Ebook: Advances in Manufacturing Technology XXXIII

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. To achieve this, 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 Huddersfiled

1990 –

1991 Hatfield

1992 Central England

1993 Bath

1994 Loughborough

1995 De Monfort

1996 Bath

1997 Glascow Caledonian

1998 Derby

1999 Bath

2000 East London

2001 Cardi

2002 Leeds Metropolitan

In 2002 the conference was accorded the title International (ICMR) to reflect the current trends in manufacturing engineering and to promote the exchange of research and engineering application experiences internationally. The ICMR, has since its introduction, incorporated the NCMR. The 17th ICMR incorporates the 34th NCMR. The host universities for ICMR have been:

2003 Strathclyde

2004 Sheffield Hallam

2005 Cranfield

2006 Liverpoor John Moores

2007 Leicester De Montfort

2008 Brunel

2009 Warwick

2010 Durham

2011 Glascow Caledonian

2012 Aston

2013 Cranfield

2014 Southampton Solent

2015 Bath

2016 Loughborough

2017 Greenwic

2018 Skövde, Sweden

2019 Queen’s University Belfast

This paper studies the real-time acquisition method of machine tool processing data and analyses the collected data in different actual application scenarios in order to solve the general problem of insufficient utilization of CNC machine tools. The most widely used communication protocols, OPC UA and MTConnect, are discussed and used for machine tool information collection. With the help of data analysis algorithms, five machine tool status indicators are proposed to comprehensively evaluate machine tools and presented in the form of radar maps which is useful for users to quickly get the current status. These achievements are applied for the on-line monitoring system of machine tools, which improves the utilization of machine tools.

Smart Systems (SS) are being seen as an approach towards achieving significant improvements in manufacturing productivity. However, there is little evidence to indicate whether UK manufacturing SMEs are prepared for the implementation of such systems. The purpose of this research is to explore the applicability of Smart Systems in UK manufacturing SMEs, and to identify the key priority areas and improvement levers for the implementation of such systems. A two-stage research approach is adopted that includes a questionnaire and, visits to 36 manufacturing SMEs. The questionnaire and interviews are guided by the development and application of a unique measuring instrument developed by the authors for this study that focusses upon measuring the preparedness of companies towards implementing Smart Systems. This tool enabled the research team to identify a number of key issues and suggests that the current turbulence in the industry could be bringing manufacturing SMEs closer to the adoption of such systems.

With the advancement of information and communication technology, manufacturing organisations are adopting digital manufacturing more than ever before. This gives rise to cloud-based manufacturing through which manufacturers can sell their production capabilities as an on-demand service termed as Manufacturing-as-a-Service (MaaS), instead of selling pre-defined products. Existing MaaS providers (e.g. online 3D printing providers) offer manufacturing customised products. But, customers have more demanding needs, such as rapid turnaround time, quality product, innovative design, etc. To meet such requirements, a Smart Manufacturing-as-a-Service (SmartMaaS) framework has been developed. In order to deliver efficient products at optimal cost, SmartMaaS can take smart actions such as negotiating with manufacturing centres (e.g. 3D printers) with respect to their availability, turnaround time, manufacturing cost, etc. In this paper, SmartMaaS is introduced and demonstrated via a prototype that is capable of accepting customers’ product request in the form of “design genes” and manufacturing 3D printed products through an actor-based system.

Manufacturing Industry is moving towards adoption of 3D models as the ultimate authoritative source for complete product definition replacing 2D drawings, which is called “Model-Based Definition”. Starting its journey from geometric information on design, manufacturing, and inspection, the targets are to achieve the ultimate goal of lifecycle model based enterprise, requiring MBD to be more comprehensive and challenging structure of information instead of just a geometric model. The industry has not yet fully achieved implementation of MBD to whole product lifecycle. This journey is long and tough, and we are still at an early stage, but it will be a decisive factor in gaining competitive advantage by the early adopters, especially in high-value manufacturing. Complete adoption of MBD has several issues and challenges that need to be addressed. This paper presents a review of current literature, intending to cover present state of knowledge, issues, challenges, and future research directions, in the development and adoption of MBD.

Motion control capabilities within rotational moulding machines have remained relatively unchanged for the past 60 years. However, driven by Industry 4.0, this is showing signs of change. The potential of robot-based rotational moulding machines, which can provide more agile movements and rotations, is beginning to be realized. This paper proposes a new novel desktop sized rotational moulding machine that can provide the user with more flexibility and control over mould rotation by incorporating a smart control system. This lifts the current restriction with conventional machines where the rotational speeds of the two motors remain constant throughout a cycle. Our recent research with this new machine showed that varying the rotational speeds throughout the cycle can improve product quality and process efficiency.

Cloud enabled systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and cloud. To overcome the limitation, this paper presents an innovative fog based system for diagnosing and optimizing machining processes. The system includes: (1) dynamic diagnosis – Convolutional Neural Network (CNN) based diagnosis is implemented to detect potential faults from customized machining processes; (2) a fog enabled architecture for diagnosing and optimizing machining processes – it consists of a terminal layer, a fog layer and a cloud layer to minimize data traffic and improve system efficiency. Under the architecture, machining processes are monitored on the terminal layer and monitored signals are processed using the trained CNN deployed on the fog layer to efficiently detect abnormal situations. Intensive computing activities like training of the CNN and system re-optimization responding to detected faults are carried out dynamically on the cloud layer to leverage its computation powers. The system was validated in real-world production. In comparison with a cloud system, this fog system achieved 70.26% reduction in bandwidth between shop floors and cloud.

Mirror milling has become an effective way to achieve high quality and green processing of large cylindrical thin-walled parts for aircraft parts like rocket fuel tanks. During processing, the feed rate ratio of milling cutter and support head is changing, which will directly affect the quality of workpiece. Both extending a virtual length of cutter and deducing the parametric equations of speed of the end effectors based on true cutter length could make the feed rates of end effectors keep a ratio to stay mirror symmetrical to workpiece. Then we can get the relationship between feed rate of end effectors and the speed of dual-robot joints based on kinematics analysis, and mirror milling could be realized by controlling the joints parameters of dual-robot directly. At last, the effectiveness of the feed rate planning of mirror milling is verified by simulation and experiment.

With the soft polishing tools being compressed to fit the shape of the complex curved surfaces and contacting with the workpieces compliantly, industrial robots of multi-joint could do a great job in the process of polishing. However, the present control system of industrial robots doesn’t have the functions to control all the cutting variables of curved surfaces polishing. The cutting variables, which have to be adjusted all the time in the process of curved surfaces polishing, are mainly three variables, the contact force, the relative speed and the staying time. And among them, only the staying time could be adjusted by programming in the control system of industrial robots. Therefore, other cutting variables of curved surfaces polishing have to be adjusted in other ways. In this paper, a ballonet polishing tool and its control system are developed to work along with a multi-joint industrial robot in the process of a workpiece of aspheric surface polishing. Consequently, it studies how the tool control system works along with the robot control system.

In addition to production-oriented robots in industry, service robots with social skills can also perform a role in production environments. They can communicate naturally (e.g., verbally) with human workers, providing on-demand ancillary services that support primary production activities. In this paper, a guide robot that provides way-finding services within an industrial facility (such as finding a person, a resource or a place) is presented. Requirements for such a robot are proposed and an early prototype of a robot with indoor navigation skills and voice interpretation ability (via a paired mobile device and app) is described. A user study is also reported, in which the user experience (UX) obtained from interacting with the robot and receiving the service is assessed. The results of said study reveal that usability was very high but overall UX was improvable, due to slow navigation speed, limited situation awareness and simplistic interpretation of voice petitions.

The high demand of efficient large scale machining operations by concurrently decreasing operating time and costs has led to an increasing usage of mobile robotic systems. This paper introduces a mobile robotic system which is consisted of a TriMule hybrid robot on an automated guided vehicle, and a fringe-projection-based measurement system. The TriMule robot is suitable to be built on an autonomous platform for multi-station manufacturing in situ because of its desirable performance in terms of rigidity, accuracy, work envelop and reconfigurability. In order to increase the absolute accuracy of the mobile robotic system, the fringe-projection-based measurement system obtains high accuracy and high density cloudy to measure the position and orientation of the robot and workpiece in relation to each other. This system is suitable for large scale manufacturing in situ, drilling, riveting and high-speed milling for example.

In order to increase absolute positioning accuracy, a 3D-vision sensor which can be integrated into machining robot is introduced and a calibration method to determine the position and pose relationship between 3D-vision sensor and robot is proposed. It only requires the sensor to scan a standard sphere with known radius at several different robot poses and then the transformation matrix between 3D-vision sensor and robot can be optimized by a two-step algorithm. First, the solution of transformation matrix can become a constrained least square estimation problem, which is solved by Lagrange multipliers. Then, the transformation matrix together with pose data of robot involved for solving can be further refined by a nonlinear optimization procedure. The experiments are conducted on a 5-DOF hybrid robot named TriMule to test the validity of the proposed method.

In the context of Industry 4.0, Cyber-Physical Production Systems (CPPS) and digital twins are key technologies for the management of huge amount of data generated by Industrial Internet of things (IIoT) devices. However, it is still an issue to make heterogeneous components interoperable and make them flexible to each industrial specific needs. Thus, this paper aims to identify the components and needs of Industry 4.0 and proposes a database architecture allowing to fulfill these requirements. The proposed architecture is implemented on a cyber-physical production system (CPPS) and its applicability is showed and discussed through several use cases.

Thus far, the aerospace industry has floundered in the uptake of automation compared to the automotive and high volume electronics manufacturing industries. This may partially be attributed to the lifecycle of many aerospace products whose components typically begin production at low to medium volumes. This environment often limits the adoption of automation, as well as proving to be an uncertain environment in which to base large capital investments. Following increases in demand, low volume processes are often found unsuitable for higher volume production, with potential re-design activities prevented by costly product qualification processes. Thus, manufacturers are forced to enable low volume processes to cope with high volume production, without product redesign for automated manufacturing, whilst remaining commercially competitive. This paper aims to investigate a world leading aerospace electronics company, to recommend an implementation framework for automation in the reduction of build cost and increase in production volume devoid of product redesign activities.

This paper presents a real-life example of using industrial robots to automate the testing procedures of aerospace products. Three different scenarios are proposed as potential solutions to allow a Mobile Manipulator to help automate the environmental testing process. An overview of different simulation and Off Line Programming (OLP) software packages are presented and discussed as methods to test and validate the scenarios. A framework is presented consisting of a product data model to be used for the identification of the different products and a unified modelling system is introduced to allow information exchange at the operational level. The focus of this paper and future work is towards integrating all the elements of the proposed framework into a human machine interface to create a collaborative process involving human, robots, machines and products.

The use of mobile manipulators for transport tasks has provided solutions to some flexibility problems in manufacturing systems. Mobile manipulators are mobile entities equipped with robotic arms for loading and unloading parts and a mobile base for transport. It can be modular, where the two entities work together or separately. The existing task assignment approaches for mobile manipulators do not take into account the balancing of robots especially when the robot can change their capacity thanks to their modularity characteristic. In this paper, we demonstrate the efficiency of the bidding mechanism for the assignment of task to robots in order to increase the balancing for the robots use.

In this paper, we discuss the concepts of a flexible and high-performing solution for automatic quality control that integrates state-of-the-art machine learning algorithms with collaborative robots. The overall aim of the paper is to take the first steps towards improved automatic quality inspections in the manufacturing industry, leading to reduced quality defects and reduced costs in the manufacturing process. For developing and evaluating a first version of a solution that integrates state-of-the-art machine vision and collaborative robots we use a real-world case study focusing on improved quality inspection. Results from the case study shows that it is possible to realize automatic quality inspections through the use of a collaborative robot as intended, but also that there are some challenges that need to be further addressed in order to achieve a top-performing system.

The application of multi-rotor UAV in vision has been developed rapidly in recent years, and multi-rotor unmanned aerial vehicles (UAV) can’t be widely used without the aid of a visual system. The application of visual technology in UAV has attracted more and more attention with the continuous development of image processing and environment reconstruction technology. Getting accurate depth map from the stereo image has always been the pursuit of researchers. Stereo matching is the most important part of stereo vision. An improved matching algorithm is proposed to solve the problems of high image noise and low image accuracy in binocular vision imaging of multi-rotor UAV after binocular information fusion. Experimental results show that the proposed algorithm has advantages in accuracy compared with other algorithms. The validity and reliability of the proposed stereo matching algorithm for multi-rotor UAV based on binocular vision are verified by indoor simulation tests and real environment tests of a UAV.

The affordability of high technology inspection systems for Micro, Small and Medium Enterprises (MSME) is a big challenge since, if not implemented, it will affect productivity and quality of the product. A case study has been carried out to understand inspection practices at an MSME in India (at an orthotic footwear company), within its medical device industry where safety and risk management are critical. Raw materials are not inspected during the initial stages of production, due to which the whole product is rejected if any defect is subsequently found. Due to this rejection, manufacturing time and man hours are squandered at the expense of quality. To enable early inspection that is also economical, existing methodology is integrated to develop a system that can boot from a Next Unit Computing (NUC) with real time processing. The inspection system uses Fault Detection and Isolation (FDI) technique using ’canny edge detection algorithm’ for image processing; defects are identified as blobs by convolving the image matrix. The images are captured by low cost off-the-shelf cameras arranged in-line perpendicular to the bespoke set up. A set of light sources is used for illumination and the algorithm is evaluated for its accuracy under different light conditions.

This paper presents a novel technique to overcome disturbances (elastic saturation/pneumatic) in slave side of tele-operable gesture control soft actuator using a Kalman filter (KF) based approach. The experimentation setup is consisting a master side (MS) that tracks human finger movements. Slave side (SS) is a pneumatic soft actuator made from an elastomer material. Duplex communication is established using MQTT (Message Queuing Telemetry Transport) server. In this research, KF is implemented in SS to overcome elastic saturation and other disturbances occurred due to pneumatic control devices. MS tracks the index finger movement of the human operator using a Data Glove (DG) and sends the bending angles via MQTT to SS. SS also tracks its bending angle using a flex sensor. We have previously encountered disturbances (seen in SS flex sensor) in the soft actuator (due to elastic saturation and pneumatic control). Therefore, we have used a discrete KF model to improve the real-time response of the soft actuator. This technique is tested in a vertical setup for gripping of a round shaped object and disturbances are studies under different conditions. The recorded disturbances/delays (as published in our previous findings) is significantly improved (error reduced by 19.06 %) after introducing this approach.

Flexible subsea risers are highly complex structures with multiple layers and varying material types. They are being used worldwide for oil and gas extraction in deep water. The complexity of these flexible risers and the hostile conditions in a deep-water environment present major challenges for non-intrusive, in-service inspection. This paper presents a novel automated radiography inspection system to detect defects from X-ray images of such flexible risers. The concept of a robotic digital X-ray scanning system which addresses the needs and challenges of deep-water flexible riser inspections has been studied. The proposed radiography inspection system is an ideal solution for flexible risers as it penetrates through all the layers in the riser structure, providing detailed information on any damage to the various layers. The system integrates the advanced technologies of image processing, machine learning, and robot crawlers to automatically detect abnormal textures such as erosion, corrosion and strand damage, foreign objects and other critical features from flexible risers. The performance of feature extraction, feature normalization, image processing, and machine learning for the purpose of defect detection have been tested and analysed. With smart integration and enhancement of different techniques, the limitations of each algorithms have been mitigated while the overall performance has been improved significantly. The detailed stages of the implementation are also discussed. The key features of the subsea riser inspection system are: (i) Real-time radiographic inspection result, (ii) Robotic system adaptable to various pipe types and sizes, (iii) High accuracy and reliability.

This paper focuses on the topic of trajectory generation of lower limb exoskeleton by a proposed central pattern generator (CPG) algorithm. By combining oscillators, i.e. dynamical system that exhibit limit cycle behavior, a CPG model for lower limb exoskeleton is proposed. Such a model can generate kinds of trajectory and achieve their smooth transition. Moreover, in order to realize the synchronization between the user and exoskeleton, an additional module is introduced which acts as a waveform learner of arbitrary periodic signal. Numerical simulations are carried out, we demonstrate that this module is robust and has good performance in generating trajectory.