Ebook: Advances in Manufacturing Technology XXXIV

The International Conference on 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 on 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 University of Derby

While previous Industrial Revolutions have increasingly seen the human as a cog in the system, each step reducing the cognitive content of work, Industry 4.0 contrarily views the human as a knowledge worker putting increased focus on cognitive skills and specialised craftsmanship. The opportunities that technological advancement provide are in abundance and to be able to fully take advantage of them, understanding how humans interact with increasingly complex technology is crucial. The Operator 4.0, a framework of eight plausible scenarios attempting to highlight what Industry 4.0 entails for the human worker, takes advantage of extended reality technology; having real-time access to large amounts of data and information; being physically enhanced using powered exoskeletons or through collaboration with automation; and finally real-time monitoring of operator status and health as well as the possibility to collaborate socially with other agents in the Industrial Internet of Things, Services, and People. Some of these will impose larger cognitive challenges than others and this paper presents and discusses parts of the Operator 4.0 projections that will have implications on cognitive work.

Digital Transformation becomes an essential strategy for organisations in this new digital arena, specifically after the COVID-19 pandemic and its unprecedented effects on the whole world and the manufacturing sector. In this new digital era, consumers became more expert and more engaging in the products using new technologies. At the same time, companies began to adopt digital transformation strategies in their manufacturing processes to become more agile and give the most value to their customers in fierce competition. This paper aims to identify and model the main challenges that face the digital transformation process in the manufacturing industry.. The main challenges were categorized to four main areas: skills, adoption of new technologies, change management practices, and innovation initiatives. By identifying these challenges, in a new and incremental way, manufacturing organisations will be able to adopt digital transformation processes efficiently and effectively in a proper manner.

Artificial intelligence in computer vision has focused on improving test performance using techniques and architectures related to deep neural networks. However, improvements can also be achieved by carefully selecting the training dataset images. Environmental factors, such as light intensity, affect the image’s appearance and by choosing optimal factor levels the neural network’s performance can improve. However, little research into processes which help identify optimal levels is available. This research presents a case study which uses a process for developing an optimised dataset for training an object detection neural network. Images are gathered under controlled conditions using multiple factors to construct various training datasets. Each dataset is used to train the same neural network and the test performance compared to identify the optimal factors. The opportunity to use synthetic images is introduced, which has many advantages including creating images when real-world images are unavailable, and more easily controlled factors.

Although digital transformation has been extensively researched in recent years, there is no clear definition, especially in the manufacturing industry. In addition, there is no oblivious model for the current manufacturing firms to be digitally transformed. As a part of a current research project, this paper proposes a framework for the digital transformation process in the manufacturing industry to be used and applied by various firms, especially small and medium enterprises (SMEs). The proposed framework combines the four main aspects of Digital transfroamtion in an integrated way. These four areas are Peaple, Strategy and Leadership, Business Processes, Enabling Technologies and Tools. The framework balances between the management and technological aspects of Digital Transformation implementation inside the manufacturing organisation.

Presented is a literature study into the importance of how information in assembly instructions in manual assembly is presented, more specifically how various factors such as the complexity of the assembly itself, the mental and physical workload of the worker, as well as the experience and skill level of the worker affect the requirements for information presentation. The requirements made by Industry 4.0 on flexibility in production lines and an increased number of variants produced causes increased demands on workers, which leads to more cognitive demands being made on assembly workers. Studies exist around assembly instruction modes, but have in many cases ignored factors such as worker skill level, mental workload, and task complexity and how these affect the requirements for information presentation, which is a major contribution of this study. The findings are that no single solution fits all requirements, but that the aforementioned factors should be taken into account.

Since late 2019, a novel Coronavirus disease 2019 (COVID-19) has spread globally. As a result, businesses were forced to send their workforce into remote working, wherever possible. While research in this area has seen an increase in studying and developing technologies that allow and support such remote working style, not every sector is currently prepared for such a transition. Especially the manufacturing sector has faced challenges in this regard. In this paper, the mental workload of two groups of participants is studied during a human-robot interaction task. Participants were asked to bring a robotised cell used in a dispensing task to full production by tuning system parameters. After the experiment, a self-assessment of the participants’ perceived mental workload using the NASA Task Load Index (NASA-TLX) was used. The results show that remote participants tend to have lower perceived workload compared to the local participants.

The objective of this paper is to outline a practical approach using numerical modelling and optimization techniques for process and product developments in metal cold rolled forming industry. The optimum economic viability in manufacturing industry requires a minimization of the amount of material used while the structural performance of a cold roll formed product relies on maintaining the stiffness and strength of the section in applications. This leads to the development of new cold forming processes and alternative cold roll formed profiles searching for the optimal profile. In this paper, a Finite Element modelling approach was utilized to simulating complicated manufacturing process and products and optimization techniques including Design Of Experiments was used to optimize the shape design of the end products to obtain lighter products while maintaining the product strength. These developments were illustrated through two case studies of Hadley Industries plc which included (1) numerical modelling of a novel Ultra STEEL^® cold roll forming process, and (2) optimization of cold roll forming sections.

Functional tones is a concept that originates in theoretical biology and resembles how the concept ‘affordances’ is used. Both functional tones and affordances are concepts dealing with particularly salient features in an individual’s immediate environment. The concept of affordances has proven useful for practitioners of usability and design as it supports intuitive ways of classifying how action possibilities match between a person and an object [1]. Functional tones have, however, thus far remained obscure among practitioners, despite functional tones having a stronger theoretical foundation and facilitates a deeper and more human-centred analysis of interaction. The functional tones related to an object depend not only on the modes of sensation and action the perceiver is capable of, but also more subjective aspects such as experience, motivation and emotions. Using functional tones in design or analysis of interaction provides a fundamentally user experience centred perspective while avoiding the philosophical luggage of affordances.

Based on different scenarios (professional guidelines) practice of Software Reverse Engineering (SRE) is used to analyse the combined instructions system to extract information regarding design and implementation of either part of, or the whole software application. These business rules are implemented in the form of a line code whereas actual source code is hidden and only gets the binary form of the code. Technologies that used for reverse engineering are CVF, V7, CFC, 14D, RTR, B#. These instruments are used for a better understanding of the program algorithm, logic, and program specifics in windows API functions, programming assembler language, network interaction principle. The tools that are discussed will not disturb the code consistency and basic structure of software. Present research shows a comparative analysis of various tools to establish which reverse engineering tool is better based on what characteristics.

Due to continuous tool engagement, turning processes tend to form long chips when machining ductile materials. These chip shapes have a negative influence on process performance and productivity. One approach to improve chip breakage is superimposition of vibrations in feed direction of the turning process, which leads to a modulation of uncut chip thickness. In a joint industrial project with Schaeffler Technologies AG & Co. KG, Fraunhofer IWU developed an oscillating actuator for turning. The actuator converts a rotational movement of a drive motor into a translational vibration via an eccentric gear. The tool shank is mounted in solid joint assemblies. With this prototypical system, a cyclic movement of the tool in feed direction can be realized. The typical operating parameters of the actuator is within the range of 1...100 Hz with adjustable vibration amplitudes up to 0.6 mm peak-to-peak. A significant improvement in chip breaking during the machining of steel 1.0503 was shown in cutting tests.

In a surface grinding process, a successive cutting-point space of grinding wheel affects the maximum abrasive grain depth of cut, which is a major factor affecting grinding characteristics such as the grinding forces and temperature. These characteristics degrade the productivity and machining accuracy. Therefore, we have to clearly define the successive cutting-point space. There are, however, few reports on the derivation method of the theoretical formula since abrasive grains inside the wheel are randomly distributed. This study aimed to theoretically derive the mean cutting-point space and to clarify the successive cutting-point space. We proposed a new derivation method for the mean cutting-point space, which was measured by mapping the diamond wheel surface using an EPMA. The theoretically derived mean cutting-point space was then compared with the measurement results.

Wire Arc Additive Manufacturing (WAAM) is a metallic additive manufacturing process based on the fusion of metallic wire using an electric arc as a heat source. The challenge associated with WAAM is heat management and understanding bead geometry. The printing process involves high temperatures, which results in the build-up of residual stresses can often cause deformations in a component. All of the process variables, such as torch speed (TS), wire feed speed (WFS), idle time, combine to produce the geometry of the deposit bead that results in the desired component shape. So, determining a method for choosing a good combined parameter process is very important to obtain a high-quality part. This article presents a study on how to use the WAAM process to produce a complexity part of aluminium alloys. The step of the determination process parameter is concentrated to develop in this study. An experimental design is determined to study the influence between the process parameters, for example, WFS, TS, high layer, length of bead. Different samples are made using the Yaskawa robot, using the classic CMT (Cold Metal Transfer) as a manufacturing method, using zigzag filling as a manufacturing strategy with the same WFS and same idle times and different TS, different bead lengths. A new manufacturing method using the zigzag filling strategy is proposed by adding an important step in determining the process parameters. The results indicate that the length of the bead has a significant impact on another parameter of the process.

Additive Manufacturing (AM) offers a range of possibilities in fluid flow research. An existing 2.5 m open channel fluid flow experiment contains a set of standard weirs which are limited in design. This research will compare experimental AM weirs (e.g. labyrinth, piano, catenary), that would not be possible on some laser-cut polymer or machined aluminium weirs. Due to the bespoke complex nature of weirs’ design other manufacturing methods would be too expensive and impossible to use. AM technology allows a cost-effective solution for progressive design modifications to be implemented throughout investigations. This paper will highlight comparisons made between a range of AM produced weirs in terms of flow rate, fluid velocity profile, water level height and discharge coefficient. Computation fluid dynamic modelling (CFD) will also be used to verify, analyse, and compare results. Based on the experimental results and verification, the paper will also discuss the suitability of application of AM techniques in fluid flow analysis.

Rapid technological change presents new opportunities and reveals new risks, challenging existing governance arrangements. The fusion of Industry 4.0 technologies combines with Additive Manufacturing (AM) to create new business solutions. Legal issues with AM are well documented, for example Daly[1] explores the interaction of 3D printing with the law, identifying Intellectual Property, Product Liability and Data Privacy as areas of importance. However, this technology fusion has also enabled improved real-time digital representation, monitoring, simulation and control of the physical delivered through applications of Digital Twin. Such Digital Twins are prevalent in manufacturing and in AM can potentially provide assurance that a printed item meets specified requirements. However, additional legal considerations are emerging. This paper illustrates these by examining the attributes of “Digital Twins in Additive Manufacture Use Cases” revealed through literature.

Open and closed porous structures with lattice and honeycomb geometry can be built using laser powder bed fusion additive manufacturing processes. The porous structures can be used to tailor the mechanical properties of a component or provide other functionality, such as for bone ingrowth in medical implants. Porous structures were created and analysed in this paper both physically and using finite element modelling. It was found that the accuracy of the built parts was reasonable and within the manufacturing processes general tolerance of +/- 50 μm. However, it was noticeable that the corners of the square shape pores were naturally filleted by the manufacturing process. The finite element model was developed using ANSYS software, stress concentrations were observed in the porous structures under loading. In addition to this, fragments of the material were present on the internal surfaces of the pores, which were formed from partially melted powder particles.

Composite materials are widely used because of their light weight and high strength properties. They are typically made up of multi-directional layers of high strength fibres, connected by a resin. The manufacturing of composite parts is complex, time-consuming and prone to errors. This work investigates the use of robotics in the field of composite material manufacturing, which has not been well investigated to date (particularly in simulation). Effective autonomous material transportation, accurate localization and limited material deformation during robotic grasping are required for optimum placement and lay-up. In this paper, a simulation of a proposed cooperative robotic system, which integrates an autonomous mobile robot with a fixed-base manipulator, is presented. An approach based on machine vision is adopted to accurately track the position and orientation of the fibre plies. A simulation platform with a built-in physics engine is used to simulate material deformation under gravity and external forces. This allows realistic simulation of robotic manipulation for raw materials. The results demonstrate promising features of the proposed system. A root mean square error of 9.00 mm for the estimation of the raw material position and 0.05 degrees for the fibre orientation detection encourages further research for developing the proposed robotic manufacturing system.

The localization of autonomous vehicles requires, accurate tracking of its position and orientation in all conditions. As modern cities evolve localization would require a more precise accuracy that up to the level of centimetre and decimetre. One of the most crucial struggles in global positioning system and inertial navigation fusion is that the accuracy of the algorithm is reduced during GPS interruptions. In recent days bigdata, machine and deep learning offer great opportunities, especially for future smart and industrial 4.0 autonomous applications. This research programme is aiming to investigate and deploy machine and deep learning approach to improve and reach the level of reliability, accuracy and robustness required at low-cost GPS/IMU unit. The programme will also present a tracking platform solution that would compensates the issues of lack of accuracy in existing localization methods. The initial result of this ongoing programme is presented and reported in this paper. The paper also covers the research programme future development plans and milestones.

Smart Factory is a key platform for recent industrial revolution 4.0 and industrial robotic platform solutions using Artificial Intelligence are an integral measure of its cell’s configuration and reconfiguration. There are two different methods of machine learning used in industrial collaborative robotics systems, Computer Vision Machine Learning and Imitation Learning. Computer vision is a classical use of machine and deep learning methods and it needs a complex, expensive resources and is not suitable for various types of manufacturing automation environment. Imitation Learning is the most fascinating method, and the recent evolving industry is interested on it. The main aim of this research programme is to develop a self-learning robotic system platform solution using Machine and Deep Imitation Learning for smart factories’ industrial applications. A self-learning robotic system using deep imitation learning can reduce working time and give a less human error when performing high-precision processes. It can also improve the ability to configure robotic platform to facilitate a more flexible decisions and cost- effective manufacturing.

3D printing of lightweight continuous carbon fiber reinforced plastics (CCFRP) in three dimensions changes the traditional composite manufacturing processes. The continuous carbon fibers reinforced plastic filament can be printed along the load transmission path and significantly improve the strength of composite structures. Compared to the three-axis computer numerical controlled (CNC) machine based printing process, industrial robots provide the possibility to manufacture complex, spatial and large-scale composite structures. Here, the concept to use multi-robot to print complex spatial CCFRP components simultaneously has been presented. More than one 6 degrees of freedom industrial robots can cooperate with each other and solve the contradiction between structural complexity and printing reachability. During the printing process, the deformation of composite structures may happen, especially for the self-supporting components. Thus, in this paper, a Light Detection and Ranging (LiDAR) method is introduced to detect the deformation of printed composite structure and the movements of two UR robots. To obtain the point clouds of the printed structure, a LiDAR camera D435i has been installed on one robot. A new approach by combining coordinate transformation and iterative-closest-point (ICP) algorithm has been developed to merge the point clouds collected from different shooting angles of the camera.

Using a combination of active Radio-Frequency Identification tracking and staff interviews with members from an aerospace manufacturing company, it was uncovered that over 80 hours per week was spent in the manual movement of goods between departments. On a site of over 1000 employees that uses dedicated build cells in separated departments, this mixed-use facility proves challenging for the adoption of an autonomous delivery system due to its narrow corridors and high occupancy, however by investigating the concerns of employees and suggesting low-cost retroactive solutions, this project seeks to justify the transition from manual to automated onsite logistics. The conclusion found that indeed the company does have the transport yields to justify the use of Autonomous Mobile Robots, that the robots would supplement rather than replace workers and that safety was a key factor to address when using robots on a site of this configuration.