control are based on bypass trajectory programming or on using the sensors that detect the position of the obstacle. This paper describes a method of training industrial robots in order for them to avoid certain obstacles in the workspace. The method is based on the modelling of the robot's kinematics by means of an artificial neural network and by including the neural model in the robot's controller. The neural model simulates the robot's inverse kinematics, and provides the joint coordinates, as referential values for the controller. The novelty of the method consists in the deliberately erroneous training of the network, so that, when programming a direct trajectory in the workspace, the robot avoids a known obstacle.
Wood is used as a raw material in various industries, including the production of furniture, which puts pressure on the exploitation of the forests and the continuous reduction of their surfaces, with undesirable effects on the environment. The paper provides a way of sustainably manufacturing furniture by replacing wood with composite materials based on natural fibers obtained from fast-growing renewable crops (hemp, willow, flax, etc.) and at the same time a method of assessing the forest areas which can be saved from cutting. The method’s algorithm is based on the estimation of forest area that ensures the annual consumption of wood for the production of furniture, both in the conventional production of furniture and in the unconventional one, where part of the products is made of composites. The agricultural areas required to be cultivated with technical plants to provide the natural fibers necessary for the wood replacement composite were also determined. The case study, based on the data of an upholstered furniture company, shows that replacing only part of the wood for the production of furniture can save about 3000 hectares of beech forests per year and the necessary plant fibers can be obtained from a surface area about 10 to 100 times smaller.
Abstract:The growing number of research dedicated to open innovation has determined a growth of company's interest towards external sources of knowledge and of the interactions with these resources in order to obtain an added value. Absorptive capacity, together with the outside-in dimension of open innovation represent interconnected concepts. The present study examines their connection both in theory and in their representation in SMEs managers' perception. The study that forms the base of the research is a survey conducted on companies' managers who worked on open innovation projects. Although few studies dealing with the subject existed up until now, the vision of the managers was chosen to be examined due to the impact of a managerial decision over a company's ability to learn from an exterior environment. In the future, open innovation is thought to be integrated in the management of innovation, this being the reason we view it as of utmost importance for the deepening of the managerial perspective regarding its influences and component elements.
The performance increasing in different stages of the value chain represents an important domain of study for both academic researchers and business experts. In the light of globalization, this topic enjoys a major interest from experts in various fields of activity and from the company's management, as well. From the research perspectives, it is desired to analyze the elements that determine and influence the oil companies' performances. For this scope, we have made an analysis of the value chain stages, and we have identified the relevant performance indicators for each working stage. By questioning some key people, managers, and experts situated on different stages of the value chain in a petroleum company, important information and data were obtained regarding their perception of the importance of the chain elements' value. Using a statistical methodology, we intend to obtain a unitary image regarding the importance of the performance in different stages of the value chain within the oil companies.
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