Significant efforts have been made to understand the intricacies of the welding process using numerical methods, machine learning, dimensional, and scaling analyses. Dimensional analysis (DA) is used for qualitative studies of weld pool spreading, heat transfer in welding, welding parameters, and detachment-droplet formation in welding. Nevertheless, DA have been used often in a rather conventional manner. This article proposes to combine DA principles with causally oriented graphical representations and functional analysis to augment the separate capabilities of those methods. The approach uses a physics-based functional model to decompose the welding phenomena into functions, with dimensionless numbers (π) representing aspects of those functions in form of mathematical relationships between the variables. These mathematical relationships are illustrated as a causally oriented graph. This graph is transformed into a system dynamic counterpart. The values of π numbers are estimated using a single example with the developed methodology. The π values in the model are the analogous of biases in Artificial Neural Networks (ANN). The current modelling approach has the advantage of exploiting supplementary sources of knowledge and consequently requires limited data in comparison to supervised machine learning (ML) algorithms used in the field. The proposed methodology is demonstrated with a case study of gas metal arc welding (GMAW) for mild steel. The developed model predicts the droplet formation in GMAW with high accuracy and offer multiple possibilities for extension and generalization to other welding and additive manufacturing processes.

Cold metal transfer wire arc additive manufacturing (CMT-WAAM) has attracted attention in recent years due to its ability to print walls with higher dimensional accuracy than regular WAAM. To print near-net shape parts by CMT-WAAM, there is a need to define a set of height-related geometrical parameters (HGPs) that can capture, quantify, and compare the quality of the height of the produced parts. In the presenting study, a set of HGPs, namely, the average height error, maximum height variation, and average absolute slope are defined and assessed. Fifteen single-track multi-layer walls are printed to check the effect of process parameters on the defined HGPs. It is found that the stability and quality of the print cannot be guaranteed by checking the visual appearance of the single beads and at least five-to-ten-layer walls should be printed. It is also found that the travel speed and the wire feed speed have positive monotonic relationships with average absolute slope and maximum height variation, respectively. Correlations between process parameters and HGPs are modeled and optimized using multi-objective optimization, and a validation test is performed to check the validity of the developed models. Moreover, HGPs of walls printed using unidirectional and bidirectional path strategies are calculated and compared. Defined HGPs are able to quantify, capture, and compare the quality of height of a wall with only three parameters. The HGPs can be used in further studies to report and compare the quality of height of thin wall structures.

Wire arc additive manufacturing (WAAM) has gained prominence in its utilization in the manufacturing industry due to its ability to build large functional components at high deposition rates. Among the different metal additive manufacturing processes, WAAM has the potential for adoption in the industry due to the ease with which the system can be integrated into factory's robotic welding cells. The ability to develop 3D components using welding has opened possibilities for redesigning and envisioning new product designs. However, there are still challenges related to ensuring process quality with WAAM. Path planning strategies have tremendous effects on structural integrity, mechanical and microstructural properties of the components. The current research aims to experimentally investigate the effect of different infill strategies on the hardness of cuboidal parts in WAAM. The experimental work uses high-strength low-alloy steel as the material of choice. These steels are found in many high-stress applications, such as automotive, load-bearing structures, and low-temperature applications that require a high strength-to-weight ratio. The study reported herein comprises of testing three different infill patterns and their impact on the final part performance (geometric, microscopic defects, and Vickers' hardness). It was observed that all three strategies ensured a stable deposition process, yet with micro and macro defects. Lack of fusion defects and pores were identified in one of the infill strategies through microscopic evaluation. The hardness mapping showed uniform properties in separate planes for all printing strategies.

This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity of temperature curves (curve segments of a temperature profile of one point) between any two successive layers, the first stage of the proposed method designs a layer-to-layer prediction model to estimate the temperature curves of the yet-to-print layer from measured temperatures of certain points on the previously printed layer. With measured/predicted temperature profiles of several points on the same layer, the second stage proposes a reduced order model (ROM) (intra-layer prediction model) to decompose and construct the temperature profiles of all points on the same layer, which could be used to build the temperature field of the entire layer. The training of ROM is performed with an extreme learning machine (ELM) for computational efficiency. Fifteen wire arc AM experiments and nine simulations are designed for thin walls with a fixed length and unidirectional printing of each layer. The test results indicate that the proposed prediction method could construct the thermal history of a yet-to-print layer within 0.1 seconds on a low-cost desktop computer. Meanwhile, the method has acceptable generalization capability in most cases from lower layers to higher layers in the same simulation, as well as from one simulation to a new simulation on different AM process parameters. More importantly, after fine-tuning the proposed method with limited experimental data, the relative errors of all predicted temperature profiles on a new experiment are smaller than 0.09, which demonstrates the applicability and generalization of the proposed two-stage thermal history prediction method in online applications for metal AM.

Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-the-art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.

Thermal history has a great effect on the part properties in metal additive manufacturing such as tensile strength and hardness. To study and control the thermal behavior of the AM processes, various data-driven thermal history modeling methods have been developed and tested on simulation data. However, their in-situ application scenarios are rarely explored and discussed. This paper aims to provide a layer-to-layer thermal history prediction model, which enables predicting the thermal history of a yet-to-print layer based on the data measured from the printed lower layers. First, the thermal behavior is analyzed to reveal the similarities in temperature curves of two successive layers. Then four input variables are identified, including the deposition rate, the relative height of the layer, the printing time, and the dwell time of one layer. Based on the selected input variables and the temperature output, a fully connected neural network with residual connection is designed to simplify the training process. Five numerical simulations are designed to collect temperature curves (curve segments of a temperature profile) on each layer, and one experimental study on wire arc additive manufacturing is completed to record the temperature curves. Based on the collected data, three cases are proposed to test the modeling framework, such as (a) dividing all simulation data into the training set and validation set, (b) training the model based on four simulation runs and its validation with the last simulation, and (c) training the model with all simulation data and testing on the experimental data. The former two cases show great prediction accuracies with a relative error of less than 5% in most cases, which indicates its potential in online prediction when trained with data from the same input conditions or same systems. While the applicability of the model trained only with simulation data should be explored further in real experiments.

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows.

Alloy steels are commonly used in many industrial and consumer products to take advantage of their strength, ductility, and toughness properties. In addition, their machinability and weldability performance make alloy steels suitable for a range of manufacturing operations. The advent of additive manufacturing technologies, such as wire and arc additive manufacturing (WAAM), has enabled welding of alloy steels into complex and customized near net-shape products. However, the functional reliability of as-built WAAM products is often uncertain due to a lack of understanding of the effects of process parameters on the material microstructure and mechanical properties that develop during welding, primarily driven by thermal phenomena. This study investigated the influence of thermal phenomena in WAAM on the microstructure and mechanical properties of two alloy steels (G4Si1, a mild steel, and AM70, a high-strength, low-alloy steel). The interrelationships between process parameters, heating and cooling cycles of the welded part, and the resultant microstructure and mechanical properties were characterized. The welded part experienced multiple reheating cycles, a consequence of the layer-by-layer manufacturing approach. Thus, high temperature gradients at the start of the weld formed fine grain structure, while coarser grains were formed as the height of the part increases and the temperature gradient decreased. Microstructural analysis identified the presence of acicular ferrite and equiaxed ferrite structures in G4Si1 welds, as well as a small volume fraction of pearlite along the ferrite grain boundaries. Analysis of AM70 welds found acicular ferrite, martensite, and bainite structures. Mechanical testing for both materials found that the hardness of the material decreased with the increase in the height of the welded part as a result of the decrease in the temperature gradient and cooling rate. In addition, higher hardness and yield strength, and lower elongation at failure was observed for parts printed using process parameters with lower energy input. The findings from this work can support automated process parameter tuning to control thermal phenomena during welding and, in turn, control the microstructure and mechanical properties of printed parts.

Design and manufacturing sectors are vital agents of an economy. However, multiple challenges influence product designs such as the predicted scarcity of energy and primary materials, the ubiquitous integration of electronic components and computing science in systems’ architectures, the pervasive production of data by most systems, the emphasis given to CO2 free energy solutions, recycling, and reuse, the transformation of the consumption model from product ownership to product as a service, as well as the geopolitical conflicts. Major technological advancements leading to transformation in socio-economic practices would be required to address these challenges which can have a profound effect on design and manufacturing activities. This research aims to evaluate the potential impact and modification induced by such transformations on product design process. The research identifies that early design automation can enable coping with unmanageable cognitive load generated by cascading changes. A list of modifications to current design practices is proposed to enable the development of a new generation of design tools. The article provides an initial prospective effort to discuss the potential services and functionality that will be offered by future design tools’.

Research on human health and safety impacts of wire arc additive manufacturing is often overshadowed by the need for weld quality and mechanical strength improvements. To address this gap, a review of research literature is conducted focusing on the influence of welding process parameters, welding fumes, and fume exposure on worker health. The review uses a causal graph to classify research literature into two domains: manufacturing technology and public health. The graph serves as a precursor to development of a Bayesian network model, whose expected benefits, steps for implementation, and likely challenges that would be encountered during implementation are discussed.

Additive manufacturing has been presented as a novel and competitive method to achieve unprecedented part shapes and material complexities. Though this holds true in niche markets, the economic viability of additive manufacturing for large-scale industrial production is still in question. Companies often struggle to justify their investment in additive manufacturing due to challenges in the integration of such technologies into mainstream production. First, most additive technologies exhibit a relatively low production rate when compared with traditional production processes. Second, there is a lack of robust design for additive manufacturing methods and tools that enable the leveraging of the attendant unique capabilities, including the ability to form organic part geometries and automated part consolidations. Third, there is a dearth of systematic part screening methods to evaluate manufacturability in additive manufacturing. To tackle the challenge of manufacturability evaluation, the present work proposes a novel approach derived from latent semantic analysis and dimensional analysis to evaluate parts and their production for a variety of selected metrics. The selected metrics serve as descriptors of design features and manufacturing functions, which are developed using functional modeling and dimensional analysis theory. Singular-value decomposition and Euclidean distance measurement techniques are used to determine the relative manufacturability for a set of parts for a specified manufacturing process technology. The utility of the method is demonstrated for laser powder bed fusion technology. While demonstrated for additive manufacturing here, the developed approach can be expanded for any given set of manufacturing processes. Expansion of this systemic manufacturability analysis method can support part design decision-making, process selection, and design and manufacturing optimization.

Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitable part quality, modelling methods are necessary to provide simulation capabilities for part quality analysis at early stages of product development. A systematic methodology is proposed to extract cause-effect relationships among variables and to transform this causal model into a Bayesian network. The Bayesian network is then used to predict the effect of specific design and manufacturing parameters on part defects and to estimate the needed input parameters backwards, based on acceptable output values.

Achieving predictable, reliable, and cost-effective operations in wire and arc additive manufacturing is a key concern during production of complex-shaped functional metallic components for demanding applications, such as those found in aerospace and automotive industries. A metamodel combining localized submodels of the different physical phenomena during welding can ensure stable material deposition. Such a metamodel would necessarily combine submodels from multiple domains, such as materials science, thermomechanical engineering, and process planning, and it would provide a holistic systems perspective of the modeled process. An approach using causal graph-based modeling and Bayesian networks is proposed to develop a metamodel for a test case using wire and arc additive manufacturing with cold metal transfer. The developed modeling approach is used to characterize the effect of manufacturing variables on product dimensional quality in the form of a causal graph. A quantitative simulation using Bayesian networks is applied to the causal graph to enable process parameter tuning. The Bayesian network inference mechanism predicts the effects of the parameters on results, whereas, conversely, with known targets, it can predict the required parameter values. Validation of the developed Bayesian network model is performed using experimental tests.

Increased competitiveness in the manufacturing industry demands optimizing performance at each level of an enterprise. Optimizing performance in terms of indicators such as manufacturing cost requires knowledge of cost-inducing variables from product design and manufacturing, and optimization of these variables. However, the number of variables that affect manufacturing cost is very high and optimizing all variables is time intensive and computationally difficult. Thus, it is important to identify and optimize select few variables that have high potential for inducing cost. Towards that goal, a dimension reduction method combining dimensional analysis conceptual modelling framework and graph centrality theory is proposed. The proposed method integrates existing knowledge of the cost inducing variables, their interactions, and input-output relationship for different functions or behavior of a system, in the form of a causal graph. Propagation of optimization objectives in the causal graph is checked to identify contradictory influences on the variables in the graph. Following the contradiction analysis, graph centrality theory is used to rank the different regions within the graph based on their relative importance to the optimization problem and to cluster the variables into two optimization groups namely, less important variables and most important variables relative to optimizing cost. The optimization problem is formulated to fix less important variables at their highest or lowest levels based on their interaction to cost and to optimize the more important variables to minimize cost. The proposed dimension reduction method is demonstrated for an optimization problem, to minimize the production cost of the bladder and key mechanism for a high-field superconducting magnet at CERN, capable of producing a 16 Tesla magnetic field. It was found that the graph region representing the electromagnetic force and resultant stress generated during energizing of the magnet ranked highest for influence on the bladder and key manufacturing cost. An optimization of the stress and its associated variables to minimize the manufacturing cost is performed using a genetic algorithm solver in Matlab.

Key performance indicators (KPIs) are used to monitor and improve manufacturing performance. A plethora of manufacturing KPIs are currently in use, with others continually being developed to meet organizational needs. However, obtaining the optimum KPI values at different organizational levels is challenging due to complex interactions between manufacturing decisions, variables, and desired targets. A Bayesian network is developed to characterize the interrelationships between manufacturing decisions, variables, and selected KPIs. For an additive manufacturing case, it is shown that the approach enables appropriate value estimation for decisions and variables for achieving desired KPI values and production cost targets in a manufacturing enterprise.