Abstract
This article introduces an algorithm for determining optimal parameters of a technological process. The objective function is the processing time of operations (efficiency) at the constraint of quality requirements of finish according to the designer specification. The problem of selecting a correct combination of processing parameters may only be solved when the cause-and-effect relationship between the finish quality and the machining settings is known. If the process considered for optimisation is repeatable, it appears economically viable to invest resources in the development of a model that would describe these relationships. To this end, we propose employing the artificial neural network trained on the progressions obtained from the tests. In the second stage, the Multiple-Input-Multiple-Output (MIMO) system, capable of representing relationships of nonlinear nature, was implemented for the optimisation of the objective function. The paper presents the application of the developed algorithm in determination of optimal parameters for the roller burnishing process of surface treatment. A technologist/software user defines the range of acceptable surface finishes. The optimisation algorithm determines a set of modifiable parameters that ensure minimal processing time at a specified surface finish requirements constraint.
Highlights
The technologies at the disposal of modern production companies are highly customisable
The algorithm determining the optimal modifiable parameter configuration of the considered process consists of three steps: − determining the input and output parameters of the modelled process; registering the values during operation of the workstation in the conditions representing batch production, − building the virtual model of input-output functional relations in the artificial neural network; − determining the input parameters that fulfil the defined specifications and provide optimal process efficiency in given conditions
The technological process of roller burnishing is applied with a view to decreasing the surface roughness and increasing the strain hardening of the material
Summary
The technologies at the disposal of modern production companies are highly customisable. An alternative solution, which is the subject of this paper, is based on artificial neural networks and genetic algorithms These methods are established on the data derived from the process under optimisation, the first step of the developed methodology consists in collection of data for analysis. Due to the complexity of modern industrial technologies it is considerably difficult to build the conventional model accounting for the character of functional relations (the non-parametric model) Another problem faced by engineers is determining the parameters and making the model functional. The third step consists in employing the properties of the trained network (the virtual process model) to establish the optimal set of technological parameters of the process, considering the design specifications and the maximal efficiency objective constraints. The steps described in the preceding paragraphs are detailed in the parts to follow
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