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.

Abstract. The paper refers to a composite material developed for manufacturing thermoformed products with applications in furniture making, automotive industry etc., a method and machinery for manufacturing the material in unwoven form. From this material, Research and Development Department of TAPARO SA has designed and built a series of furniture components. The composite material made of a thermoplastic fibrous component and hemp fibre component, the way of obtaining and the properties of the thermoformed material presented in the paper are necessary in the process of designing and optimizing the parts.

No abstract

Abstract. Based on different design processes for innovative products, the paper presents a new approach of the process. The new method studies the problem of replacing the wooden components of the resistance structure of furniture with other materials. This is a reverse engineering process, that starts with the entire component, following a series of steps to the part drawings, ready for manufacturing. The method was validated by redesigning some parts of the upholstered products manufactured by TAPARO Company, by replacing the wooden parts of the resistance structure of sofas with composite material.

The paper studies the possibility of using artificial neural networks (ANN) to determine certain mechanical properties of a new composite material. This new material is obtained by a mixture of hemp and polypropylene fibres. The material was developed for the industry of upholstered furniture. Specifically, it is intended for the making of elements of the support structure of some upholstered goods (chairs, armchairs, sofa sides) with the objective of replacing wood. The paper aims to calculate the following mechanical properties: maximum tensile strength and maximum elongation.