Design for welding: an application in top class cars chassis

doi:10.21203/rs.3.rs-1873679/v1

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Design for welding: an application in top class cars chassis

https://doi.org/10.21203/rs.3.rs-1873679/v1

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High quality standards and components downsizing are two main quests of the engineering designers in the nowadays automotive industry. To this aim, lightweight materials as aluminum are used. Moreover, most parts of the automotive chassis made of aluminum are assembled by means of welding techniques. Lightweight materials, such as aluminum, are more and more adopted in chassis design even if the assembly process represents a challenge from a technological point of view, especially due to the distortions caused by the welding process. In case of thin aluminum structures, non-convenient distortions occur due to a combination of effects of fixturing and cooling (after welding) process. The reduction of distortions or almost their prediction is still a very challenging issue, so that no conclusive technique has been found to exclusively fix the problem yet. In this paper, a “design for welding” approach is proposed as an integrated product design and process simulation method, with the aim of predicting the final distortion of an aluminum welded automotive chassis. Thermal-Elastic-Plastic (TEP) finite element analyses (FEA) simulations are capable to provide acceptable results, but require high computational efforts, especially in case of complex geometries as car chassis. To overcome this problem, in this paper, the local-global FEA approach has been adopted with satisfactory results. A top-class car automotive aluminum chassis has been chosen as a case study.

automotive design

FEA simulations

chassis design

local/global approach

interactive design

1.1. Background

In automotive industry contexts, high quality standards are mandatory. Moreover, lightweight materials (e.g. aluminum) are used to respond to the strong request for downsizing components. Most parts of the automotive chassis made of aluminum are assembled by means of welding techniques ([1]), so that welding is a consolidated assembly process in automotive industry ([2]) Nevertheless, thin aluminum structures are known to be affected by distortion problems ([3]). This phenomenon occurs because aluminum has a high heat conductivity, a coefficient of expansion which is almost twice than the one of steel, and the modulus of elasticity that is one third of the one of steel ([4]). Hence, in case of thin aluminum structures, distortions occur due to a combination of effects related to the high stresses due to high temperatures reached in a small area instantaneously, followed by a rapid cooling. Hence, the reduction of distortions or almost their prediction is a very challenging issue. Even if the prediction of welding distortion in assembled structures has been extensively tackled in the literature since 1960 (e.g. [5]-[11]), no definitive technique has been found to fix the problem yet. This happens because the process must be modelized taking into account the many aspects of the welding process, in terms of material microstructure behaviour, thermal source modeling, process parameters management, optimal welding sequence, and optimal fixturing system design. In this complex environment, multiple criteria and objectives have to be considered concurrently: Most of them are correlated while other are conflicting (e.g. the speed of process and the quality of the welded beam). Moreover, a perspective of efficiency optimization has to be achieved in an integrated design and production context. As a recent example Favi and Campi [12]) coined the term “design for welding” (DFW) defining a method to formalize welding knowledge to be reused as a base for the development of an engineering design platform, applying design for assembly method to assure product manufacturability and welding operations.

1.2. Related literature review

According to the Computational Welding Mechanics (CWM) literature, among the several numerical methods used for mathematically modelling the behaviour of the structure during the welding and the cooling phases in terms of stresses and distortions, the thermal-elastic plastic (TEP) method and the inherent strain (IS) (local- global) based method are used in Finite element analyses (FEA) simulations. In particular, the TEP model is used to replicate the behaviour of the residual stresses and deformations, generated in the cooling phase. First, a transient thermal analysis is carried out, replicating both welding and cooling phases. Hence, an elastic analysis is implemented, with the resulting thermal energy distribution as the initial load condition for an elastic analysis yielding distortions. Despite a very high computational effort, the TEP model provides results that can be superimposed on the experimental data ([13], [14]). In the inherent deformation method, the residual deformations resulting from the cooling phase are taken as the initial load condition for an elastic analysis to find out distortions. For what concerns the evaluation of the FEA model for reproducing the welding process, several works are related to the evaluation of the final distortion of both aluminum and steel plates thicker than 2mm, with the IS based methods ([15]-[22]). On the one hand, IS-based methods are intended to speed up the prediction of distortions in industrial applications (e.g. [15], [20], [22]), or they have been adopted to fulfil the tight tolerances required in the automotive field ([23]). On the other hand, handling complex geometry using full TEP simulation requires high computational efforts ([24], [25]).

More recent works ([26]) propose a novel inverse analysis FEA based method to obtain the inherent deformations on a thin aluminum welded joint, overcoming the problem of high computational efforts requested in this case, by means of an improved method based on the cutting technique, providing efficient results. In [27], the effect of external constraints in welding deformation of thin panel welding structures is showed. A first TEP analysis is carried out and then an elastic FEA based on the inherent strain method is carried out, taking the results obtained from the TEP analysis as initial conditions. Results related to a thin plate aluminum T-joint show that external constraints have a little influence on the tendon force (i.e. the longitudinal shrinkage) a moderate influence on the transverse shrinkage and a high effect on the angular distortion.

FEA models correlated to the use of shell elements have been handled to reduce computational times. Hirohata et al ([28]), proposed a simplified FEA for the simulation of the fillet welding process in steel structural members. A shell elements-based FEA model was compared to a 3D elements FEA, providing accurate results. More accurate results have been gained by replicating a local 3D FEA model in the zone nearby the HAZ and a 2D shell model on the rest of the structure. Nevertheless, this approach is not so efficient due to non-negligible errors in the 3D-2D interface ( [29]-[31]).

Reducing distortions due to the welding process helps increasing the quality of final product, fulfilling tight tolerances on the dimensions and shape of the final assembly of the chassis ([32], [33]). A numerical method for simulating distortions on welded chassis is the local/global method ([24], [29], [32]). Especially in large structures, this method allows to reduce the computational effort with respect to an entire TEP model. This is because the TEP analysis is carried out on a portion of the entire model called the local model. Hence, the residual stresses of the local model are used as initial conditions for the global model, to evaluate distortions by means of a static analysis. This approach has been addressed in the literature only on simple automotive components, as extruded parts or plates (e.g. [25], [30], [32]). Conversely, in the proposed work, as a novelty, the local-global method FEA simulation is applied on an entire four-elements aluminum car chassis, to be assembled by means of the gas metal arc welding (GMAW) process. Moreover, 3D finite elements are used for both the local and the global models, to increase efficiency while overcoming accuracy problems related to 2D shell global models. Hence, the final aim of this paper is to propose an integrated design and process simulation method for increasing quality of chassis in top class cars, in order to approach the strict requirements of automotive standards, by predicting distortions due to welding even in the earliest design stages, with acceptable accuracy and a reduced computational effort. The design method is described in three steps; results are provided with a comparison to standard specifications for the automotive industry. Discussions on advantages and limitations of this work are proposed. Conclusions end the paper.

The proposed integrated design and process simulation method has the practical aim of predicting the final distortion of a complex structure assembled by welding in the automotive chassis design. The method is based on the local/global approach and is made of three steps, as depicted in Fig. 1 and explained in the following. As input data, the designer provides the CAD model which is processed into the welding environment. Other input data regard the experimental data on material, welding process parameters, Goldak parameters ([34]), boundary and initial conditions for process modelling, as well as the automotive industry quality standards, that are allowable distortions given in reference points locations (i.e. allowable tolerance displacements in reference points). The core part of the weld design method has three steps, namely: the local step, the global step, and the quality check step. In the following section a detailed description of these steps is reported.

2.1. Step 1: the local step.

In step 1, the local step, a local model is built for the entire joint to be welded or a part of it, depending on the size of the problem. The local model is used to carry out the TEP analysis. In the TEP analysis, thermal and mechanical simulations are carried out in sequence. First, the transient temperature field is defined by the welding heat transfer model. Hence, the transient and residual deformations due to the cooling phase after welding are found by means of a mechanical model, whose load is the thermal load of the welding process. Temperature dependent material properties are used to simulate the metallurgic phenomena occurring during the changes of phase.

The TEP simulation consists of two sequentially coupled analyses for thermal and mechanical models. The thermal model uses heat transfer elements. The heat flux 𝑄(𝑥’,𝑦’,𝑧’,𝑡) supplied by the moving welding torch is modelled by means of the Goldak model of a double ellipsoidal density distribution ([34]), as usually adopted for the MIG welding simulation ([19], [35]). The Goldak parameters on the welding pool are experimentally found. The welding power input 𝑃=P(𝑡) is calculated as in (1):

$$P=IU\eta$$

where 𝜂 is the welding arc efficiency with respect to the input energy (0.8 for MIG technology), ([36]) 𝐼 is the current and 𝑈 the voltage. Taken the heat source in the axis origin, the heat flux distribution has different parameters for the front and rear quadrants:

$$\left\{\begin{array}{c}Q\left({x}^{\text{'}},{y}^{\text{'}},{z}^{\text{'}},t\right)=\frac{6\sqrt{3}fP}{abc\pi \sqrt{\pi }}{e}^{-3\left[\frac{x{\text{'}}^{2}}{{a}^{2}}+\frac{y{\text{'}}^{2}}{{b}^{2}}+\frac{z{\text{'}}^{2}}{{c}^{2}}\right]}\\ f,c=\left\{\begin{array}{c}{f}_{f},{c}_{f}z\text{'}\ge 0\\ {f}_{r},{c}_{r}z\text{'}<0\end{array}\right.\end{array} \right.$$

where (𝑥′, 𝑦′, 𝑧′) are the local coordinates in the moving reference system associated with the welding torch, 𝑓_𝑓 and 𝑓_𝑟 are the fractions of the power applied onto the front and rear quadrants, assuming 𝑓_𝑓+𝑓_𝑟=2 (as in [34]).

The parameters 𝑎, 𝑏, 𝑐_𝑓 and 𝑐_𝑟 define shape and dimension of the elliptical distributions. Considering the continuity of the volumetric heat source, 𝑓 factors are linked to 𝑐 parameters by 𝑓_𝑓=2𝑐_𝑓/(𝑐_𝑓+𝑐_𝑟) and 𝑓_𝑟=2𝑐_𝑟/(𝑐_𝑓+𝑐_𝑟).

The boundary conditions on the local model must be chosen carefully, as they affect the local plastic strain field, as well as the global results.

2.2. Step 2: the global step.

In the second step, the global model is built, connecting boundary nodes of the local model to the corresponding ones in the global model. Hence, the local model, now integrated in the whole structure, is loaded with the plastic

deformations ε_p computed in the TEP analysis, used as the initial conditions. Hence, an elastic FEA simulation is carried out on the entire structure that is the global model, for evaluating the residual stresses and distortions, according to Eq. 3, in which E is the elasticity matrix, and ε is the total strain vector

${\sigma }={E}\left({\epsilon }–{{\epsilon }}_{{p}}\right)$ (3)

2.3. Step 3 quality check step

In the third step, the quality check is carried out on the entire structure. Resulting distortions are checked in specific locations, called reference points, to lay within dimensional tolerance specifications required by the automotive industry.

Figure 2 The four-members aluminum chassis as a case study for the welding simulation

A 4-members aluminium chassis of a top-class car has been chosen as a case study. Of the four members, two are shell moulded (1, 3 Figure 2) and two are extruded, (2, 4 Figure 2). The fixture system replicates the real assembly asset, as depicted in Figure 3 and digitally reported on the chassis cad model (Figure 4).

2.4. Experimental data

Experiments have been carried out in the OMR (Officine Meccaniche Rezzatesi) Automotive industry in Modena (Italy). More than 50 chassis have been checked for measuring the distortions in specific locations. A robotic system for MIG welding is used for the welding process, using different aluminum alloys for base and filler (EN AW-6082-T6 and S-Al-4043 respectively).

Figure 3 The digital replication of the four-members aluminum chassis with related fixturing system

Welding process parameters have been defined as in Table 1. In Table 2, the Goldak parameters used in the analysis have been derived from experiments ([14]).

In order to check the accuracy of the results of the simulations, the tolerance zones for the displacements of some reference points located on the chassis (Fig. 5) have been reported in Table 3. The values of these tolerance zones are defined from the automotive industry standards and will be compared to resulting simulated distortions on the same points.

Table 1

Welding process parameters used for the experiments
Welding speed v	Current (I)	Voltage (U)	Efficiency (η)	Wire feed (v_wire)	Wire diameter (d_wire)
0.01 m/s	88A	18.8 V	0.8	0.067 m/s	0.0012 m

Table 2

Goldak parameters used for the TEP simulation
Goldak parameters	a	b	c_f	c_r	f_f	f_r
Value (mm)	2.33	2.85	2.55	4.02	0.78	1.22

Table 3

tolerance zones of the displacements in reference points located on the chassis
Reference points		1000	1001	1010	1013	1014	1015	1017	2001	2010	2013	2014	2015
Requested Tolerance zones	Tol_X	± 0,8	± 0,3			± 0,8		± 0,8	± 0,3			± 0,8
	Tol_Y	± 0,3	± 0,3					± 0,3	± 0,3
	Tol_Z	± 0,5	± 0,3	± 0,5	± 1		± 0,5	± 0,5	± 0,3	± 0,5	± 1		± 0,5

2.5. TEP analysis of the local models

A procedure for building the FEA model has been followed. First, a CAD model has been simplified in order to clean up geometries, for better mesh handling. Fillets or holes that have not been loaded and far from the welding beads have been closed or simplified. The local models are a sub-assembly of 4 contiguous joints, in order to increase accuracy on the boundary conditions between the joints. The plastic deformations have been applied on the global model at the same time, without considering the welding sequence.

Two local models have been built, as to carry out a TEP analysis for each local model. The local model is made of 4 overlap joints, modelled together to increase the accuracy of the boundary conditions on the nodes of the joints (Fig. 6). Tetrahedral elements have been used for the FEA models built in the Altair Hypermesh® software. Boundary conditions are applied on each local model, to replicate the actual fixture system.

Hence, the TEP analysis has been carried out on the two local models separately, by using the SimufactWelding® software. The transient heat transfer analysis (welding + cooling phases) is carried out to determine the temperature distribution, used as the initial load condition of a mechanical analysis, yielding distortions. The Goldak model has been used for describing the heat flux distribution, using parameters defined in Table 2. Analyses have been carried out on an Intel(R) Xeon(R) CPU E5-1620 0 @ 3.60GHz, with 8.00 GB RAM.

On the Simufact Welding® software, a selected refinement of the mesh has been performed on the bead elements, as to increase the accuracy. The elements of the weld bead are created during the welding process according to the birth-death method. Given the temperature range as the initial thermal load condition (Fig. 7), the corresponding stress values on the local models are evaluated (Fig. 8).

2.6. Elastic analysis on the global model

For the global model, a tetrahedral mesh is built on Altair Hypermesh® software and imported in ABAQUS 6.13® environment for setting up the elastic analysis. In the joint zones, “tie” constraints are applied as to stitch nodes of the extruded and the moulded parts (Fig. 9). This is aimed to ensure that the corresponding nodes behave in a conforming manner and penetration is avoided. Fixturing system is replicated as to restrain the global model during welding, as depicted in Fig. 10. Residual stress on the welded joints are used as initial conditions for the evaluation of the distortions in the global model (Fig. 11)

Results related to the stresses and displacements on the chassis, after the elastic analysis carried out in the global model, have been depicted in Fig. 12 and Fig. 13.

Results are compared to the experimental ones. In Table 4, simulated values of displacements (U_Xsim, U_Ysim, U_Zsim) are compared to tolerance zone requested by the automotive industry standards (Tol_X, Tol_Y, Tol_Z). Results show that the values of the simulated displacements lay within the tolerance zones.

Table 4

Displacements resulting from the FEA analysis, evaluated in given reference points, are compared to requested tolerance zones
Reference points	1000	1001	1010	1013	1014	1015	1017	2001	2010	2013	2014	2015
U_Xsim	0,093	-0,025	0,037	-0,033	0,113	-0,183	0,054	-0,073	0,018	-0,074	-0,104	0,18
U_Ysim	-0,012	-0,004	0,025	-0,009	-0,031	-0,036	0,013	0,063	0,006	0,012	0,033	0,016
U_Zsim	0,017	0,001	-0,017	0,021	0,256	0,069	-0,018	-0,063	-0,002	0,018	-0,226	-0,064

The model has provided efficient results for practical application in industrial welding of automotive chassis design and production. Quality standards have been achieved, as displacements in specific standard points in the 3D structure lay within the tolerance zone. Nevertheless, some limitations have to be pointed out. The accuracy of the model could be increased by including specific geometric and dimensional tolerances on the geometrical features. Moreover, the quality of the final product could be enhanced by optimizing the welding sequence in order to minimize final distortions on the product. The problem of finding the optimal sequence for welding has been tackled on simpler structures (e.g. [37]) proving the correlation between reduced distortions and optimal welding sequence. Despite, in case of several welding beams and increased geometric complexities, the problem of finding the optimal welding sequence is considered as an NP hard problem which could be tackled by means of metaheuristics as genetic algorithms (e.g. [38]). Hence metaheuristic algorithms could be included in an integrated welding design process since the earliest design phases to achieve optimal structure response in terms of quality standards. Several works in the last two decades have been dedicated to the evaluation of efficient optimization techniques for reducing distortions in welding by controlling process parameters during welding process by means of robotic welding. Robotic welding is nowadays extensively used to increase repeatability and enhance final product quality. In this context, the prediction of bead quality is achieved by means of optimization methods applied to the control of the welding process parameters. Optimization techniques belonging to artificial intelligence are mainly related to hybrid combinations of artificial neural networks and evolutionary algorithms, to enhance production efficiency and product quality. In [39], digital manufacturing has been efficiently applied on welding pressure vessels by means of robotized welding processes. The optimal layout of the workstation, the design of the fixturing system, the definition of the poses of the robot to replicate and comply with good welding practice, the programming of the robot path have been tackled interactively. In [40], neural networks are adopted to predict the out-of-plane welding distortions; [41] used hybrid optimization algorithms to model the welding process. In [42] authors propose a multi-objective teaching learning-based optimization (EMOTLBO) method for welding robot optimal trajectory generation. Pal and Pal ([43]) used Neuro NSGA-II for multi objective optimization of the welding process. Sivasakthivel and Sudhakaran ([44]) used nature instigated optimization algorithm for modelling and optimisation of welding parameters for multiple objectives. In [45], Radial Based Function Neural Network and NSGA-II has been applied for the evaluation of process parameters in gas metal arc welding (GMAW). Hybrid BPNN-PSO approach has been tackled for modelling and optimization of activated gas tungsten arc welding process.

Hence, further work regards the modelization of the welding problem related to the prediction of welding distortions in thin aluminum structures as a NP hard complexity-multiple objective optimization problem (NP-MOP). Moreover, adding a finer control of the process could enhance the product quality. In particular, the process control could be digitally replicated by populating a database with data derived from on-line control of the welding parameters. Finally the analysis of geometrical tolerances on the final shape of the welded chassis with respect to reference points, could provide further interesting results on shape optimization.

Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Conflict of interest The authors declare no competing interests.
Availability of data and material Data will be available upon request
Code availability software application
Ethical approval Not applicable.
Consent to participate Not applicable.
Consent to publish Not applicable.
Author contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cristina Renzi and Francesco Leali. The first draft of the manuscript was written by Cristina Renzi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Design for welding: an application in top class cars chassis

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Abstract

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1. Introduction

1.1. Background

1.2. Related literature review

2. Method

2.1. Step 1: the local step.

2.2. Step 2: the global step.

2.3. Step 3 quality check step

2.4. Experimental data

2.5. TEP analysis of the local models

2.6. Elastic analysis on the global model

3. Discussions And Conclusions

Declarations

References

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