In production of automotive components, control-measuring is an important activity to assure that geometries meet expected tolerances. This is done via randomly taking parts out of production for control-measuring in a fixture. This fixture is both a tedious and repetitive product to design and configure. The aim of this paper is therefore to present an approach to adopt a design automation strategy towards supporting the configuration of fixtures and to discuss opportunities for moving towards a Product-Service System-paradigm in this domain. This paper reports on a development of a design automation demonstrator to configure fixtures for control-measuring. The demonstrator has been developed in a commercial CAD-environment and will be deployed through a web-based interface. The paper concludes with a discussion on PSS-opportunities and how to drive this with a Knowledge-Based Engineering-modelling approach. 

Sheet Metal Forming is a very complex manufacturing process with a number of non-linearities, e.g. large deformations, localisation, elastic-plastic materials, pressure and velocity dependant friction conditions and structural deficiencies in the die and press, present and interacting simultaneously. This leads to disturbances in running production that results in production waste, e.g. down time for the press line and cost for rework and scrapping of parts. These production problems are also hard to understandand solve based on experience and analytical models due to the presence of several non-linearities. An alternative is to try to solve these problems proactively before they occur. This could be done with model based control by creating a digital twin of the die-set and the press line. Therefore, a virtual production process is developed to be able to use as knowledge building and as engineering tool during development, manufacturing, issue resolution and optimization. In this paper presents the authors ideas and plans for research and other activities within the area of model based control of sheet metal forming.

A major issue within automotive Sheet Metal Forming (SMF) concerns ensuring desired output product quality and consistent process performance. This is fueled by complex physical phenomena, process fluctuations and complicated parameter correlations governing the dynamics of the production processes. The aim of the thesis is to provide a deeper understanding of the challenges and opportunities in this regard within automotive SMF. The research is conducted in collaboration with a global automotive manufacturer. 

The research shows that systematic investigations using process simulation models allow exploration of the product-process parameter interdependencies and their influence on the output product quality. Furthermore, it is shown that incorporating in-line measured data within process simulation models enhance model prediction accuracy. In this regard, automating the data processing and model configuration tasks reduces the overall modelling effort.

However, utilization of results from process simulations within a production line requires real-time computational performance. The research hence proposes the use of reduced process models derived from process simulations in combination with production data, i.e. a hybrid data- and model-based approach. Such a hybrid approach would benefit process performance by capturing the deviations present in the real process while also incorporating the enhanced process knowledge derived from process simulations. Bringing monitoring and control realms within the production process to interact synergistically would facilitate the realization of such a hybrid approach.

The thesis presents a procedure for exploring the causal relationship between the product-process parameters and their influence on output product quality in addition to proposing an automated approach to process and configure in-line measured data for incorporation within process simulations. Furthermore, a framework for enhancing output product quality within automotive SMF is proposed. Based on the thesis findings, it can be concluded that in-line measured data combined with process simulations hold the potential to unveil the convoluted interplay of process parameters on the output product quality parameters.

Since its introduction in 2011, industry 4.0 has been coined the“4th industrial revolution” following mechanization, industrialization and IT/automation as the first three, and represents the current trend of automation technologies (cyber‐physical systems, internet of things, cloudcomputing, etc.,) in the manufacturing industry, with their potential for disruption of the manufacturing paradigm as we know it. However, the effect and role of industry 4.0 on the design and development of the new products to be manufactured in industry 4.0 facilities is not clear. This research presents a literature review to; 1) understand the concept of industry 4.0 from an implementation (state of practice) viewpoint, 2) learn about approaches and considerations currently deployed for developing products to be produced in manufacturing plants progressively transforming into industry 4.0 environments. Results reveal that the potential of industry 4.0 is underexploited within product design and development, especially in the conceptual stages lacking methods, tools, and approaches.While later stages of the product development (production planning,ramp‐up) have received some attention in regards with optimizing production operations, several publications acknowledge its potential to benefit earlier process stages.

The ability to control quality of a part is gaining increased importance with desires to achieve zero-defect manufacturing. Two significant factors affecting process robustness in production of deep drawn automotive parts are variations in material properties of the blanks and the tribology conditions of the process. It is imperative to understand how these factors influence the forming process in order to control the quality of a formed part. This paper presents a preliminary investigation on the front door inner of a Volvo XC90 using a simulation-based approach. The simulations investigate how variation of material and lubrication properties affect the numerical predictions of part quality. To create a realistic lubrication profile in simulations, data of pre-lube lubrication amount, which is measured from the blanking line, is used. Friction models with localized friction conditions are created using TriboForm and is incorporated into the simulations. Finally, the Autoform-Sigmaplus software module is used to create and vary parameters related to material and lubrication properties within a user defined range. On comparing and analysing the numerical investigation results, it is observed that a correlation between the lubrication profile and the predicted part quality exists. However, variation in material properties seems to have a low influence on the predicted part quality. The paper concludes by discussing the relevance of such investigations for improved part quality and proposing suggestions for future work.

Reliable design of stamping dies is desired for efficient and safe production. The design of stamping dies are today mostly based on casting feasibility, although it can also be based on criteria for fatigue, stiffness, safety, economy. Current work presents an approach that is built on Simulation Driven Design, enabling Design Optimization to address this issue. A structural finite element model of a stamping die, used to produce doors for Volvo V70/S80 car models, is studied. This die had developed cracks during its usage. To understand the behaviour of stress distribution in the stamping die, structural analysis of the die is conducted and critical regions with high stresses are identified. The results from structural FE-models are compared with analytical calculations pertaining to fatigue properties of the material. To arrive at an optimum design with increased stiffness and lifetime, topology and free-shape optimization are performed. In the optimization routine, identified critical regions of the die are set as design variables. Other optimization variables are set to maintain manufacturability of the resultant stamping die. Thereafter a CAD model is built based on geometrical results from topology and free-shape optimizations. Then the CAD model is subjected to structural analysis to visualize the new stress distribution. This process is iterated until a satisfactory result is obtained. The final results show reduction in stress levels by 70% with a more homogeneous distribution. Even though mass of the die is increased by 17 %, overall, a stiffer die with better lifetime is obtained. Finally, by reflecting on the entire process, a coordinated approach to handle such situations efficiently is presented.

The ability to predict and control the outcome of the sheet metal forming process demands holistic knowledge of the product/process parameter influences and their contribution in shaping the output product quality. Recent improvements in the ability to harvest in-line production data and the increased capability to understand complex process behaviour through computer simulations open up the possibility for new approaches to monitor and control production process performance and output product quality. This research presents an overview of the common process monitoring and control approaches while highlighting their limitations in handling the dynamics of the sheet metal forming process. The current paper envisions the need for a collaborative monitoring and control system for enhancing production process performance. Such a system must incorporate comprehensive knowledge regarding process behaviour and parameter influences in addition to the current-system-state derived using in-line production data to function effectively. Accordingly, a framework for monitoring and control within automotive sheet metal forming is proposed. The framework addresses the current limitations through the use of real-time production data and reduced process models. Lastly, the significance of the presented framework in transitioning to the digital manufacturing paradigm is reflected upon.

With growing demands on quality of produced parts, concepts like zero-defect manufacturing are gaining increasing importance. As one of the means to achieve this, industries strive to attain the ability to control product/process parameters through connected manufacturing technologies and model-based control systems that utilize process/machine data for predicting optimum system conditions without human intervention. Present work demonstrates an automated approach to process in-line measured data of tribology conditions and incorporate it within sheet metal forming (SMF) simulations to enhance the prediction accuracy while reducing overall modelling effort. The automated procedure is realized using a client-server model with an in-house developed application as the server and numerical computing platform/commercial CAD software as clients. Firstly, the server launches the computing platform for processing measured data from the production line. Based on this analysis, the client then executes CAD software for modifying the blank model thereby enabling assignment of localized friction conditions. Finally, the modified blank geometry and accompanied friction values is incorporated into SMF simulations. The presented procedure reduces time required for setting up SMF simulations as well as improves the prediction accuracy. In addition to outlining suggestions for future work, paper concludes by discussing the importance of the presented procedure and its significance in the context of Industry 4.0.

Ability to predict and control involved parameters and hence the outcome of sheet metal forming processes demand holistic knowledge of the product/-process parameter influences and their contribution in shaping the output product quality. Recent improvements in the ability to harvest inline production data and the capability to understand complex process behaviour through computer simulations opens up the possibility for new monitoring and control approaches forimproving production process performance and output product quality. Current work presents a framework for monitoring and control of sheet metal forming processes which incorporates a hybrid data-and-model-based approach. An initial attempt to evaluate the proposed frameworks’ ability to support output product quality and process performance enhancements is made by implementing the proposed approach via an in-house built wire-bending machine prototype. Initial experiments conducted using the built prototype indicate that the proposed framework has the potential to support such enhancements and further work is needed to validate the overall framework.