Ebook: Advances in Manufacturing Technology XXXVI

The International Conference in Manufacturing Research (ICMR) is a major event for academics and industrialists who are engaged in manufacturing research. Held annually in the UK (except 2018 in Sweden) since the late 1970s, the conference is renowned as a friendly and inclusive platform that brings together a broad community of researchers who share a common goal: developing and managing technologies and operations that are key to sustaining the success of manufacturing businesses. For over two decades, ICMR has been the main manufacturing research conference organized in the UK, successfully bringing researchers, academics, and industrialists together to share their knowledge and experiences. Initiated as a National Conference by the Consortium of UK University Manufacturing and Engineering (COMEH), it became an International Conference in 2003.

COMEH is an independent body established in 1978. Its main aim is to promote manufacturing engineering education, training, and research. The Consortium maintains a close liaison with government bodies concerned with the training and continuing development of professional engineers, while responding to the appropriate consultative and discussion of documents and other initiatives. COMEH is represented on the Engineering Professor’s council (EPC) and it organizes and supports manufacturing engineering education research conferences and symposia. Hosts for National Conferences on Manufacturing Research (NCMR) have been:

1985 Nottingham

1986 Napier

1987 Nottingham

1988 Sheffield

1989 Huddersfield

1990 -

1991 Hatfield

1992 Central England

1993 Bath

1994 Loughborough

1995 Leicester De Montfort

1996 Bath

1997 Glasgow Caledonian

1998 Derby

1999 Bath

2000 East London

2001 Cardiff

2002 Leeds Metropolitan

In 2003 the conference title became the International Conference in Manufacturing Research (ICMR) incorporating the NCMR. The host universities for ICMR have been:

2003 Strathclyde

2004 Sheffield Hallam

2005 Cranfield

2006 Liverpool John Moores

2007 Leicester De Montfort

2008 Brunel

2009 Warwick

2010 Durham

2011 Glasgow Caledonian

2012 Aston

2013 Cranfield

2014 Southampton Solent

2015 Bath

2016 Loughborough

2017 Greenwich

2018 Skövde, Sweden

2019 Queen’s University Belfast

2021 Derby

2022 Derby

2023 Aberystwyth

Companies are operating in an era where Volatility, Uncertainty, Complexity and Ambiguity (VUCA) is commonplace in manufacturing companies. To manage effectively in this environment, manufacturing supply chains need to adopt resilient strategies. Organisations continue to adopt various technologies in order to manage the VUC environment and Marine Renewable Energy (MRE) supply chains are seen as a front runner in emerging technologies which has an impact on the business environments and supply chain practices globally. In this study, the authors undertook a systematic literature review of 95 scholarly articles published on Marine-driven supply chains and explore the impact and importance of manufacturing resilience. A bibliometric analysis was carried out via the Scopus database and results filtered, coded, and analysed using the bibliometric-R package. The authors then evaluated the thematic areas of Marine Supply Chains and explored identified challenges and proposed areas of development to move towards a resilient manufacturing supply chain.

This is a review conducted in relation to the application of artificial intelligence in operations management particularly in the design aspects between the years 2010–2021. The purpose of this paper is to provide a survey of the usage of artificial intelligence methods in operations management aimed at presenting the themes, trends, direction of research and its practical impacts. Artificial Intelligence is playing a major role in the fourth industrial revolution, and a lot of evolution in various operations management and engineering scope. Artificial Intelligence techniques are widely used by operations managers to solve a whole range of intractable problems. This study provides a forum for rapid evaluation of the works describing the theoretical and practical application of artificial intelligence methods in operations management. The study reports some novel aspects of artificial intelligence used for real-world operations/engineering applications towards solving problems, increased productivity, reducing costs and improved customer satisfaction.

The increasing global focus on sustainability developed the need to integrate sustainable practices among all sectors of the economy, including the public sector, which faces pressure from the governments to improve their organisational performance. This study explores the opportunities of Lean implementation as an effective approach to achieving sustainability in public service organisations in the context of environmentally sustainable development goals (SDGs). Through a literature review, the authors explored how Lean applications and practices could be borrowed from the manufacturing industry and implemented in the service sector. They examined the various practices employed by service organisations to integrate sustainability into their operations to enhance their environmental performance. Initial findings indicate that Lean methodologies can potentially enhance environmental performance in public service organisations by reducing non-value-added activities, waste, and environmental impact. This implementation requires adapting and customising Lean methodology to align with the unique characteristics of these organisations.

With each phase of industrial revolution, the interaction between humans and technology have evolved and context of work has changed, calling for new approaches to leadership. The fourth industrial revolution (Industry 4.0) calls for responsive and digital leaders who can navigate through complex adaptive systems where direction, decision and innovation emerge from connected ecosystem & diverse agents. However, Industry 4.0 tends to overlook the importance of human-value dimensions, sustainability, and social fairness and hence a paradigm shift towards Industry 5.0 will focus shifts from a techno-deterministic rationale to human-deterministic rationale. Industry 5.0 seeks to combine consistency and speed of collaborative machines with resourcefulness and creativity of humans for the mutual benefit of industry and workforce. Thus, with a focus on manufacturing industry, this paper focuses on identifying the gap in leadership literature and emphasises on the need to identify how leadership should evolve to address the changing dynamics between human-machine co-working in manufacturing that is expected in the era of Industry 5.0.

Laser-powder bed fusion LPBF techniques can be used to manufacture complex-shaped, thin-walled, hollow, or slender parts. Although the dimensions of the generated components are close to the final measurements, additional machining processes are required to obtain the desired surface finish and dimensional tolerance. The melt pool dynamic during the LPBF operation results in directional gain structures in alloys. The resulting mechanical properties are strongly dependent on the component build orientation, which can affect the machinability of the produced part. This review paper provides knowledge on the role of microstructure in the machinability of LPBF-produced IN718. The effect of grain shape and distribution, grain boundary density on the surface integrity, and resulting cutting forces are investigated.

The steel industry is a significant contributor to global carbon emissions, making the sustainability of it an important area of improvement. Existing decarbonisation solutions such as carbon capture, hydrogen-based steelmaking and electrolysis have been explored but the potential of artificial intelligence, and specifically computer vision, is yet to be realised. Computer vision has shown competence in a range of steelmaking applications but has not been linked to sustainability in the industry. This lack of awareness results in missed opportunities for sustainable development. The introduction of this paper connects computer vision to steelmaking and sustainability, which is followed by a literature review based on existing technologies, and the description of a future vision of steelmaking. The paper will be finalised with conclusions.

Cold Spray is an emerging technology in the domain of additive manufacturing. It is a solid-state high strain rate material deposition technique. It uses a supersonic (2-4 Mach) impact of process gas (such as nitrogen or helium) to deposit micron-sized (1-100 μm) metallic or composite powder particles onto a substrate via a severe plastic deformation mechanism without any significant fusion. To have a successful deposition, the specific powder particles should travel above a material-dependent threshold velocity, which is called the critical velocity. The convergent-divergent nozzle is employed for achieving high velocities. The main objective of the current research is to study the flow visualization of two-phase titanium particle laden nitrogen gas in a simulated 2-D axisymmetric nozzle where particles are having a particle size of 25 microns, and to investigate the suitability of a specific set of cold spraying process parameters for the successful deposition of titanium powder using computational fluid dynamics. For the analysis, a two-equation realizable k-ε simulation viscous model was preferred due to its more realistic consideration of the cold spray process and reduced computational cost. Titanium powder particles will be successfully deposited using cold spray when operated at the precise set of process conditions on account of the average particle velocity observed at standoff distance higher than the critical velocity.

Additive Manufacturing (AM) offers itself as a valuable tool for exploring a range of possibilities through design and development of complex shapes in fluid flow research. itself as a valuable tool for exploring. In this research, the limitations of existing components in a 2.5 m open channel fluid flow experiment, which are characterized by their standard basic shapes, are examined. Specifically, this research focuses on investigating flow control in hydraulic systems by introducing gates with more intricate geometries. However, these complex design shapes are typically expensive and time-consuming to acquire from equipment suppliers. To circumvent these challenges, AM technology allow a cost-effective solution to implement progressive design modifications throughout the investigation. This paper presents a comparative analysis between a range of positions of an AM produced curved drum gate in terms of flow rate, fluid velocity profile, water level height and related fluid flow parameters. To analyse and validate the results, Computational Fluid Dynamics (CFD) modelling, analysis, and simulation techniques are employed. Based on the experimental findings and verification, this paper discusses the suitability and applicability of AM techniques in fluid flow analysis. The ability to manufacture customized components through AM offers a promising avenue for enhancing fluid flow research, allowing for cost-effective and time-efficient design modifications. The key concept associated with this study is the importance of utilizing AM and specific materials for advancing fluid flow analysis.

Parallel Kinematic Machines (PKM) demonstrate the capability of adapting to modern, flexible manufacturing systems due to their higher flexibility and improved motion dynamics. Compliance of a machine tool has a significant impact on the performance, which directly contributes to the quality of the machined workpiece. Compliance deformations result in inaccuracies in the geometry of the machined part. Therefore, prediction of compliance deformation helps to determine the geometrical quality. To fill in the knowledge gap, this paper presents a compliance-induced geometrical error prediction method based on a semi-analytical stiffness model.

Monitoring operator compliance with work instructions is critical to ensuring that product quality is maintained, safety requirements are fulfilled, rules are followed, efficiency is enhanced, and suitable training is provided. Various industries employ different tools to manage operator performance, such as Standard Operating Procedures, Checklists, Performance metrics, Audits or Inspections. Due to the requirements for infrastructure, manpower, training, maintenance, integration, and privacy issues, the process of monitoring operators’ performance can be costly for industry. On the other hand, the advantages of a well-designed monitoring system can offset these cost expenses by increasing productivity, quality, and safety, resulting in long-term cost savings and better competitiveness. This paper reports a research project that transferred Hololens 2 with Microsoft Dynamics Guide to a tool that simultaneously guides and monitors operators’ real-time performance. The new technology can assist industry in identifying areas where operators may require more training or support and optimising workflow to increase overall efficiency. Furthermore, the data gathered by this technology may be utilised to enhance future trainings and modify ongoing production processes.

Digital Twin (DT) is a virtual representation that is parameterized based on the real process data to model, simulate, monitor, analyze, and optimize the physical systems they represent. DT have been predominantly used in the mechanical engineering field and have yet to be extensively used in chemical flow processes particularly for the challenge of scale-up which is very important particularly when moving from lab experiment to industrial scales. It is a challenge to maintain various process parameters while increasing the scale of reactors geometry. The parameters might not show a predictable linear co-relationship due to concurrent chemical conversion processes behaving differently on different scale. We apply and compare various machine learning methodologies such as Radial Basis Function Neural Networks, Gaussian Process Regression and Polynomial Regression to the development of chemical flow process DT for scale-up. We show that these methodologies can be used to predict the product yield of a chemical flow process during scale-up.

The industrial transition to Industrie 4.0 and subsequently Industrie 5.0 requires robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by the humans and where it is possible for the human and the robot to mutually perceive, interpret and act on each other’s actions and intentions. To achieve this, strategies for human- robot interaction are needed that are adapted to operators’ needs and characteristics in an industrial context, i.e., Operator 5.0. This paper presents a research design for the development of a framework for human-robot interaction strategies based on ANEMONE, which is an evaluation framework based on activity theory, the seven stages of action model, and user experience (UX) evaluation methodology. At two companies, ANEMONE is applied in two concrete use cases, collaborative kitting and mobile robot platforms for chemical laboratory assignments. The proposed research approach consists of 1) evaluations of existing demonstrators, 2) development of preliminary strategies that are implemented, 3) re-evaluations and 4) cross-analysis of results to produce an interaction strategy framework. The theoretically and empirically underpinned framework-to-be is expected to, in the long run, contribute to a sustainable work environment for Operator 5.0.

The industrial evolutions require robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by human workers. A prerequisite is the possibility for the human and the robot to mutually perceive, interpret and act on each other’s actions and intentions. To achieve this, strategies for human-robot interaction are needed that are adapted to operators’ needs and characteristics in the industrial contexts. In this paper, we aim to present various taxonomies of levels of automation, human-robot interaction, and human-robot collaboration suggested for the envisioned factories of the future. Based on this foundation, we propose a compass direction for continued research efforts which both zooms in and zooms out on how to develop applicable human-robot interaction strategies that are worker-centric in order to obtain effective, efficient, safe, sustainable, and pleasant human-robot collaboration and coexistence.

Collaborative robots, designed to work alongside humans in industrial manufacturing, are becoming increasingly prevalent. These robots typically monitor their distance from workers and slow down or stop when safety thresholds are breached. However, this results in reduced task execution performance and safety-related uncertainty for the worker. To address these issues, we propose an alternative safety strategy, where the worker is responsible for their own safety, and the robot executes its task without modifying its speed except in case of imminent contact with the worker. The robot provides precise situation-awareness information to the worker using a mixed-reality display, presenting information about relative distance and movement intentions. The worker is then responsible for placing themselves with respect to the robot. A pilot user study was conducted to evaluate the efficiency of task execution, worker safety, and user experience. Preliminary results may indicate a superior user experience while maintaining worker safety.

Demonstrators, testbeds and learning factories enable researchers to investigate important manufacturing challenges and to trial solutions without disrupting industrial production facilities. In this way, solutions and systems can be developed close to a ‘production-ready’ state prior to industrial deployment. This paper reviews demonstrators and testbeds developed for smart manufacturing in the last 20 years. A key observation is that such demonstrators have predominantly focused on emulating single or multiple closely connected operations. Such developments reflect the activities of a single production facility and/or organisation. In contrast, there are few reports on demonstrators which seek to replicate the behaviour and challenges associated with multi-site factories or integration with existing legacy factory systems. To address this gap, a multi-operation demonstrator has been created. The demonstrator aims to replicate coordinated production between multiple small manufacturing sites and provides a testbed to investigate operational challenges. The current demonstrator, the research investigated, and the direction of future research proposed are outlined.

This study intends to bridge the extant literature and knowledge gap by investigating UK textile manufacturing to demonstrate the effect of supply chain practices and organisational performance. The significance of this research cannot be understated and will be of great significance to textile companies in the UK and globally as it will offer beneficial preferences into the idea of effective supply chain management practices and their impact on unit performance. In addition, their stakeholders will gain a better understanding of the barriers to implementing effective SCM practices and make relevant recommendations on how to address them. Policymakers and governments will also benefit from this research on appropriate strategies for implementing supply chain management practices. In addition, this research also has great importance for other scholars and students who are interested in conducting similar research, so it can be used as a reference for the literature. This study will open avenues for the UK scholars, providing them the opportunity to establish the relationship between supply chain practices and organisational performance.

The rapid emergence of digital technologies is revolutionising the manufacturing industry, with digital twins (DTs) emerging as a transformative innovation. DTs, virtual counterparts of physical entities or systems, hold immense potential for optimizing and overseeing manufacturing processes. By proposing a three-dimensional classification framework that considers DT integration levels, supply chain (SC) structural hierarchies, and SC processes, this study examines the evolution of manufacturing DTs to encompass the SC. An analysis of existing literature reveals a significant gap in extending manufacturing DTs to the SC system level without incorporating structural and process aspects of DT models. The paper also investigates the key performance objectives, including efficiency, resilience, and sustainability, as well as the challenges associated with DT implementation. The insights provided in this paper contribute to a better understanding and practical application of DTs in the dynamic contexts of manufacturing and the SC.

Although Industry 4.0 and the Internet of Things (IoT) have been implemented in various manufacturing sectors, medium and small enterprises have limited integrations with this strategy, especially those that still rely on conventional production lines and lack the capacity for transition in near future. This paper presents a work demonstrating how conventional production lines could be upgraded for implementing an Industry 4.0 strategy, using plastic injection moulding as a case study. This transformation helps to achieve a real-time optimised value chain, considering factors such as cost, availability, and resource consumption, while the implementation cost is kept low.

With the evolution of Industry 4.0, the most advanced technologies have been invented and due to rapid globalisation, supply chains (SC) have become vulnerable to various risks and reconfiguration of supply chain design has gained a significant consideration in recent years. This paper intends to provide a critical literature review on the current research practices and identify the key factors influencing the reconfiguration of supply chain design in the digital environment and prioritise the factors considering relative importance and develop a framework to mitigate the risk level. A systematic literature review is conducted to identify and analyse the key factors that influence the reconfiguration of supply chain design, and Analytical Hierarchy Process (AHP) method used to develop a conceptual framework. The findings of this study revealed that reconfiguration of supply chain design in digital environment sheds light on future research and focuses on the potential to enhance supply networks’ efficiency and responsiveness.