Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

Pashazadeh, Hamed; Gheisari, Yousof; Hamedi, Mohsen

doi:10.1007/s10845-014-0891-x

Cited by 60 publications

(21 citation statements)

References 24 publications

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“…However this reduced nugget height is may slightly affect the tensile strength. Pashazadeh et al., 34 also reports that increasing the welding current causes nugget height reduction. TSFL and nugget area was increased with welding current depicted in Supplementary Figure 12(A) and 12(B).…”

Section: Resultsmentioning

confidence: 98%

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Krishnan

Elayaperumal

Paramasivam

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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This paper examines the impact of welding parameters on tensile shear fracture load, nugget geometry and microstructure of resistance spot welds (RSW) of austenitic stainless steel AISI 316 L and duplex stainless steel 2205 under lap shear loading condition. The macroscopic examination resulted that many of the nugget lengths were nearer to and higher than the AWS recommended value 4√t and failed at higher tensile shear load. Nugget height for DSS 2205 side was higher in comparison with AISI 316 L due to higher thermal conductivity of duplex stainless steel. Three welding parameters mainly welding current of 9 kA, heating cycle of 9 and electrode tip diameter of 6 mm were discovered as most effectual parameters on the tensile shear load and microstructure of weldments. Heterogeneous hardness was observed in the fusion zone due to the transition of equiaxed to columnar grains takes place in the both sides of nugget edge. DSS HAZ nearby BM observed higher hardness and ASS HAZ nearby BM reported lower hardness. WMZ Microstructure confirmed that thickness of austenite layers increased with heat input. Also, an unmixed zone in the microstructure identified as HAZ which contains delta ferrite. Scanning Electron Microscope (SEM) images in the nugget zone for different welding parameters confirmed that Intra-Granular Austenite (IGA) highly developed at higher welding current. SEM fractrograph for the tensile sheared specimens at higher and lower heat input confirmed the ductile type fracture even failed at Inter-Facial (IF) mode. Nugget area and nugget hardness were positively correlated with Tensile Shear Fracture Load (TSFL).

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Section: Resultsmentioning

confidence: 98%

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Krishnan

Elayaperumal

Paramasivam

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…After predicting the numerical values of spot diameters, a classification can still be made according to the tolerance bands, which usually vary for different welding conditions and user specifications. Other works (Tseng 2006;Hamedi et al 2007;Pashazadeh et al 2016) studied the optimisation problem, that is to optimise the influential factors so that the quality can improve.…”

Section: Related Workmentioning

confidence: 99%

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

et al. 2022

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Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance Spot Welding. Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

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“…The surrogate models described in [29] were trained on a set of samples generated by a classical DoE strategy -a Taguchi L 25 orthogonal array. Full-factorial design is another classical DoE strategy that has been employed successfully in combination with surrogate-based multi-objective evolutionary algorithms to find optimal process-parameter combinations whenever the search space is sufficiently small [45] (i.e., five process parameters). While both [29] and [29] report overall successful optimization results, the neural network models on which their surrogates are based show signs of overfitting (likely on account of the low number of available data samples).…”

Section: Comparison With Related Workmentioning

confidence: 99%

A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production

Zăvoianu

Lughofer

Pollak

et al. 2021

Applied Soft Computing

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We describe a general strategy for optimizing the quality of products of industrial batch processes that comprise multiple production stages. We focus on the particularities of applying this strategy in the field of micro-fluidic chip production. Our approach is based on three interacting components: (i) a new hybrid design of experiments (DoE) strategy that combines expert-and distribution-based space exploration with model-based uncertainty criteria to obtain a representative set of initial samples (i.e., settings of essential machining process parameters), (ii) construction of linear and non-linear predictive mappings from these samples to describe the relation between machining process parameters and resulting quality control (QC) values and (iii) incorporation of these mappings as surrogate fitness estimators into a multi-objective optimization process to discover settings that outperform those routinely used by operators. These optimized settings lead to final products with better quality and/or higher functionality for the clients. The optimization module employs a co-evolutionary strategy we developed that is able to deliver better Pareto non-dominated solutions than the renowned NSGA-II multi-objective solver. We applied our proposed highlevel surrogate-based multi-objective strategy both in a single/late-stage optimization scenario and in a more challenging multi-stage scenario, yielding final optimization results that improved parameter settings and thus product quality compared to standard expert-based production process parameterizations.

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Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

Cited by 60 publications

References 24 publications

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production

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