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.

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.

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.

This paper presents a method for obtaining a neural model used in industrial robots control. The method refers to the forming of a small number of examples used in the training of a neural network that lead to the creation of a suitable model. This paper constitutes a development of the work [2] in order to increase the opportunities for its application in various fields. The description of the method is generally done, without relying on a specific application in the domain of industrial robots. The testing and the validation of the shown method were completed using the example of a system in which the relationship between inputs and outputs is described by means of mathematical functions. The set of learning examples, generated through the proposed method, served to the ANN training by a cross-validation technique, in case of these functions. The evaluation of the proposed method has been done by analysing the results obtained by applying it compared to those obtained with a known method, namely the uniform generation of training examples. The use of the method in the field of industrial robots’ control was illustrated by a concrete application in the case of a robot with 6 degrees of freedom.