Publications
A digital life-cycle management framework for sustainable smart manufacturing in energy intensive industries
Malarvizhi Kaniappan Chinnathai, Bugra AlkanJournal of Cleaner Production (2023) A digital life-cycle management framework for sustainable smart manufacturing in energy intensive industries
Abstract
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 digitalisation 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-cyle framework helps EIIs realise sustainable smart manufacturing through better understanding of the energyintensive processes. The study also provided a better understanding of the integration of process mining and simulation & modelling within the context of EIIs. Abstract
Publication Link
A novel data-driven approach to support decision-making during production scale-up of assembly systems
Chinnathai, Malarvizhi Kaniappan, Bugra Alkan, and Robert Harrison Journal of Manufacturing Systems 59 (2021): 577 - 595 A novel data-driven approach to support decision-making during production scale-up of assembly systems
Abstract
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.Abstract
Publication Link
An Application of Physical Flexibility and Software Reconfigurability for the Automation of Battery Module Assembly
Malarvizhi Kaniappan Chinnathai, Till Gunther, Mussawar Ahmad, Cosima Stocker, Lukas Richter, David Schreiner, Daniel Vera, Gunther Reinhart, Robert HarrisonProcedia CIRP 63 (2017): 604 - 609 An Application of Physical Flexibility and Software Reconfigurability for the Automation of Battery Module Assembly
Abstract
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.Abstract
Publication Link
Convertibility evaluation of automated assembly system designs for high variety production
Chinnathai, Malarvizhi Kaniappan, Bugra Alkan, and Robert Harrison Procedia CIRP 60 (2017): 74-79 Convertibility evaluation of automated assembly system designs for high variety production
Abstract
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.Abstract
Publication Link
Pilot To Full-Scale Production: A Battery Module Assembly Case Study
Chinnathai, Malarvizhi Kaniappan, Bugra Alkan, Daniel Vera, and Robert Harrison Procedia CIRP 72 (2018): 796 - 801 Pilot To Full-Scale Production: A Battery Module Assembly Case Study
Abstract
Electric vehicles are currently on the rise due to environmental and legal concerns. Furthermore, improvements made in battery assembly steadily boosts the efficiency of electric vehicles. A well-prevalent method to overcome the uncertainties that emerge from the ever-changing battery technology, is to assemble products using pilot production lines. However, literature pertaining to the scale-up of pilot production lines for full scale production is scarce. Therefore, in this paper, potential scale-up scenarios for battery module assembly line are proposed in a discrete event simulation software and results are compared. Furthermore, the benefits of the proposed method are discussed with a test caseAbstract
Publication Link
A framework to predict energy related key performance indicators of manufacturing systems at early design phase
Fadi Assad, Bugra Alkan, Malarvizhi Kaniappan Chinnathai, Mus'ab Ahmad, Emma Rushforth, Robert HarrisonProcedia CIRP 81 (2019): 145 - 150 A framework to predict energy related key performance indicators of manufacturing systems at early design phase
Abstract
Increasing energy prices, growing market competition, strict environmental legislations, concerns over global climate change and customer interaction incentivise manufacturing firms to improve their production efficiency and minimise bad impacts to environment. As a result, production processes are required to be investigated from energy efficiency perspective at early design phase where most benefits can be attained at low cost, time and risk. This article proposes a framework to predict energy-related key performance indicators (e-KPIs) of manufacturing systems at early design and prior to physical build. The proposed framework is based on the utilisation and incorporation of virtual models within VueOne virtual engineering (VE) tool and WITNESS discrete event simulation (DES) to predict e-KPIs at three distinct levels: production line, individual workstations and the components as individual energy consumption units (ECU). In this framework, alternative designs and configurations can be investigated and benchmarked in order to implement and build the best energy-efficient system. This ensures realising energy-efficient production system design while maintaining predefined production system targets such as cycle-time and throughput rate. The proposed framework is exemplified by a use case of a battery module assembly system. The results reveal that the proposed framework results meaningful e-KPIs capable of supporting manufacturing system designers in decision making in terms of component selection and process design towards an improved sustainability and productivity.Abstract
Publication Link
Assessing complexity of component-based control architectures used in modular automation systems
Bugra Alkan, Daniel Vera, Malarvizhi Kaniappan Chinnathai, and Robert Harrison IJCEE 9 (2017) Assessing complexity of component-based control architectures used in modular automation systems
Abstract
Component-based development (CBD) supports hierarchical decomposition of manufacturing control architectures through data and procedural abstraction, allowing designers to handle system development complexity better than function-oriented methods. Although the CBD approach helps managing complexity of the software design and development process, it does not reduce or eliminate complexity of control systems. In fact, large and highly coupled system architectures make entire software very difficult to understand and modify, especially during manufacturing system re-configuration and scale up/down processes. Therefore, it is essential to maintain simplicity in control system design, without disregarding the required modularity and functionality. This paper proposes an information-theoretic measure to quantify the complexity of component-based manufacturing control systems. The proposed measure is tested over the auto-generated control codes of Festo MPS system for its validity. The authors believe that the proposed approach can serve as a proactive design support, especially useful for early design stages as it allows designers to select the optimal control architectures with least complexity and provides a clear understanding of the potential stress points.Abstract
Publication Link
Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches.
Dominik Kohr, Mussawar Ahmad, Bugra Alkan, Malarvizhi Kaniappan Chinnathai, L. Budde, D. Vera, T. Friedli, R. HarrisonCOMPLEXIS (2018)Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches.
Abstract
A multiplicity of factors including technological innovations, dynamic operating environments, and globalisation are all believed to contribute towards the ever-increasing complexity of manufacturing systems. Although complexity is necessary to meet functional needs, it is important to assess and monitor it to reduce life-cycle costs by simplifying designs and minimising failure modes. This research paper identifies and describes two key industrially relevant methods for assessing complexity, namely a data-centric approach using the information theoretic method and a human-centric approach based on surveys and questionnaires. The paper goes on to describe the benefits and shortcomings of each and contributes to the body of knowledge by proposing a holistic framework that combines both assessment methods.Abstract
Publication Link
A Framework for Pilot Line Scale-up using Digital Manufacturing
Malarvizhi Kaniappan Chinnathai, Zeinab Al-Mowafy, Bugra Alkan, Daniel Vera, Robert HarrisonProcedia CIRP 81 (2019): 962 - 967A Framework for Pilot Line Scale-up using Digital Manufacturing
Abstract
Pilot lines are essential test-beds for process and product validation before the establishment of production lines. However, there is a lack of well-defined methodology for pilot line scale-up. To better support this transition, Virtual Models can be integrated with Discrete-Event Simulation (DES) models for potential production-line configurations. However, the validation of the developed models is hardly possible due to the absence of a physical counterpart. Therefore, this paper proposes a framework to increase the accuracy of the DES scale-up models with Virtual Modelling tools and Ontology. Subsequently, a test-case is used to explain the concept.Abstract
Publication Link
Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components
Bugra Alkan, Malarvizhi Kaniappan Chinnathai Machines 2021 9(12) 341Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components
Abstract
The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.Abstract
Publication Link