A digital life-cycle management framework for sustainable smart manufacturing in energy intensive industries

Energy intensive industries can be classified into those that process metal, glass, ceramics, paper, cement, and bulk chemicals. They are associated with significantly high proportions of carbon emissions, consume a lot of energy and raw materials, and cause energy wastage as a result of heat escaping from furnaces, reheating of products, and rejection of parts. In alignment with UN sustainable development goals of industry, innovation, infrastructure and responsible consumption and production, it is important to ensure that the energy consumption of EIIs are monitored and reduced such that their energy efficiency can be improved. Towards this aim, it is possible to employ the concepts of digitalization and smart manufacturing to identify the critical areas of improvement and establish enablers that can help improve the energy efficiency. The aim of this research is to review the current state of digitalisation in energy-intensive industries and propose a framework to support the realisation of sustainable smart manufacturing in Energy Intensive Industries (EIIs). The key objectives of the work are (i) the investigation of process mining and simulation modelling to support sustainability, (ii) embedding intelligence in EIIs to improve energy and material efficiency and (iii) proposing a framework to enable the digital transformation of EIIs. The proposed five-layer framework employs data acquisition, process management, simulation & modelling, artificial intelligence, and data visualisation to identify and forecast energy consumption. A detailed description of the various phases of the framework and how they can be used to support sustainability and smart manufacturing is demonstrated using business process data obtained from a machining industry. In the demonstrated case study, the process management layer utilises Disco for process mining, the simulation layer utilises Matlab SimEvent for discrete-event simulation, the artificial intelligence layer utilises Matlab for energy prediction and the visualisation layer utilises grafana to dashboard the e-KPIs. The findings of the research indicate that the proposed digital life-cycle framework helps EIIs realise sustainable smart manufacturing through better understanding of the energy-intensive processes. The study also provided a better understanding of the integration of process mining and simulation & modelling within the context of EIIs

Convertibility Evaluation of Automated Assembly System Designs for High Variety Production

The recent advancements in technology and the high volatility in automotive market compel industries to design their production systems to offer the required product variety. Although, paradigms such as reconfigurable modular designs, changeable manufacturing, holonic and agent based systems are widely discussed to satisfy the need for product variety management, it is essential to practically assess the initial design at a finer level of granularity, so that those designs deemed to lack necessary features can be flagged and optimised. In this research, convertibility expresses the ability of a system to change to accommodate product variety. The objective of this research is to evaluate the system design and quantify its responsiveness to change for product variety. To achieve this, automated assembly systems are decomposed into their constituent components followed by an evaluation of their contribution to the system's ability to change. In a similar manner, the system layout is analysed and the measures are expressed as a function of the layout and equipment convertibility. The results emphasize the issues with the considered layout configuration and system equipment. The proposed approach is demonstrated through the conceptual design of battery module assembly system, and the benefits of the model are elucidated

An Application of Physical Flexibility and Software Reconfigurability for the Automation of Battery Module Assembly

Batteries are a strategic technology to decarbonize conventional automotive powertrains and enable energy policy turnaround from fossil fuels to renewable energy. The demand for battery packs is rising, but they remain unable to compete with conventional technologies, primarily due to higher costs. Major sources of cost remain in manufacturing and assembly. These costs can be attributed to a need for high product quality, material handling complexity, uncertain and fluctuating production volumes, and an unpredictable breadth of product variants. This research paper applies the paradigms of flexibility from a mechanical engineering perspective, and reconfigurability from a software perspective to form a holistic, integrated manufacturing solution to better realize product variants. This allows manufacturers to de-risk investment as there is increased confidence that a facility can meet new requirements with reduced effort, and also shows how part of the vision of Industry 4.0 associated with the integration and exploitation of data can be fulfilled. A functional decomposition of battery packs is used to develop a foundational understanding of how changes in customer requirements can result in physical product changes. A Product, Process, and Resource (PPR) methodology is employed to link physical product characteristics to physical and logical characteristics of resources. This mapping is leveraged to enable the design of a gripper with focused flexibility by the Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, as it is acknowledged that mechanical changes are challenging to realize within industrial manufacturing facilities. Reconfigurability is realised through exploitation of data integration across the PPR domains, through the extension of the capabilities of a non-commercial virtual engineering toolset developed by the Automation Systems Group at the University of Warwick. The work shows an “end-to-end” approach that practically demonstrates the application of the flexibility and reconfigurability paradigms within an industrial engineering context

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort

Image segmentation of micro-TIG battery welds

Inspection of cell-to-tab welds in module assembly for battery pack is one the most critical processes in the production of battery packs for transportation electrification. A procedure suitable for the segmentation of weld images on module assembly lines is proposed to separate them into weld and tab regions. The procedure is centred around identifying the edge of the weld and the convex hull region that includes it. The edge is detected with a fuzzy logic rule-based inference system. The procedure is demonstrated with a set of 71 images that are labelled to establish a comparison reference, the so called ground truth. Particle swarm optimisation is used to find values of the parameter procedure that result in a a local minimum of the mean percent error (MPE). An MPE of 4.25 per cent has been obtained

Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network

Calibration is a vital part of ensuring the safety and smooth operation of any industrial robot and this is particularly essential for collaborative robots as any issue pertaining to safety can adversely impact the human operator. Towards this aim, Prognostics and Health Management (PHM) has been widely implemented in the context of collaborative robots to ensure safe and efficient working environments. In this research, as a subset of PHM research, a novel positional health assessment approach based on a Long Short-Term Memory auto-encoder network (LSTMAE) is proposed. An experimental test setup is utilised, wherein the collaborative robot is subject to variations of coordinate system positional error. The operational 3-axis position time-series data of the collaborative robot is collected with the aid of an industrial data acquisition platform utilising influxDB. The experiments show that, with the aid of this approach, manufacturers can assess the positional health of their collaborative robot systems