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Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

Digital.CSIC. Repositorio Institucional del CSIC
  • Rivas, Daniel
  • Quiza, Ramón
  • Rivas, Marcelino
  • Haber, Rodolfo E.
Optimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty's method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars., This work was supported in part by the project Power2Power: Providing Next-Generation Silicon-Based Power Solutions in Transport and Machinery for Significant Decarbonisation in the Next Decade, funded by the Electronic Component Systems for European Leadership (ECSEL-JU) Joint Undertaking and MICINN under Grant 826417, in part by the European Commission through the Project H2020 Platform enable KITs of Artificial Intelligence for an Easy Uptake of SMEs (KITT4SME) under Grant 952119, and in part by the Cuban National Program on Basic Sciences through the Project Multi-Objectives Optimization Heuristics for Industrial Applications, under Grant P223LH001-068., Peer reviewed




Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process

Digital.CSIC. Repositorio Institucional del CSIC
  • Cruz, Yarens J.
  • Rivas, Marcelino
  • Quiza, Ramón
  • Villalonga, Alberto
  • Haber Guerra, Rodolfo E.
  • Beruvides, Gerardo
This paper presents an approach for image classification based on an ensemble of convolutional neural networks and the application to a real case study of an industrial welding process. The ensemble consists of five convolutional neural networks, whose outputs are combined through a voting policy. In order to select appropriate network parameters (i.e., the number of convolutional layers and layers hyperparameters) and voting policy, an efficient search process was carried out by using an evolutionary algorithm. The proposed method is applied and validated in a case study focused on detecting misalignment of metal sheets to be joined through submerged arc welding process. After selecting the most convenient setup, the ensemble outperforms other seven strategies considered in a comparison in several metrics, while maintaining an adequate computational cost., This work was partially supported by the H2020 projects "Platform enable KITs of Artificial Intelligence for an Easy Uptake of SMEs (KITT4SME)" [grant number 952119]; and “Power2Power: Providing next-generation silicon-based power solutions in transport and machinery for significant decarbonisation in the next decade (Power2Power)” funded by ECSEL-JU and MICINN [grant agreement No. 826417]., Peer reviewed




Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0

Digital.CSIC. Repositorio Institucional del CSIC
  • Haber Guerra, Rodolfo E.
  • Castaño, Fernando
  • Armada, Manuel
  • Villalonga, Alberto
Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables.

The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc., European Commission: KITT4SME - platform-enabled KITs of arTificial intelligence FOR an easy uptake by SMEs (952119)., Peer reviewed
Proyecto: EC/H2020/952119