However, this process is generally limited to single nominal product geometries.
Geometry variants are neither tested nor compared. Thus, no support is provided on how the different product geometries, constituting the design space, affect the output of the welding process. Industry aligns with academy in this case. During pre-production phases, manufacturing analyses are carried out for an already selected single nominal geometry. However, a range of possible geometries are not analyzed with regards to manufacturing. This leads to a lack of awareness of production capabilities with respect to welding, since this manufacturing method output is highly dependent on product geometry.
A slight change in product geometry can result in a totally different welding outcome. The lack of data and information about production capabilities hinders the application of DFM approaches and manufacturability assessments within the design space exploration process where a number of design variants are considered potential design candidates.
Therefore, an important aspect when building DFM selection tools and methodologies for designers is the provision of clear guidance in identifying and quantifying the important design issues that affect manufacturing. Scope of the paper In this paper, a method is proposed to identify and assess geometrical design parameters that affect welding outcomes, thus compromising product quality and cost. The aim is to provide designers with support when exploring and analyzing the design space concerning welding capabilities, building reliable capability databases.
Such a framework for interactive analysis of welding capabilities presenting the relevant design issues would support concurrent development of the design and welding processes earlier in the product development process, connecting welding specialists with designer specialists and supporting virtual and interactive design. The focus of this paper has been on high performance and integrated products that are fabricated using welding.
In Sect. Welding simulation combined with interviews have been chosen as a mixed method approach to assess the welding capabilities for different design variants. Coming sections cover discussion and conclusions. This section presents a review of previous work in fields relevant to the method proposed in this paper.
First, the authors introduce the multidisciplinary design context for aerospace products. Next is a review of Design for Assembly DFA and DFM methods, in which the authors identify differences between the types of product and manufacturing processes used in the DFM and DFA methods reviewed, in particular with respect to welded aerospace components. One main difference is the evaluation criteria which need to shift from cost and time towards quality. Based on the above, in the last part of the background Sect. The design of aircraft engines involves expertise within various disciplines, including aerodynamics, mechanical engineering and manufacturing.
Requirements from multiple engineering disciplines must be traded off against target cost and customer value. This situation has motivated the increased adoption of parameterized product models together with multidisciplinary optimization techniques. Different methods and simulation tools are employed to find the optimal value of each design parameter in order to fulfill technical requirements [ 20 ].
Furthermore, in early phases, designers must account for uncertainties in the requirements due to the large number of different partners involved in the design process, and the complexity of the engine system. To approach the uncertainty and complexity, requirements are defined in ranges, with a set of possible solutions, referred to as Set-Based Design SBD [ 21 ]. In SBD, a broad set of design variants is considered and analyzed. This set of variants is narrowed down as the detailed requirements are specified and knowledge about the feasibility of the different solutions is generated.
In recent years, multidisciplinary design MDD has progressively benefitted from advancements in computer performance and statistical analysis methods for design space exploration [ 22 ]. The automation capabilities within computer-aided design CAD software have improved, enabling design engineers to automatically generate a large number of different design variants [ 20 ]. These models can be assessed from the perspective of many disciplines and there are significant achievements in automated analysis within Mechanical Engineering and Computational Fluid Dynamics CFD [Ansys Workbench, Hyperworks, Siemens Advanced Simulation].
However, the assessment of manufacturing capabilities based on CAD geometry is, if possible, mostly limited to interactive and manual analysis of a single design. There is no automated assessment that can be used, where the need to cover an extended number of design variants is the greatest. One of the reasons is the lack of manufacturing capability data and quantitative data to perform optimization and evaluate trade-off alternatives [ 15 , 16 ]. Much of the recent work focuses on providing computer support by using expert systems and Knowledge-Based Engineering KBE [ 14 , 24 , 25 , 26 , 27 ].
In broad terms, traditional DFA and DFM methods can be classified in two main groups: Qualitative methods composed of guidelines and heuristic illustrations, and quantitative methods for analyzing design alternatives based on cost and time criteria. In the field of qualitative DFA methods, Andreasen et al. Their handbooks provide an understanding of the technical capabilities and limitations of specific manufacturing processes. These guidelines were initially produced with mature production technologies in mind, such as machining processes, injection modeling, casting or stamping.
Other authors, such as Swift et al. However, the information they contain about welding is vague with no further content than the welding handbooks can provide [ 29 , 30 ]. These methods base redesign improvements on simplifications of the product structure by reducing the number of parts, thus reducing assembly time. The different product concept alternatives are evaluated based on assembly difficulty and assembly time. DFM was intended to be applied at the part design level, after DFA had addressed the product structure design level. In a first step, DFM methods intend to assist the manufacturing process and material selection, and in a second step, they seek to improve the design to optimize manufacturing costs.
Traditional DFM methods are those assessing part-manufacturing difficulties. Therefore, the focus is on optimizing the design for individual production processes, such as casting, stamping, injection molding and machining processes. Cost estimation models have also been developed as DFM quantitative tools to evaluate manufacturability [ 6 , 9 ]. Some of these methods are feature-based evaluation tools. Cost indices are provided for processing the different features using parametric models and a library of manufacturing knowledge bases. Although DFM has traditionally been focusing mainly on part forming processes with regard to welding, Schreve et al.
This cost model uses a times and rates approach but does not focus on output quality. In the methods described above, the type of products in which DFA and DFM are usually applied are those that can be complex in geometry and that contain large numbers of parts. However, in these products, geometry is not highly linked to functionality.
This fact allows easy geometrical modifications to solve manufacturing difficulties, as well as product structure modifications to solve assembly difficulties. These actions are aimed at reducing time and cost during production. Nevertheless, as mentioned in the introduction, welded aircraft structures are products made of geometries closely linked to functionality.
Thus, manufacturing variation in key product characteristics becomes a critical issue. Therefore, for this type of application, producibility criteria cannot solely rely on the time and cost spent during the manufacture and assembly but also on the quality built into the product, as suggested by the authors in previous studies [ 33 , 34 ].
The objective then becomes to reduce quality-related failures during production, thereby minimizing rework costs. Under the umbrella of quality, recent research in the area of variation management has been focusing on reducing quality failure and related costs [ 15 , 35 , 36 ]. Subramaniam et al. Their approach focuses on producibility problems that arise due to part geometries. However, this approach only covers forming processes, such as extrusion, injection molding, casting, and machining processes but not welding.
Even so, their research has provided significant inspiration to the authors in developing the method proposed in this paper. Product quality is built into the product throughout the manufacturing process. The set of operations within a manufacturing process transform a product from raw material to a final shape, material and properties. Madrid et al. This model was inspired by the Theory of Technical Systems TTS [ 38 ] and by manufacturing variation models [ 35 , 39 ].
Model of quality creation during manufacturing a single operation b entire fabrication process [ 34 ]. Transformation process executed by a Technical System as proposed by Hubka and Eder [ 38 ]. In the sequence of manufacturing operations, see Fig. The sequence of KCs is made so that inputs and outputs of each operation Opi can represent the variation propagation.
In a similar fashion, some authors [ 41 , 42 , 43 ] have been using the KC flowdown approach developed by Thornton [ 35 ] to determine what drives quality. An example of a KC flowdown is given in Fig. In addition, a manufacturing operation can be controlled by factors related to both the product and the manufacturing systems, which in their interactions influence the variation of the operation outcome. Example of KC flowdown applied on airplane wings system by Thornton [ 35 ]. The method proposed in this paper is an extension of previous work that resulted in the model presented in Fig.
This model is aimed at classifying and representing the different factors affecting the product quality creation during the sequence of manufacturing operations. This previous work did not address how to extract and assess those factors. In this paper, a method is presented that supports the systematic identification and assessment of factors related to the design of the product geometry and that affect the quality of the product resulting from the fabrication process, specifically welding.
In this way, the welding capability space is analyzed, thus supporting design space exploration during virtual and interactive design. In this research, production quality is defined as the concept of capability, as in Quality Engineering theory [ 44 ]. Quality is achieved if the output variation of a manufacturing operation is within the tolerance limits. Thus, the manufacturing capability space is defined as the design parameter space that fulfills manufacturing quality.
Step 1 The first step involves identifying the key product characteristics KCs , output of the welding operation system, critical to ensure product performance quality big Q-quality. The target value and tolerance of each output KC are then set to fulfill technical requirements. Step 2.
This involves setting tolerances for the design parameters that act as control variables on which the welding output is dependent. In this way, the limits of the welding capability space can be drawn. Three main groups were targeted as interview subjects: design engineers, welding engineers and welding simulation engineers. The interviews with design engineers were carried out around two topics of discussion. The first part asked about how welds influence product performance. This allowed the identification of generic output KCs, contributing to Step 1.
The second part discussed the critical product geometries and design parameters that have conflicting product performance and welding capabilities. Half of the interviews were held with welding engineers, during which the aim was to study in detail the phenomena occurring during welding operations. The lists presented in both steps are the result of converging data from these interviews and findings from welding handbooks, in which the areas of interest included weld quality and distortion.
The interviews with welding simulation engineers contributed to the development of Step 2. Company documents were also used to develop each method step. These documents relate to welding operations, procedure specifications, lessons learned, welding tests, as well as quality requirement standards for aerospace fusion welding. The first step involves identifying the output required from the welding operation under analysis, i. A deviation from the target value outside the tolerance limits due to manufacturing variation will affect product functionality see definition of key characteristics in [ 35 ].
In the identification of input and output KCs, the authors propose using the KC flowdown approach [ 35 ] to study how manufacturing variation in key product characteristics propagates throughout the manufacturing chain. Using the model developed by the authors see Fig. The model applied in an example is presented in Fig.
Connection to performance big Q-Quality.
Furthermore, product characteristics created in previous operations such as part geometry and joint preparation thickness, as shown in the Fig. For example, part thickness in combination with the the type of welding method will determine the amount of heat needed, which eventually will influence the distortion. Control factors related to product geometry are identified and assessed in Step 2. Output KCs For aircraft welded structures, the output key product characteristics KCs of a welding operation that will affect product functionality product life and aerodynamics include: 1 weld bead geometry, 2 metallurgical weld discontinuities and 3 those dimensions with geometrical variation that due to distortion will affect the performance functions mentioned.
In cases where the final assembly has not been reached, geometrical variation after welding can also influence the welding performance of the next sub-assembly [ 46 ]. Target values and tolerances need to be set in the output KCs to fulfill technical requirements. For that purpose, different calculation methods can be used.
The dimensions of the weld bead can affect aerodynamics and product life. For example, the weld bead high KC: ht can work as a step in the air flow if the weld is located perpendicularly to the flow path. Severe changes in geometry lead to strong notch effects and associated stress concentrations [ 29 ]. Thus, a definition of weld bead geometry and its tolerances is required [ 47 ]. These discontinuities reduce the cross-sectional area, thus amplifying stresses [ 29 ].
The limits of these defects regarding number, size and location, are determined by crack propagation calculation methods. Therefore, form tolerances on the output dimensions of this sub-assembly are needed. In addition, due to the variation stack up, in order to ensure alignment conditions gap, flush, parallelism for the performance of the next welding operation, the edges that will conform to the joint in the next assembly require special form tolerances.
Variation simulations are used to estimate the output result and set tolerances [ 39 , 46 ]. In the second step, the objective is to identify and assess the product design aspects that cause manufacturing variation in output KCs of the welding system, which were identified in Step 1. The failure modes, as defined by the authors, are the different manners in which the welding process can fail. A failure can occur either when the welding output exceeds tolerance limits, or when the operation cannot be performed due to, for example, accessibility problems.
Nine common welding failure modes have been identified and presented as guidelines. In this study, only failure modes relevant to aerospace applications have been considered. Failure mode 4: Overlap Overlap can happen when the fluid weld metal flows to the base material without any resulting fusion.
Fused metal lies over unfused metal forming a severe mechanical notch, as shown in Fig. The four failures modes described in Fig. Failure mode 5: Distortion primary During welding, the heating and cooling cycle makes the weld metal and adjacent base metal expand and contract. This phenomenon creates stresses and shrinkage forces that lead to product distortion. Three fundamental dimensional changes can cause transverse, longitudinal and angular distortions. When these dimensional distortions occur simultaneously, they can induce buckling. Product distortion translates into deformation at the edges, causing misalignments in the next assembly level.
Distortion is a consequence of material shrinkage if the part is not being restrained. However, conditions of high restraint increase the likelihood that hot or cold cracks may initiate in the weld metal or heat-affected zone [ 29 ]. Failure mode 6: Distortion due to multiple weld passes Welding is ideally performed in one weld pass. However, when welding is performed in multiple passes, particularly when the expected weld bead geometry cannot be initially achieved, shrinkage is accumulated.
The more the shrinkage accumulates, the greater the distortion. Therefore, the distortion from multiple weld passes is defined as a failure mode and separate from primary distortion, because primary distortion, as here defined, can only be mitigated but not avoided. Failure mode 7: Metallurgical discontinuities Pores and cracks are examples of metallurgical discontinuities, which represent changes in the properties of the weld or base metals.
Depending on the shape, size, quantity, distribution and orientation of the discontinuities within the weld, the effect on mechanical properties can be more or less severe. Failure mode 8: Limited access to weld Accessibility is a failure mode, as it inhibits the welding operation from being executed. Limited access to welding makes it difficult for the welder to precisely guide the welding process and increases the likelihood of other failure modes, such as metallurgical discontinuities or weld bead geometry discontinuities [ 29 ].
Failure mode 9: Limited access to inspect In aerospace applications, the inspection requirements demand every weld to be inspected. Accessibility to inspect is as important as accessibility to weld. Each failure mode is modelled by considering the underlying physics of the transformation process that occurs during the welding operation. During a welding operation, many phenomena occur involving specific part geometry and welding parameters.
Currently, there does not exist a precise process model or simulation tool that can accurately predict the likely success of the process. In some cases approximate models exist, but in other cases such models may need to be developed from scratch. Therefore, at this stage, if quantitative models of the physical phenomenon are not possible, experts working with the welding process can make qualitative descriptions of the phenomena occurring and qualitative evaluations of the design aspects that intervene in the phenomena.
Joint thickness t The thickness of the joint will determine whether it is possible to achieve complete joint penetration in a single weld pass for the material and welding method chosen. Joint penetration is related to the heat transfer phenomena that occur within the welded material. Depending on the material and welding method, the heat will be able to reach a certain depth. Each material has its own thermal conductivity and each welding method transfers the heat into the material differently. In addition, the plate thickness has a strong effect on the heat flow. A transition from 2D to 3D heat flow can occur, hampering the complete joint penetration.
A significant change in thickness within a short distance will not allow the welding method to rapidly adjust the welding parameters, thus resulting in discontinuities.
Inner radius r In the case of a curved weld, the inner radius of the curve can cause overlap on the root side as shown in Fig. This is related to the depth of the weld bead root side. The weld root of the two welded sides converging on the corner can overlap causing a notch area similar to a crack. Since access inside the curved area needs to be provided to ensure visibility according to the requirements of the Non-Destructive Testing NDT methods chosen, the inner radius can also limit the inspectability of the root side of the weld.
Outer radius R In the curved region of a closed weld, the robot and the part to be welded need to synchronize their movements so that the robot adopts a normal position to the part at any instant.
Therefore, the outer radius of the curve, R, can limit the relative movement between the part and robot, thereby obstructing the operation. Having overcome this operational failure, there are other failures particularly of closed joints and related to weld quality. When welding the curved area, the welding torch turns around the part rotating around the same point for several instances, which causes increases in the volume of the melted pool and temperature in the material, thereby increasing viscosity.
If the volume of the melted pool is bigger and the viscosity higher, the effect of gravity, when part and robot are moving, will cause a higher drop of melted material which can lead to overlap discontinuities on the top side and incomplete joint penetration on the root side. Reducing the power effect in the curve through the welding parameter settings may be an option to address this failure.
However, a drastic drop of the power effect can cause incomplete joint penetration problems. Thus, an approach that sometimes is adopted to ensure weld bead quality is to employ multiple weld passes around the curved area, which consequently will lead to more distortion. Common example of a parameterized cross-section of a closed weld in an aircraft blade application.
To facilitate the analysis, the position of the weld can be parameterized. An example of a common weld in aerospace application, which has been parameterized, is shown in Fig. Distance H The parameter H defines the distance between the weld and a nearby product element. This distance can limit the accessibility of both the welding torch and the equipment to inspect.
A deviation from that normal direction can have an effect on the weld bead geometry or metallurgical discontinuities, thus affecting weld quality [ 50 ]. Expert knowledge of process capability, historical manufacturing data, laboratory and simulation data can be employed to make both qualitative and quantitative assessments. In Step 2. This qualitative assessment serves as an initial guide to quickly evaluate the impact of the design on welding outcomes. However, this needs to be complemented with quantitative assessments to provide a more accurate evaluation that can support tolerancing.
Thereafter, quantitative methods are discussed.
Thickness t : The thickness of the joint is one of the main variables to determine the choice of welding method. Each welding method has a range of joint thicknesses for which the method is capable of achieving complete joint penetration. Outside this joint thickness range, no combinations of welding parameters would be able to achieve complete joint penetration in a single weld pass. Decomposition from product system to component level to part level to weld interface. Problematic area is indicated in weld cross-section. Inner radius r : With regard to weld bead geometry failures, as explained in Step 2.
The smaller the radius, the higher the probability of overlap on the root side see Fig. The accessibility to inspect can also be limited by this parameter. Narrower inner radii are more difficult to inspect inside the root side of the weld. Outer radius R : Narrower outer radii are more difficult to rotate around the curve while welding. A sharp outer radius would place high demands on synchronizing the robot-workpiece movement. Additionally, this relative movement between robot and workpiece to constantly try to obtain a perpendicular position of the weld torch towards the joint can cause failure including overlap and incomplete joint penetration.
A narrower outer radius makes the torch rotate longer around the same centre point, thereby overheating the curved area and causing a larger weld pool melted material with higher viscosity due to the increment in temperature. Therefore, if the radius decreases, the probability of dropping the weld pool causing a top side overlap increases. A first option to deal with a narrow radius would be to reduce the power effect in the curved area, which may cause complete joint penetration.
Another option would be to undertake a second weld pass, which would cause greater distortion but ensure weld bead quality. If H decreases so that the weld is located closer to the nearby object, the access of the welding torch becomes more limited. Welding handbooks Thickness ranges for different materials and welding methods have been extensively studied and can be found in welding handbooks [ 30 , 45 ]. Some recommendations about thickness uniformity and relations between the thinner and thicker parts of a joint can be found in guidelines and company standards, however not for all combinations of welding methods and materials.
Still, physical experimentation is required. Physical tests Quantitative assessments that would indicate a range of action for parameters r, R and H with regard to weld quality problems are difficult to perform and may only be performed via physical tests. Today, in industry practice, welding simulations cannot reproduce solidification phenomena when forming the weld bead nor the creation of material microstructure and metallurgical discontinuities.
Therefore, design parameter ranges become known because of physical experimentation. However, physical tests are expensive to perform when the effect of different geometries is being analyzed. With regard to accessibility, physical tests can be used to study the interaction between the welding and inspection equipment towards the part. Nevertheless, simulation is more advanced in this area.
Simulation tests In industrial practice, welding simulations sometimes combined with variation simulations are extensively employed to calculate distortion, i. However, not all phenomena related to welding can be virtually modelled. Nevertheless, in this paper, the authors propose a method using welding simulations to study incomplete joint penetration and overlap see Case Study section. With regard to accessibility, mathematical models can be developed connecting the welding torch and product model dimensions including split lines [ 51 ].
There also exists path planning software to study the collisions and movements of the robot in relation to the range of values for different geometrical parameters [ 52 ]. Through a case study, the method proposed is justified and applied to a concrete weld within an aircraft engine structure.
The approach can help designers identify critical design characteristics to the welding operation and the means for assessing the design space regarding welding capabilities. The TEC is a product situated at the rear frame of a jet engine, in the path of exhaust gases, where it is exposed to temperatures up to C during normal flight conditions. The TEC also works as a mounting device to secure that the engine is attached to the wing of the aircraft, as well as serving as a load carrier for other systems.
Therefore, this structure must withstand significant thermal and structural loads. In addition, the component needs to be as light as possible and possess good aerodynamic properties to optimize fuel efficiency and reduce CO2 emissions. Materials employed in their fabrication are nickel-based super alloys due to their high temperature resistant properties.
Different welding methods are also employed for their fabrication. This component is common in various product families and four variants belonging to such product families have been studied. The differences among the variants concern such features as the size or product structure or the number of blades. However, all variants share similar weld features. They all include a split line in the blade a closed weld that connects the blade to the rest of the structure as shown in Fig.
This type of weld is characterized by a complex contour geometry see weld cross-section in Fig. Among the various potential welding methods to employ, gas tungsten arc welding GTAW has been selected for the case study. GTAW welding, commonly known as tungsten inert gas TIG welding, is an arc welding process that employs a non-consumable tungsten electrode to create the weld.
Several factors make the welding of this contour more troublesome than, for example, a straight weld of two identical thick plates, a common example used in welding research.
From production experience, it has been concluded that some of the problems aggregate in the curved area of the weld where many phenomena occur during welding see problematic area indicated by a slashed circle in Fig. There are more than different welding processes depending on the specifics of heat input and pressure application. Besides the usual application of welding as a joining method for metals, the importance of the welding process for joining of plastics and glasses is increasing.
Although joining is the main application field of welding processes, it is also used in deposition processes in order to create durable and hard surfaces by means of cladding. There have been two main demands from industry to researchers during the past few years. First, in order to move further towards energy efficiency in the automotive industry , the importance of lightweight construction is increasing.
The development of new high-strength materials, as well as welding of dissimilar alloys, like steels, titanium and aluminum alloys, are representing a serious challenge for joining technology. Second, decreasing energy consumption during the production itself is desirable. The amount of filler material is reduced and lap joints are replaced with butt joints in order to reduce the overall weight of the product.
Welding processes with low energy input , numerical welding simulation, and application of virtual welding trainers contribute to the overall goal to make joining technology more energy- and material-efficient. A lot of progress has been made concerning the combination of different welding processes hybrid welding. The combination of metal arc welding with laser beam welding in particular has been successfully adapted for industrial production through utilization of the advantages of both processes.
These advantages include high energy density, penetration depth, and the feed rate of a laser beam welding process as well as high gap bridging ability and the minimal welding defects of an arc welding process. Both processes combined allow single pass welding of components with thick walls, which would be not possible for each process applied by itself. Furthermore, if it comes to joining of aluminum components, such hybrid approach helps to reduce the usage of fluxing agents, simplifying the process chain and reducing the number of needed production steps. Hybrid processes such as these have a high economical potential.
On the other hand, industry tends to reduce investments in welding technology wherever possible instead of making investments in new technologies. Expensive staff training is often reduced by subcontraction, which also reduces required manpower. Thus, at the moment it seems that recently developed welding processes will not succeed on the market.
Only welding equipment for established processes seems to be worth the investment. In this case, there is a higher probability for investment in new welding equipment when it comes to replacement of older equipment. Due to a lack of experienced welding specialists, available professionals are forced to complete more demanding work in less time. This situation leads to an increased amount of mistakes due to insufficient abilities of staff members and overloading of experienced specialists.
Labor costs, as well as overall costs for development and trial tests, are expected to increase. Welding simulation software offers the possibility to capture the institutional knowledge of welding processes, allowing virtual try-outs that help to investigate process parameters and their influence on the results of an applied welding process, as well as support in finding and documenting convenient process parameters.
Based on T. In order to prove the general weldability of a structure, one has to consider and plan the weld reliability design , weldability choice of material , and welding feasibility manufacturing. These domains interact with each other — particularly with regard to welding distortions. Welding distortions play economically together with reduced strength of a component - the most important role for a welding process design or of a welding assembly design.
Unexpected welding distortions often cause expensive subsequent machining and straightening steps. Additionally, further effects might influence the quality of the final product.