This paper describes a novel end-to-end approach for automatic welding-robot programming based on a product-process-resource (PPR) model, for one-of-a-kind manufacturing systems. For welding-robot programming to be possible, in a one-of-a-kind manufacturing system, a large range of information about the product is required e.g., customer requirements, product design features, manufacturing process parameters and resource capability. Traditionally, this information is processed and transferred along the manufacturing organisation’s value chain by using several stand-alone digital systems which require extensive human input and high skill to operate. A PPR model is proposed through this research as a platform for storing and processing the necessary information along the value chain seamlessly. This is achieved through the digital integration of CAD software tools compliant with the ISO 2553:2019 standard, and offline programming systems (OLP) for welding-robots. The PPR model enables the automatic programming of welding robots and drastically reduces human input. The applicability in manufacturing of the theoretical concept is demonstrated through technical implementations tested in laboratory and on the value chain of an engineering-to-order (ETO) industrial partner involved in the metal fabrication industry. The experiments were conducted by creating several products using the proposed artefact. Experiments show that automatic programming of welding robots can be achieved using PPR models, drastically reduce the human input necessary to obtain a welding-robot program, and drastically reduce the ETO product’s lead time.
Research Article
Automatic Welding-Robot Programming Based on PPR Models
https://doi.org/10.21203/rs.3.rs-3346143/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 25 Mar, 2024
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Welding-robots
Automatic robot programming
Product Model
End-to-end integration
In this paper, the theoretical concept of a product-process-resource (PPR) model for automatic welding-robot programming is proposed and then its potential to be adopted by practitioners is tested through practical experiments in laboratory and at a partner manufacturing company. This concept lays the basis of organising the PPR [1] data required to program a welding robot in a centralised manner which facilitates the fast and automatic programming of welding robots proving their high utilisation rate in high-mix low-volume production environments to be feasible. Traditionally, the computer aided design (CAD) environment is disconnected from the offline programming (OLP) environment for welding robots. This discontinuity within the digital value chain of the company, requires redundant human input to re-digitalize data which is often already present in CAD models but lost upon transfer in the OLP environment. The proposed PPR model enables the seamless integration of the multiple operations necessary to be conducted before obtaining a functional robot welding program, by making use of the CAD system’s compliance with the ISO 2553 standard. Thus, an end-to-end digital pipeline can be put in place to automatically program welding robots without the need of highly skilled labour, resulting in lower production cost and dramatically faster lead time, without affecting the flexibility associated with a one-of-a-kind manufacturing system.
Enterprises located in high-cost environments, e.g., Denmark, face the challenge of an ever-increasing competition from low-cost environments [2]. Furthermore, the Danish metal fabrication industry is facing a shortage of skilled welders [3]. This shortage compels enterprises in high-cost environments to rely on automation, which is highly challenging in one-of-a-kind manufacturing setups. With every product being unique, companies with one-of-a-kind manufacturing systems must invest heavily in engineering resources to create new product designs and to reprogram the welding robots every time a new order is received. Reprogramming of welding robots is a process involving advanced competencies and time resources. Bejlegaard et al. and Tolio et al. show however in their studies that digital technologies tailored to fit the needs of the ETO small-medium-enterprises (SME) can reduce many of the trade-offs which concern these companies, such as flexibility versus efficiency [4, 5]. The artefact which is described in section 4 of this paper is meant to drastically reduce the time and the competencies level required to reprogram welding robots, while at the same time facilitating lower production cost, faster lead time and increased utilization of welding robots when compared with traditional, manual programming methods.
To program welding robots, a thorough understanding of the product, the manufacturing process and the manufacturing system’s capabilities is required. In ETO value chains, engineering resources are used to either create new products based on customer’s requirements or adapt existing product designs to satisfy the customer’s demand. A literature study was conducted on existing literature in the domain of product-process-resource models and how product-process-resource data can be structured in such manner that it allows fast re-design based on the customer’s requirements and automatic programming of welding robots. The findings in the fields of PPR models and methods for automatic programming of welding robots are presented in the following.
2.1 PPR models
Integrating the customer’s requirements into the product design is a matter of high importance when it comes to ETO supply chains and this can put extraordinary strain on the delivery dependability of products, especially for highly individualized products, where the customer is involved in the product design and engineering process [6]. The scalability and the personalisation of the product’s architecture in the most efficient manner possible is a topic investigated extensively in literature [6–8]. Tan et al. propose an open product architecture design, which makes use of several optimisation criteria to come up with the best solution to satisfy both the customer’s requirements and the manufacturing company’s targets for production cost. By using genetic algorithm solvers, the optimal attribute selection for the product is provided [8]. Customers can interact with the product architecture through product configuration systems, which facilitates customer-centric product development. By using the open architecture product platform conceptualized by Zheng et al., customers can either choose a configure-to-order approach on their order, or an engineer-to-order approach to have access to the full suite of engineering services provided by the company [6]. A similar approach is investigated by Bejlegaard et al. where the case-company provides emerging customers access to a focused product portfolio, in a made-to-order regime, while frequent customers are given access to innovative products in an ETO regime [4].
To program welding robots, information is required concerning the product design, the process and the resources needed to perform the weldments [9]. The systematic overview on product-process-resource information is also known as the “PPR perspective” and it is formalised in existing literature through PPR models [10–12]. PPR models include in their scope details about the resource capability, or skill required to conduct the manufacturing processes necessary to product an item. Brecher et al. and Ferrer et al. use PPR models to particularly formalize the process required and the skills needed to sustain assembly operations. The information required to set up the assembly process and the resource dimensions are extracted from the CAD through means of ontology-tailored meta models or semantic web rule language [13, 14]. A similar approach is taken by Ahmed et al. by using a PPR model in their study-case for automotive engine assembly lines. The PPR model is queried for information in two stages, first to determine the capabilities of the available resources given the product requirements, and then to align the resource capabilities with the proper assembly process parameters [1]. As Ahmed et al. point out, the PPR model has the potential to increase engineering productivity and bring together information and knowledge under a single knowledge base. The same view on the PPR concept in shared by Winkler et al. who use PPR to trace the product requirements along the value chain and facilitate the propagation of information across the product-process and resource domains which form an engineering asset network [15]. The implementation of PPR view actively contribute at the increase of the adaptability of the production systems and allow for faster new product insertion with reduced human intervention [16, 17].
While the work mentioned above showcases the benefits of using the PPR perspective on assembly lines, no substantial literature and empirical research was found on how PPR models can be used for welding applications, specifically for automatic programming of welding robots in one-of-a-kind manufacturing systems. Therefore, the approach presented in this paper aims towards defining a PPR model, using the PPR perspective to enable automatic programming of welding robots and fast redesign of products, all within an end-to-end integrated information flow. The environment in which product data can be manipulated by the product engineers is the CAD software tool. A deep dive into existing methods on how automatic programming of welding robots can be obtained with the data existing in CAD models is given in the following.
2.2 CAD-based automatic programming of welding robots
Welding is one of the most exploited applications for industrial robotics [18] and the programming of welding robots is a well-researched field that has been in the attention of both researchers and practitioners for several decades. Current research in this field is focused on the reduction of time, skills and overall costs involved in programming and deploying a welding-robot program. This is especially important for one-of-a-kind production setups where welding robots must be reprogrammed every time a new customer order is received. There are three major approaches towards automatic programming of welding robots [19]: CAD-based programming, sensor-based programming, and hybrid programming.
As mentioned in the previous subsection, the main environment where product data is manipulated is the CAD model. Therefore, the focus of the manuscript is placed on CAD-based programming methods only. The computer-aided-design (CAD) system lays at the core of the engineering to order (ETO) supply chains facilitating the creation and the transformation of the product’s design by design engineers [20]. Methods for automatic programming of welding robots using CAD models are at the core of intelligent weldment systems (IWS) [21]. The CAD model is needed within offline programming systems (OLP) for collision detection and avoidance algorithms [22–26] which allow automatic robot programming (ARP) [27, 28]. However, this is only possible if information about the locations and the geometry of the weldments exists. Traditionally, this information is manually inserted by the robotics engineers using various human machine interfaces (HMI) either readily available in the OLP system or custom made to make the programming process as intuitive as possible and independent of programming languages [29]. The information that is manually inserted in the OLP is gathered from the technical drafts which are automatically generated by modern CAD systems under the form of 2D technical drawings annotated with the positions of the weldments as stipulated by ISO Standard 2553:2019.
Recent developments in the field of ARP for welding applications have been focused on the automation of weldment related information transfer from the CAD environment directly to the OLP environment. Feature recognition algorithms are used to process the CAD model stored in STEP (Standard for the Exchange of Product Data ISO 10303) files [30]. Xuan and Ngoc make use of the hierarchical structure of the STEP format to access the set of faces and edges which make up the geometry of the product designed in CAD [31]. The relationships between the faces and the edges are evaluated and the coordinates of the weldments are determined. Kuss et al. exploit a similar approach augmented by rules, which delimit the recognition procedure to fillet weldments only [32]. By using the surface normal of the extracted faces, Kuss et al. can determine the angle between the surfaces which are to be welded. Fang et al. also makes use of data extracted from the CAD model to discretise the weldment by using methods available in the SolidWorks CAD system [33].
Modern CAD systems allow product design engineers to model the geometry of the product, perform engineering analysis, review the design, and finally create the technical drafts of the product. The technical draft contains 2D drawings annotated with the positions of the weldments [34]. Weldments are considered rigid engineering connections in CAD assemblies and by using modern CAD tools, weldment information can be overlaid on CAD models as directed by ISO Standard 2553:2019 [35–39]. This is also supported so some extent in the second edition of STEP AP242 and an implementation for extracting information about the weldments directly from the CAD model without feature recognition algorithms is given by Mohammed et al. [40]. Mohammed et al. show that process information like current, voltage and torch speed can be additionally overlaid on the STEP file format by intervening directly on the extensible markup language (XML) structure of the STEP file.
To obtain reliable information for ARP purposes, Tran et al. use a composed implementation using information obtained both from STEP files with ISO Standard 2553:2019 annotations, and feature recognition tools [41]. The annotations present in the STEP files are created by Tran et al. using the tools available in the Siemens NX CAD system [39]. The weldment related information obtained by Tran et al. can be further used to automatically generate a robot program with collision free movements between and across the weldments [42].
Larkin et al. classify the methods for extracting weldment information from CAD models using the CAD tools available, as either automatic, using the feature recognition algorithms, or semi-automatic, by using manual inputs to create the necessary annotations on the CAD model which define weldments [43]. It is to be noted, however, that weldment annotations fall under the ISO Standard 2553:2019, which stipulates the proper drafting of products with rigid engineering connection held together by weldments. And as mentioned before, there are several CAD systems commercially available which are compliant with this standard. Therefore, the proper annotation of weldments falls under the responsibility of the product design engineer. Moreover, as shown in the related work presented above, information encapsulated in both the product and process domain can be stored in modern CAD file formats. While research similar with the one made by Tran et al[41, 42] and by Mohammed et al.[40] offer examples on how data can be extracted directly from CAD models to program welding robots, a research gap is identified in the lack of a general approach for extracting relevant information from CAD systems compliant with the ISO 2553:2019 standard. The approach presented in this paper and the proposed theoretical concept addresses CAD software compliant with the ISO 2553:2019 standard independent of the supplier.
It is concluded from the literature study made that the ETO case-company may benefit from a technical solution which allows fast adjustment of product designs without the need for an iterative process, ideally where the customer can have more control over the product’s design, but without the assistance of the engineers. Although the company is using CAD software and OLP software, the data exchange between these two environments is made manually and is disconnected. The digital integration of CAD and OLP is expected that the information flow will become streamlined, and the lead time will be reduced.
By linking the findings in the literature survey on the PPR perspective and automatic programming of welding robots based on CAD models, a PPR model for automatic programming of welding robots is hereby proposed. The product design engineers use CAD tools to document information about the product beyond the geometry of the product. The information can be overlaid on CAD models using modern CAD tools, can be used to set up the production processes and ensure that the process can be conducted using the available resources.
3.1 Theoretical foundations
Ahmad et al. offer a decomposition of the PPR model [1] used for automatic assembly applications as follows:
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The product domain contains all the available information about the product’s characteristics, the product’s family and variations. The product domain is often fully encompassed in the CAD model of the product. The product can be an assembly of multiple components which share various engineering connections or liaisons (fixed, cylindrical, prismatic or rigid).
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The process domain encapsulates the knowledge and information necessary to set up the operations required to manufacture the product. As described by Ahmad et al.[1], a production operation is realised through several processes which are sustained using resources. Some processes require certain skills and resources to be conducted.
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The resource domain concerns the set of physical and non-physical entities involved in supporting the production process. The physical resources may be manufacturing tools and various consumables, while non-physical resources can be the tacit or documented knowledge regarding how the production process can be set up.
The product model concept proposed is shown in the diagram displayed in Fig. 1. The Unified Modelling Language (UML) was selected to describe the PPR model proposed. UML provides a general and flexible modelling language using specific graphical elements which are widely understood in the software development community [44]. UML is fit for the development of a theoretical concept that has its foundation in the functionalities of existing CAD software tools. The general nature of the language allows for applicability across the whole domain of CAD software compliant with the ISO 2553:2019 standard. The flexible nature of UML allows the presented model to be adapted further, as the CAD tools will evolve in the future, or even modified to be compatible with custom-made or open-source CAD software which are not compliant with ISO 2553:2019 but have available tools for weldment annotations.
The major suppliers of CAD systems are extending the capabilities of the tools they supply, to encapsulate more product data [1]. The implementation of rule-based design methods enables the digitalisation of the product knowledge [45] and parametric CAD modelling which enables the reusability, the adaptability, and the configurability of a CAD model [46], making the product engineering stage faster and more cost-effective. These capabilities are made use of to build parametric rules and product configurations in the proposed PPR model. The changes in the product domain directly influence the way the manufacturing process is set up and the way the available resources can sustain the manufacturing process [1].
The classification of input parameters for programming of welding robot given by Lauridsen [9] is used to formalize the information contained and processed by the proposed PPR model:
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Design parameters: the product parameters which are related to the workpiece to be welded. These parameters include the geometry of the workpiece, the thickness of the plates joined through welding and the material specification. These parameters are varied by varying the design of the product, as requested by the customer.
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Motion parameters, the process parameters which are specific to the manipulator of the welding torch (welding robot). The motion parameters include the position and the orientation of the torch relative to the workpiece or welding seam which influence the length of the exposed wire at the tip of the welding torch (stick-out), the velocity of the torch and the weaving of the torch. Depending on the design parameters, the motion parameters may include certain offsets such as multi-pass offsets which allow an incremental increase of the distance between the torch and the work piece as the weld bead increases in thickness.
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Arc parameters, the process parameters which are specific to the welding apparatus and are independent of the overall geometrical design of the product but are used to fulfil the quality requirements of the weldment, e.g., weldment throat size. These parameters are the arc current and voltage. Modern welding apparatus allows for automatic control of wire feed rate and shielding gas flow depending on the preset material specification and joining plate thicknesses. Other parameters which are important before the welding process can commence and which directly influences the arc parameters are the shielding gas and the filler wire material.
3.2 Proposed PPR Model for automatic programming of welding-robots
In the proposed PPR model, represented in Fig. 1, the PPR perspective is made apparent by using different colours to represent the classes which constitute the product, process and resource related elements. The classes represented with dark and light orange are related to the product component of the model. The green coloured class diagrams are related to the process component of the model, and the purple coloured class diagram is related to the resource part of the model. The main contribution brought to the ARP field is the process related component of the model.
Product:
The product element of the proposed PPR model has its roots in the generic representation of a product in a CAD system. As observed in Fig. 1, a CAD component class is composed of one or more Surface instances. A Surface class can be composed of one or more Edge instances. An Edge class is constituted of several tessellation points which can be accessed through the GetTessellationPoints() accessor. Depending on the shape of the Edge object, it can have two (straight edge) or more tessellation points (curved edge). The Surface class contains the normal vector associated with a surface, or several normal vectors, if the surface is curved. The coordinates of the tessellation points and the components of the normal vectors are expressed in relation to the origin frame of the CADComponent instance. In the context of this paper, the CADComponent is a metal plate that must be welded, the attributes the CADComponent class has are the shape parameters, the material (metallic alloy) of the component, the thickness of the plate, and the transformation associated with the origin of the product when it composes a CADAssembly.
Multiple CADComponent instances compose the CADAssembly class. As the assembly in the context of this paper is going to be welded, all the components which compose it share the same material. The Origin attribute of the assembly can be custom set up to match the position and the orientation of the product when attached to a fixture for welding, in the physical setup. As observed in Fig. 1, the CADAssembly class can be composed of one or more CADWeldment instances. The CADWeldment class can only be instantiated if it is associated with an Edge object and with two Surface objects. We consider the weldment to be attached to an edge, between a base surface and a joining surface. The base surface is the surface which contains the weldment seam, while a joining surface is bordered by the welding seam, as represented in Fig. 2.
A CAD model can be driven by several ParametricRule objects instantiated by the product design engineers. These rules compose a ProductConfiguration object. This facilitates the accessible configuration of a product model by inserting the design parameters through the interface realized by the ConfigurationWizard. A ParametricRule contains a mathematical expression which drives various elements inside the model. For example, depending on the thickness of the plates, the throat size of the weldment can be automatically adjusted based on a mathematical rule set by the design engineer, based on specifications. Another example can be a ParametricRule object attached to a joining Surface object and a base Surface object that controls the length of the two surfaces in the same direction can directly drive the length of the intersection Edge and therefore the length of the Weldment object attached to that Edge. This simplifies the design process from having to modify two parameters to having to modify only one.
The interface towards the CADAssembly object is realized by the ProductModel class. The ProductModel interface can be implemented by using the API (application programming interface) available with the CAD system. The interface clearly specifies the methods which must be made available through the API, for the CAD system to correctly adhere to the proposed PPR model [44]. Through the interface, accessors are made available for the design parameters of the weldment.
Process
The process component of the product model is the main contribution of this work as part of the proposed PPR model for automatic programming of welding-robots. The process model is constituted of a WeldmentExtractionTool class, which acts as a bridge between the CAD environment and the OLP environment, by using the available interfaces implemented for these systems. The WeldmentExtractionTool directly connects to the ProductModel interface and makes use of the available accessors to extract the design parameters of the weldments. The WeldmentExtractionTool, uses a ProcessParameters data base, which contains the digitalized tacit knowledge of the engineers under the form of a look-up table. For example, depending on the designed throat size of the weldment and the plate thickness on which the weldment is attached, a certain set of process parameters (motion parameters and arc parameters) are used for the welding process. Some of the motion parameters contained in the data base which are dependent on the design parameters are: the welding angle α and the travel angle β, which are represented in Fig. 2, the torch travel velocity, and the stickout, which is the distance between the welding torch and the welding seam. The arc parameters can be the voltage and the current for the weldment. Some process parameters, like the wire feed and the gas flow may either be present in this data base or be controlled by the welding apparatus.
The WeldingProgram class is instantiated by the WeldmentExtractionTool class upon call of the ComputeTorchPositionParameters() method. The WeldingProgram is composed of one or more WeldmentOperation objects which share the same process setup parameters e.g., the materials of the consumables, the welding process type (MIG, TIG etc.), and the origin frame in which the geometry of the weldments is expressed. These parameters remain the constant across the whole welding process. A WeldmentOperation object is discretised in one or more DiscreteWeldmentOperation objects, as described by Sarivan et al. [48]. Each discrete weldment operation which composes a WeldmentOperation object shares the same process parameters e.g., the torch travel speed, the wire stickout and so on (the attributes of the WeldmentOperation class). Every DiscreteWeldmentOperation object is instantiated with its own torch position parameters.
After the WeldingProgram is instantiated, collision free trajectories must be generated for the welding torch in order for the welding process to be successfully supported. This is made possible through the OfflineProgrammingSystem interface. The interface is made available by using the OLP’s API.
Resource
The resource component in the proposed PPR model is represented through the OfflineProgrammingSystem interface which offers access to the capabilities of the offline programming system which has available in it a digital twin of the robot welding cell. These capabilities are the motion solver of the robot, which makes available the possible configurations in which a robotic manipulator can reach a point, and the collision detection functionality. By using these capabilities, it is ensured that the available resources on the manufacturing system can successfully support the welding process. The algorithms used to setup the welding process and the collision free trajectories for the welding program are given in the next section.
3.3 Automatic programming based on product model:
To facilitate the automatic programming of a welding robot based on a PPR model, the following prerequisites are necessary:
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The creation of a CAD model with weldments annotations as stipulated by ISO 2553:2019 standard by the product design engineers (represented by the ProductModel class in Fig. 1).
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The creation of a configuration through parametric rules which will be used to easily modify the CAD model without skilled intervention (represented by the ProductModelConfiguration class, in Fig. 1).
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The digitalization of the process parameters (motion and arc parameters) under the form of look up tables depending on the design parameters of the product which will be organized in the ProcessParameters data base.
With the prerequisites in place, the following steps must be followed to automatically program a welding-robot using the proposed PPR model:
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The design parameters are input to the ConfigurationWizard interface by the sales managers or the design engineers. A new instance of the CADAssembly class is created by using the ParametricRule objects which compose the configuration created by the product design engineers.
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The composing elements of the CADAssembly instance are driven by the rules, including the weldment information, constituting as such the Design Parameters which are used to program the welding robot.
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The geometry of the weldments is extracted from the CADAssembly instance through the ProductModel interface.
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The geometry of the weldments is discretised and for each discrete state, motion and arc parameters are computed through the WeldmentExtractionTool class.
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Collision free trajectories for the robot are automatically computed using the functions available in the OLP and accessed through the OfflineProgrammingSystem interface.
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The program is saved and ready to be validated through simulations and then deployed on the welding-robot.
Algorithms are provided for steps 3–5 of the automatic welding-robot programming process. Each algorithm is associated with the methods available in the WeldmentExtractionTool class. A description of how the weldment geometry can be extracted from the product model is provided in Algorithm 1.
Once the ProductModel instance is created, it is possible to query the list of weldments using the GetWeldments() method. The method is accessed by first creating a handler towards the CAD system. For each weldment, the Edge to which the weldment is attached is extracted, together with the joining and base surfaces. The number of Edge instances attached to a weldment must be the same as the number of joining Surface instances. The tessellation points defining the curve underlaying the Edge instance are extracted. The tessellation points are important for the discretisation of the weldment into prismatic quasi-stationary elements [48] and are part of the weldment definition. If the underlaying curve is curved and not straight, for each tessellation point, the coordinates are saved and the normal vector components of each joining Surface and base Surface is saved. The normal vector components are saved in order to determine the position and the orientation of the torch relative to the weldment seam. In case the underlaying curve of the Edge instance is straight, there is only one set of normal vectors of each Surface (joining and base). It is to be mentioned that there can exist only one base Surface instance, while it is allowed to have multiple joining Surface instances for any single Weldment instance.
To compute the motion parameters for the welding process, namely the torch position and orientation for each discrete weldment point afferent to each extracted tessellation point, the ComputeTorchPositionParameters(ProcessParameters) method is called. The method makes use of the ProcessParameters database to properly set the orientation of the welding torch relative to the weldment bead. The steps used by the method is presented in Algorithm 2. Horn’s Absolute Orientation Method [48, 49] is used in Algorithm 2 when the ComputeAbsoluteOrientation method is called to change the reference frame in which the weldments’ geometry is expressed.
The weldments geometry extracted in step 3 are used as input for the algorithm 2. For each weldment definition, the coordinates of each tessellation point and the afferent normal vectors are extracted from each column of the matrix M. This happens as described in step 4 and each discrete state is associated with a tessellation point. The first three entries of the column represent the position of the tessellation point, the next three entries represent the normal vector’s components of the base surface, the next three components represent the normal vector’s components of the joining surface, and the last entry is the dimension of the weldment’s throat in millimetres. The dimension of the weldment’s throat is used as look-up parameter to extract the associated sickout S (distance between welding torch and weldment seam), welding angle of the torch α (angle between torch and the base surface), and travel angle β (found between the torch and the weld seam) using the lookup table in the Process Parameters data base. The α and β angles are also represented in Fig. 2. By using the coordinates of each tessellation point and its afferent normal vectors, discrete weldment data is generated by using Horn’s Absolute Orientation method [48, 49]. For each torch position determined using Horn’s method, the S, α and β parameters are set and the necessary transformation is computed from the origin frame of the CAD model to the position of the weldment torch afferent to the respective discrete weldment point. The result of Algorithm 2 can be observed in Fig. 3.
With the torch position parameters computed, Algorithm 3 is used to generate collision free trajectories for the welding-robot. This is associated with step 5 in the proposed automatic programming of welding robots’ procedure. Algorithm 3 takes as input all the torch position parameters computed in Step 4. A handler towards the offline programming software is created in order to access the kinematics of the welding-robot and test the reachability of the target point and to access the collision detection functionality of the OLP [22–26]. For each discrete point which composes each weldment, the end effector (the welding torch’s tip) of the virtual welding-robot is commanded to reach the target point. While collisions are detected, the torch is twisted around the axis of the welding wire both ways until a configuration is reached which is free of collisions. Algorithm 3 is offered as alternative in case the collision detection and automatic collision solver algorithms are not available in the OLP of choice, however many of the modern OLPs do have such methods available [27, 28].
3.4 Reflections on the proposed system:
This section has provided a thorough description of the proposed PPR model concept for automatic programming of welding robots which facilitates automatic programming of welding-robots without the intervention of robotics engineers in defining the weldment paths for the welding-robots. The concept was developed based on findings and formalisations from literature, as presented in subsection 2.1 to achieve a PPR integration addressed to welding applications. The PPR model is composed of a collection of data contained in a CAD system compliant with the ISO 2553:2019 standard and process data that can be digitalized in the ProcessParameters database specific to the company where the system is implements. The proposed PPR model summarized in Fig. 1 extends the scope of extant research, which is applied on a specific CAD system, as shown in subsection 2.2 [41, 42]. Algorithms were provided on how the data is processed to achieve a program for welding-robots in an automatic manner. The proposed system is meant to drastically reduce manual input required to program weldment locations in the OLP.
In order to prove the feasibility of the proposed PPR model, a technical implementation was put together using tools commercially available which are widely used across industry. The elements of the technical implementation are put together as showcased in Fig. 4. For the creation of the product family design to be configured, the SolidWorks CAD software was selected due to availability for the researcher and its compliance with ISO 2553:2019 standard. The CAD assembly created in SolidWorks serves as part of the Product component of the PPR model and it can be accessed through the ProductModel interface, as presented in Fig. 1. The interface is created as showed in Fig. 4 by using the SolidWorks API. The ProductModelConfiguration is created using the DriveWorks add-on for SolidWorks. DriveWorks was selected as it allows the creation of complex ParametricRule instances which can drive both the CAD models and the ISO 2553:2019 standard compliant annotations of the weldments. The ProductModelConfiguration is created by the design engineers. Upon instantiation of the Product Model through the graphical user interface implemented in DriveWorks, the CADWeldment information can be extracted using the Weldment Extraction Tool interface annotated with the robot arm in the diagram presented in Fig. 4, and by using the ProcessParameters database populated by the welding and robotics engineers in a Microsoft Excel document which serves as a lookup table. The Weldment Extraction Tool uses the SolidWorks API in order to extract the necessary information to automatically program the welding robot. The result of the extraction is the .STEP file which can be imported in the OLP and files containing information about the weldments and process parameters. By using Algorithm 3, automatic programming of the welding robot is achieved in the RoboDK OLP software. A special tool called Oqton is used to facilitate the automatic programming of welding robots for the DTPS software supplied by Valk Welding (technology supplier) [28].
There are two approaches towards the automatic programming of the welding robot:
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Using the RoboDK software, which makes use of the algorithm 3 described in section 4, by connecting to the RoboDK API and importing the CAD model in STEP file format and the Welding Program containing weldment information and process parameters.
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By creating special CSR files which are programs that can be imported in the Desktop Programming Software developed by Panasonic and supplied by Valk Welding. The programming of the welding robot is fully automized using the Oqton add-on supplied by Valk Welding [28].
Experiments were conducted with the technical implementations in the welding laboratory at Aalborg University and on the production line at the case-company to test the hypothesis that welding robots can be programmed automatically based on the PPR model with minimum human input necessary. Therefore, two experimental pipelines and two experimental setups are used with the available hardware and software in the two environments. The technical implementation using the RoboDK OLP is used in the laboratory at Aalborg University and the technical implementation using the DTPS OLP is used on the production line at the case company.
5.1 Product design for experiments
An experimental product was designed in collaboration with the case company which will cover in its design the wide range of geometrical elements encountered in the company’s real products (observed in Figure 5 and Figure 3). The geometrical elements are circle arches of various dimensions with various orientations. The weldments which are tested are fillet weldments. Weldment annotations compliant with the ISO 2553:2019 standard, are added using the “Weld Bead” tool available in the SolidWorks CAD software and are visible as black and grey bead like elements overlaid on the CAD model [37]. The CAD model in Figure 5 together with the overlaid annotations constitute a CADAssembly instance, accessible through the ProductModel interface as explained in the architecture presented in subsection 3.2.
By using the DriveWorks add-on available in SolidWorks, a ProductModelConfiguration was put together by the design engineers which will drive the Product component of the PPR model. The ProductModelConfiguration for the experimental case receives a total of 8 parameters driving the dimensions of various features of the item showed in Table 1.
The design parameters serve as input for a Configuration Wizard implemented in DriveWorks and are displayed in Figure 6. Each design parameter drives one or more parametric rules attached to various elements of the Product. The process of creating a new product variant takes only a few minutes to complete. For testing the technical implementation presented in the previous section, three products are created using the Product Model Configurator and then fabricated by the case company for running the experiments both on their production line an in the controlled environment at Aalborg University. The design input for each of the test products is shown in Table 1.
The case company was provided with the technical drawings, and the components of the item were fabricated, and tack welded together, being ready to be completely welded by the welding-robots. The material used for all the items used in experiments is SSAB 335MC. In order to weld this material, based on the recommendations received from the case-company, G3SI1 compliant filler wire with 1.2mm diameter is used, together with a shielding gas mixture composed of 82% Argon and 18% Carbon Dioxide.
Table 1: Input design parameters of the instantiation of three separate Product Model instances. The values are expressed in millimetres.
|
|
Base Length |
Base Width |
Plates Thickness |
Attachment Height |
Arch Ray |
Segment Length |
Attachment Width |
Attachment End Size |
|
Item 1 |
400 |
250 |
10 |
80 |
50 |
180 |
200 |
90 |
|
Item 2 |
400 |
250 |
10 |
80 |
65 |
150 |
220 |
110 |
|
Item 3 |
450 |
300 |
10 |
80 |
50 |
160 |
270 |
120 |
The arc and motion parameters which populate the ProcessParameters database were also agreed upon together with the case company given the plate thickness used in the items to be welded. The parameters which are used in the experiments are displayed in Table 2. The parameters for fillet weldments between plates of 10mm and 12mm are given, however, the full database contains parameters for plates between 8mm and 26mm. The scope of the experiments presented in this paper does not span beyond fillet weldments of plates with 10mm thickness.
Table 2: Lookup table which representes the Process Parameters database. The database is built using the tacit knowledge of the robotics and welding engineers from the case-company. Same kind of look-up table is implemented in a database as showcased in Figure 4
|
Plate thickness |
Torch Velocity |
Stick-out |
Welding Angle |
Travel Angle |
Amperage |
Voltage |
|
10mm |
0.007m/s |
18mm |
45° |
0° |
290A |
28.2V |
|
12mm |
0.006m/s |
18mm |
45° |
0° |
303A |
28.9V |
5.2 Experiment in controlled environment at Aalborg University
For the experiments in the controlled environment, at Aalborg University, the technical implementation with the RoboDK OLP is used. A digital model of the robot cell was built using the RoboDK OLP. The digital model of the welding-robot cell can be observed in Figure 7 together with the physical cell. The experimental setup is composed of a Universal Robots UR10 collaborative manipulator, a Migatronic Sigma Select 400 CW welding apparatus and a Siegmund Professional 750 welding table which allows for precise placement of the work piece to be welded using the measurement mesh available on the table’s surface.
After the geometry and the design parameters of the weldment are extracted using Algorithm 1 the motion parameters for the welding torch can be computed by using the Weldment Extraction Tool interface in which Algorithm 2 is implemented. Each position