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The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding.

The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results.

The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status.

This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.

Penetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.

This paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.

The experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.

This paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

The purpose of this paper is to analyze the characteristics of sound during gas tungsten argon welding (GTAW), which is very important to effectively monitor the welding quality in future by using the information extracted from sound.

The hardware used in the experiment is described. Then the paper researches the influence of welding techniques (gas flow, welding speed, welding current, and arc length) on arc sound and the distribution of the welding sound field. Finally, the relation between welding power and sound are studied based on Fourier transforms and recursive least square methods.

The sound pressure is affected greatly by gas flow, arc length, and current; welding sound source obeys the dipole model; the sound can be better predicted when the three orders derivative of the welding power are combined together.

This paper provides a new insight into welding sound resource model and a detailed analysis of the influence of the welding sound caused by welding techniques.

This paper aims to assess the combined effect of the Cold Metal Transfer (CMT) advanced process and of a thermal management technique (near immersion active cooling [NIAC]) on the macro and microstructure of Al wall-like preforms built by wire arc additive manufacturing (WAAM). As specific objective, it sought to provide information on the effects of the electrode-positive/electrode-negative (EP/EN) parameter in the CMT advanced process fundamental characteristics.

Initially, bead-on-plate deposits were produced with different EP/EN ratios, still keeping the same deposition rate, and the outcomes on the electrical signal traces and bead formation were analyzed. In a second stage, the EP/EN parameter and the layer edge to water distance (LEWD) parameter from the NIAC technique were systematically varied and the resultant macro and microstructures compared with those formed by applying natural cooling.

Constraints of EP/EN setting range were uncovered and discussed. The use of the NIAC technique favors the formation of finer grains. For a given EP/EN value, a variation in the NIAC intensity (LEWD value) showed marginal effect on grain size. When the EP/EN parameter effect is isolated, i.e. for a given LEWD setting, it was observed that an increase in the EP/EN level favors coarser grains.

Both the EP/EN parameter and the use of an active cooling technique (NIAC) might be used, even in combination, as effective tools for achieving proper macro and microstructure in WAAM of thin wall builds.

Joining, while first and foremost a pragmatic undertaking, concerned more with needs and results than with theory, will likely have to change with the dawn of the twenty‐first century to a true science. As materials become ever‐more sophisticated in their chemical composition, molecular morphology, micro‐ and nano‐structure, and macro‐structural arrangement to provide ever‐better functionally specific properties, a more complete and precise understanding of how such materials can be joined for optimal effectiveness and efficiency will become essential. Traditional options for joining will surely evolve – sometimes to provide unimagined capabilities. But, in addition, totally new methods will almost certainly emerge as evolution of materials gives way to revolution to meet unimagined new designs and design demands. This paper takes a glance at the past and a hard look at the present in the hope of catching a glimpse of the future.

The purpose of this paper is to present a 3D finite element model of the electromagnetic fields in an AC three‐phase electric arc furnace (EAF). The model includes the electrodes, arcs, and molten bath.

The electromagnetic field in terms of time in AC arc is also modeled, utilizing a 3D finite element method (3D FEM). The arc is supposed to be an electro‐thermal unit with electrical power as input and thermal power as output. The average Joule power, calculated during the transient electromagnetic analysis of the AC arc furnace, can be used as a thermal source for the thermal analysis of the inner part of furnace. Then, by attention to different mechanisms of heat transfer in the furnace (convection and radiation from arc to bath, radiation from arc to the inner part of furnace and radiation from the bath to the sidewall and roof panel of the furnace), the temperature distribution in different parts of the furnace is calculated. The thermal model consists of the roof and sidewall panels, electrodes, bath, refractory, and arc. The thermal problem is solved in the steady state for the furnace without slag and with different depths of slag.

Current density, voltage and magnetic field intensity in the arcs, molten bath and electrodes are predicted as a result of applying the three‐phase AC voltages to the EAF. The temperature distribution in different parts of the furnace is also evaluated as a result of the electromagnetic field analysis.

This paper considers an ideal condition for the AC arc. Non‐linearity of the arc during the melting, which leads to power quality disturbances, is not considered. In most prior researches on the electrical arc furnace, a non‐linear circuit model is usually used for calculation of power quality phenomena distributions. In this paper, the FEM is used instead of non‐linear circuits, and calculated voltage and current densities in the linear arc model. The FEM results directly depend on the physical properties considered for the arc.

Steady‐state arc shapes, based on the Bowman model, are used to calculate and evaluate the geometry of the arc in a real and practical three‐phase AC arc furnace. A new approach to modeling AC arcs is developed, assuming that the instantaneous geometry of the AC arc at any time is constant and is similar to the geometry of a DC arc with the root mean square value of the current waveform of the AC arc. A time‐stepping 3D FEM is utilized to calculate the electromagnetic field in the AC arc as a function of time.

To fabricate fully dense components with low costs, a rapid prototyping (RP) system based on micro‐plasma arc welding (MPAW) was developed. The appropriate process parameters were investigated to build the parts with good mechanical properties and surface smoothness.

A simplified overlapping model between deposited tracks was established to investigate the relationships among the overlapping parameters, such as the ratio of width to height of the deposited track cross‐section (λ), scan spacing and overlapping ratio. Some ER308L stainless steel parts were built by different overlapping parameters, and the surface smoothness, tensile strength and elongation of the parts were tested.

The overlapped surface smoothness, tensile strength and elongation of the parts built with larger λ were better than those built with smaller λ. The longitudinal tensile strength and elongation of the parts were better than the transverse data.

The scanning direction obviously affected the tensile strength and elongation of the parts, so the multi‐directional scanning mode should be used to get isotropic parts.

This MPAW‐based RP system provides a solution to build fully dense metal parts with relatively lower costs. The appropriate process parameters can be obtained with the developed overlapping model.

This paper aims to introduce a novel concept of a double-wire feed (DWF) to alleviate heat accumulation and improve the cooling rate of the molten pool in gas tungsten arc (GTA)-based additive manufacturing (AM), in which the former wire is fed into the arc and the latter wire is melt by the molten pool.

The microstructure, phase composition and mechanical properties of 308 L stainless steel components built by single-wire feed (SWF) AM and DWF-AM are compared, and the differences are analyzed in detail.

The microstructures for both wire feeding modes include δ and γ phases. Compared with the SWF-AM, the sample fabricated in the DWF-AM exhibits finer microstructure, and the microstructure in the middle region is transformed from columnar grains to cellular grains. Microhardness of the sample produced in the DWF-AM is higher than the SWF-AM. In comparison to the SWF-AM, the tensile strength of the specimen fabricated using the DWF-AM reaches 571 MPa and increases by 16.14%.

This study proposes a novel concept of the DWF-AM to reduce heat accumulation as well as enhance the cooling rate of the molten pool, and improved mechanical properties of the 308 L stainless steel component are obtained.

The most difficult tasks in the design and development of products for diverse engineering applications were the selection of suitable materials. Choice of inappropriate process variables leads to poor performance, which increases the cost of the product. The selection of the best option of available alternatives is important to improve the performance and productivity of the manufacturing enterprises.

The paper aims to develop Hybrid Multi-Criteria Decision Making (HMCDM) by integrating two potential optimization techniques Elimination Et Choix Traduisant la REalité and multi-objective optimization on the basis of ratio analysis. The weight of the criteria was calculated using the critic weight method.

The efficiency and flexibility of the proposed HMCDM technique were illustrated and validated by two examples. In the first case, the best electrode material among the five available alternatives was selected for the electrical discharge machining of AZ91/B4Cp magnesium composites. In the second case, the optimum weight percentage of composites providing the best tribological properties was chosen.

It was noted that the HMCDM methodology was quite simple to comprehend, easy to apply and provided reliable rankings of the material alternatives. The proposed hybrid algorithm is suitable for product optimization as well as design optimization.

The purpose of this study is to examine the postprocessed wire tool surface using scanning electron microscopy and find out the streamlined conditions of input process variables using multi-objective optimization techniques to get minimum wire wear values.

A federated mode of response surface methodology (RSM) and artificial neural network (ANN) is used to optimize the process variables during the machining of a nickel-based superalloy.

The study explores that with the rise in spark-off time and spark gap voltage, the rate of wire tool consumption also escalates.

Most of the researchers used the RSM technique for the optimization of process variables. The RSM generates a second-order regression model during the modeling and optimization of a manufacturing process whose major limitation is to fit the collected data to a second-order polynomial. The leading edge of ANN on the RSM is that it has comprehensive approximation capability, i.e. it can approximate virtually all types of nonlinear functions, including quadratic functions also.