In manufacturing system management, the decisions are currently made on the base of 'what if' analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.
This paper concerns a new approach of the optimization problem, intending to turn into profit the last evolutions from IT domain. On the base of this approach, both new concept (the holistic optimization), and method ("zoom & pick") for its implementation it in manufacturing process optimization were developed. According to the new concept, the optimization problem gets a new structure, which includes not only the optimal solution finding, but also the optimal formalization of the problem as well as the tooling for assessing the position of potential solutions relative to the optimal one. The method for holistic optimization successively addresses the optimized object at different levels of its description. The main application domain is the manufacturing process from "Make to Order" environment, which is difficult to optimize because of dealing with a wide range of products.
In this study is present a systematic analysis of already published works on formulating and solving optimization problems concerning manufacturing process. Analysis it was performed on two levels, namely: planning and scheduling of manufacturing process. They were considered: type of optimization (mono-criterion or multi-criteria); objective function (the energy consumption, the manufacturing costs, the productivity, the manufactured surface roughness); methods of solve (Genetic Algorithms GA, Particle Swarm Optimization PSO technique, Artificial Neural Networks ANN). The main purpose of this study it is to substantiate a new approach to optimization problems. The proposed approach is of holistic type, based on integrated process planning and scheduling (IPPS) and defines new performance indicators, to be adapted to market current requirements.
Energy consumption is different for various technological processes used in manufacturing (cutting, plastic deformation, sintering, welding, etc.) and nowadays is becoming a more and more important issue to be considered when planning certain manufacturing processes. Related to this, when releasing a quotation for a given product request coming for the market, it is important to have a tool enabling to evaluate the estimated energy consumption that will be required. This paper proposes a method to predict the consumption of energy in the welding process depending on the same main parameters of the process. The method is based on causal identification and on the NN modelling technique. Method application is sampled in the case of a real database including information concerning pipes welding. The method proves to be fast and delivers results of reasonable precision.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.