Ebook: Advances in Manufacturing Technology XXXII

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 since the late 1970s, the conference is renowned as a friendly and inclusive environment that brings together a broad community of researchers who share a common goal; developing and managing the technologies and operations that are key to sustaining the success of manufacturing businesses. For over three decades, ICMR has been the main manufacturing research conference organised 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 documents and other initiatives. COMEH is represented on the Engineering Professor's council (EPC) and it organises 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 De Montfort

1996 Bath

1997 Glasgow Caledonian

1998 Derby

1999 Bath

2000 East London

2001 Cardiff

2002 Leeds Metropolitan

In 2002 the conference was accorded the title International to reflect current trends in manufacturing engineering and to promote the exchange of research and engineering application experiences internationally. The 16th ICMR for the first time is being held outside the UK. Previous host universities for ICMR:

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 Cranfiel

2014 Southampton Solent

2015 Bath

2016 Loughborough

2017 Greenwich

2018 Skövde, Sweden

Motion control of rotational moulding is an area which has not been extensively investigated beyond the current trial and error approach. Different motion control schemes will lead to varied powder flow regimes which exhibit different levels of mixing and temperature uniformity. Rotational moulding is hampered by long cycle times. Choosing the ideal rotational speed should allow the optimum use of the inputted heat energy to cause all of the powder bed to become ‘tacky’ and adhere to the mould wall in the shortest time period. This paper investigates the hypothesis that the optimum rotational speed should be chosen by considering the flow regime of the powder bed at the bottom of the mould, in order to reduce manufacturing time and energy consumption. Experiments were completed to validate this approach using a cylindrical mould under uni-axial rotation. Results show that the ideal rotational speed is found when the powder is flowing in a rolling regime and suggests that further cycle time savings could be achieved by varying the speed during the heating cycle. The effect of rotational speed on part quality (wall thickness uniformity) was also investigated.

Laser drilling is a non-conventional machining process which is widely used in automotive, electronics and aerospace sectors to produce holes in diverse range of materials. Different types of lasers and methods are available to produce various hole geometries. Big number of researchers have examined several ways to enhance the performance of this process by investigating different process parameters and drilling methods, that seek improvement of the drilled hole quality. Whereas, productivity and operating cost are also important factors which need to be evaluated along with drilled hole quality. Reducing the drilling time can improve productivity and the selection of a suitable laser can save operating cost which will benefit laser processing industries in this global competitive environment. A case study was performed using different lasers for single pulse and percussion drilling. A significant improvement in productivity was observed with the use of a high power laser that is subject to high operating cost.

Fatigue life of machined parts strongly depends on their surface condition. The rotating bar bending fatigue testing method is widely used to obtain the fatigue behavior of metallic materials due to its simplicity. In this work, the methodology for the design, manufacturing and setup of a fatigue test bench is exposed. The main novelty lies on the reuse of several elements from an old parallel lathe, currently out of order, and their use to manufacture some parts for the test bench. In this way, a double objective is achieved: high quality elements are recycled and the machine manufacturing cost is reduced.

This paper proposes a simple and practical method that facilitates capture of the most significant sources of variation within a craft-based textile manufacturing operation. With the aim of reducing waste in such processes and improving the consistency of the fabric finish, this research work establishes a method that helps indicate the degree to which variation sources contribute to the variability noticed in finished woven fabric. This is achieved by a methodology that is based on the combined use of process modelling theory, specifically the integrated definition for function modelling (IDEF) and process expert knowledge.

In recent years, the pharmaceutical industry is seeing a movement towards the implementation of more efficient continuous manufacturing. This shift requires the development of in-line process analytical technologies to monitor and control the process at any given time. However, extracting reliable information from these sensors is a challenge. Among the available technologies, in-line image analysis is quickly gaining importance. This work presents an image analysis framework developed to address one of the main challenges of in-line image analysis: the presence of out-of-focus particles. Through two relevant examples such as the characterisation of a system of microparticles of mixed shapes and the monitoring of a common operation in the pharmaceutical industry such as the wet milling process, the benefits of incorporating this technique are assessed. The real-time analysis of imaging data in combination with other simultaneously-acquired quantitative data streams enables the user to make informed decisions and implement enhanced control strategies.

In the last decades, micro manufacturing was driven by micro-electro-mechanical systems (MEMS), where well-established manufacturing methods based on semiconductor technologies are able to produce structures in miniscule dimensions. Often, such modern electronic devices offer high level functionality in reduced space. However, such components may be impaired in several ways during fabrication and assembly stages resulting in damages or/and structural failures. To enable inspection of MEMS components, new technologies are needed to ensure reliable quality control in particular, in medical/aerospace industries where 100% quality inspection is required to achieve highest safety standards. In this paper, an outline of the inspection system architecture that can be applied to inspect MEMS component during the production phase using plenoptic camera and x-ray will be described. Preliminary test results demonstrate the system applicability. The inspection system aims to achieve an autonomous, reliable and accurate solution to reduce the production costs.

During end milling operation, the residual stresses are developed from two sources: plastic deformation of workpiece material and thermal energy generated. These two sources of residual stresses are often combined as one toward prediction of the efficient combined milling parameters which consequently minimised the residual stresses induced in the material. Hence, a mathematical model for predicting the magnitude of induced-residual stresses during end milling of 304L stainless steel was formulated, using analytical approach. The formulated model captured both mechanical and thermal (thermo-mechanical) stresses, which play a significant role during material deformation prior to fracture. The model was simulated with MATLABTM software. The mill cutter has a nose radius of 0.4 mm and operated at a constant cutting speed of 3 m/min. The simulation results showed that when the depth of cut was increased from 0.1 mm to 0.4 mm, the resultant residual stress varied from 150 MPa to 500 MPa, respectively. Evidently, the value of the residual stress value recorded same in both xx and zz-directions, at a particular depth of cut. However, the residual stress decreased exponentially as it approached zero under the surface of the material. Therefore, this model is capable of predicting the residual stresses induced during end milling operation, depending on the material (workpiece) properties, tooling material and selected end milling parameters.

Turning is the most commonly available and least expensive machining operation, in terms of both machine-hour rates and tool insert prices. A practical CNC process planner has to maximize the utilization of turning, not only to attain precision requirements for turnable surfaces, but also to minimize the machining cost, while non-turnable features can be left for other processes such as milling. Most existing methods rely on separation of surface features and lack guarantees when analyzing complex parts with interacting features. In a previous study, we demonstrated successful implementation of a feature-free milling process planner based on configuration space methods used for spatial reasoning and AI search for planning. This paper extends the feature-free method to include turning process planning. It opens up the opportunity for seamless integration of turning actions into a mill-turn process planner that can handle arbitrarily complex shapes with or without a priori knowledge of feature semantics.

The paper presents an investigation on surface quality in terms of surface roughness when micromachining of nanoclay/polyester (PE) and graphene/epoxy nanocomposites. Micro-milling experiments were performed at levels of nano-filler weight fraction, feed rate and cutting speed. Contact stylus was employed to measure surface roughness of the machined surfaces. Experimental results show an improvement of surface quality due to the presence of nano-fillers. Maximum Ra measured on graphene/epoxy surfaces was around half of the maximum Ra of the nanoclay polyester machined surfaces (0.9 μm).

Graphene and halloysite nano-clay have been employed as reinforcements in polymeric nanocomposites due to their superior properties such as high strength, thermal conductivity while epoxy and polyester (PE) are the most popular polymers that have been widely applied in industry as structural material. In the present study, GNP and halloysite nano-clay were used as nano-fillers to reinforce epoxy and polyester matrix, respectively. Micro end milling trials were conducted on a 3-axis ultraprecision CNC machine tool and cutting forces were measured. Higher cutting forces were recorded when cutting graphene/epoxy specimens compared with the nano-clay/PE at higher cutting speed of 188.5 m/min.

Remanufacturing of high-value engineering structures is set to become an important aspect of the future manufacturing industry. However, this depends on the ability to accurately, and rapidly inspect used components for damage, such as corrosion. Visual inspection in both manufacturing and remanufacturing is often performed manually, which is a time-consuming, subjective process. This paper looks at the application of machine learning to the automation of visual inspection for remanufacturing. A Gaussian mixture model is trained on a novel set of image features, specifically designed for the task of corrosion detection in used parts. The probabilistic model is used to segment images of automotive engine components into corroded and non-corroded areas. It is possible that the uncertainty in this segmentation may be used to automate further inspection.

Titanium and its alloys have been widely used in the automotive, biomedical and aerospace industries due to their good strength-to-weight ratio and corrosion resistance. They are considered as difficult-to-machine materials i.e. Titanium and its alloys possess poor machinability. The experimental work reported in the present paper attempts to enhance the machinability of Titanium Grade 2 (a good candidate for bio-implants) under the influence of minimum quantity lubrication at high speed conditions. In this work full factorial technique has been adopted to design and conduct the machining experiments (27 Nos). The paper details the experimentation, optimization, and effect of machining parameters on surface roughness and tool wear during MQL assisted high speed machining of Titanium Grade 2. Investigation reveals significant effect of machining parameters under MQL environment on surface roughness and tool wear. Machining at optimum combination of parameters resulted in precision finish with average roughness value 0.67 μm and maximum tool flank wear value 0.210 mm. The outcomes of this investigation identify MQL as a sustainable substitute of conventional wet cooling for enhanced machinability of Titanium Grade 2 at high speed conditions.

Nowadays, solid lubricants are being considered as sustainable alternate to the conventional cutting fluids. Solid lubricants such as graphite, molybdenum disulfide, and boric acid etc. play vital role to achieve sustainability, productivity, and surface quality in machining. This article presents an introduction to solid lubricants and lubrication technique, highlights the use of solid lubricants in the machining of difficult-to-machine materials, and reviews some previous articles focused on solid lubricant based machining. The article aims to facilitate the researchers working in the field to conduct further research and development.

Nickel Titanium (NiTi) shape memory alloy is a prominent material for biomedical implants. Machining of shape memory alloy is challenging and requires intervention of sustainable techniques to produce quality products with minimum environmental footprints. This paper details the results of experimental investigation conducted on MQL assisted high speed machining of shape memory alloys. It reports the effect of MQL parameters on surface roughness and tool wear during turning (at speed 90 m/min) of NiTi shape memory alloy. Experiments are conducted based on Taguchi's robust design of experiment technique with L9 orthogonal array. A rhomboid-shaped simple carbide tool is selected for experimentation. Green lubricant which is a blend of natural, synthetic and sulphurized esters is used as MQL fluid. The three important MQL parameters of flow rate, air pressure and nozzle distance are each varied at 3 levels. Parameters are optimized to secure the optimum combination producing best surface finish (Ra~1.39μm) and tool flank wear 1.6 mm.

Although Electrical Discharge Machining (EDM) is essentially a material removal process, it has been possible to use this technique for surface alloying of the work materials. Traces of additive powders in the dielectric medium have been found in the machined surface. Conductive powders in the dielectric affect the surface finish and micro-hardness. Experimental investigations were carried out for improving the surface properties of Oil-Hardening Non-Shrinkable die steel by graphite tool electrode and by graphite powder mixed in the dielectric. Results show increase in the percentage of carbon in the machined surface from 0.82% to 1.16% by graphite electrode and from 0.82% to 4.11% by graphite powder. SEM micrograph shows smooth surfaces devoid of any craters and nodules of free graphite. Increase in micro-hardness of the machined surfaces is 12.6% and 22.5% respectively. It can be concluded that it is possible to deposit carbon by this method of machining.

This paper shows the work completed by Progressive Technology to improve the design process for safety critical end use components produced using additive manufacturing. Currently a designer relies on a generic material model, which may not represent the material and machine characteristics of the intended additive manufacturing process. There has been much research concerning the effect of weld plume control in laser beam welding with agreement that failure to remove the weld plume effectively has a detrimental consequence on part quality. A new Complex Laminar Flow Nozzle, was investigated with the intention of controlling the removal of the weld plume from above the weld pool, was compared to a baseline for both Inconel 625 and Inconel 718. The data gathered was utilised to improve the accuracy of a simple FE simulation. Recommendations on the most cost-efficient way to collect material properties and correlate an FE model with real world data to establish accurate FE models that inform intelligent design are also made.

Hyperspectral Interferometry (HSI) is a recently-proposed technique for measuring 3-D point clouds from an opaque object in a single shot. We propose a new application of HSI enabling single-shot 3D surface measurements of optically rough surfaces commonly found on additively manufactured and machined components. Using an additively manufactured sample, single-shot surface profiles were taken at a fixed distance to capture and reconstruct the surface profile. This enables the single-shot measurements of rough surfaces over many independent channels in a short time.

Poly(methylmethacrylate), PMMA, is one of the most commonly used thermoplastics for the manufacture of micromechanical and microfluidic devices, due to its optical transparency, rigid mechanical properties, low cost and good workability in conjunction with its rapid prototyping and mass manufacturing. Recent advances in the rapid-prototyping fields have allowed the production of precise features compatible with microfluidic structures and accelerated the conversion process from bench-side to mass market. For example, to address the need for fast design cycles using material compatible with mass manufacturing, we have developed an ultrafast prototyping technique for the manufacture of multi-layer PMMA micro devices (doi:10.1007/s10404-016-1823-1) and described a method to choose the right PMMA for this prototyping technique (doi:10.3233/978-1-61499-792-4-181). PMMA is a petrochemical-derived material and the rising demand for single-use disposable devices will inevitably result into increased medical plastic waste. To address this problem at the design/prototyping stage, we explored the possibility of utilizing recycled PMMA (Re-PMMA) as the substrate material in our technique. The aim of this work is to compare commercially available recycled PMMA (Re-PMMA) with pristine PMMA (pPMMA) in conjunction with our prototyping technique. The information reported here will provide a practical guide to researchers when selecting Re-PMMA material for a more sustainable approach to micro-engineering and microfluidic rapid-prototyping.

Whilst Additive Manufacturing (AM) has evolved to offer a wider choice of materials, there is little in literature regarding the detailed mechanical properties of these materials. This research focuses on one of the stronger materials, a clear UV curable photopolymer resin used in a material jetting machine. The authors would like to extend their research work, which has looked at tensile properties, to investigate the behaviour of rigid additive manufacturing materials in terms of their shear strength and behaviour. Destructive testing will be based on small plate assemblies, held together by AM nuts and bolts. A tensile testing machine provides the required force that will act in a shear line across the bolts. The paper will review the distribution, levels of variance and consistency of results. The study will look at single bolts and multiple bolt arrangements. A review of the validity of different theories of failure will be applied to the results, including a comparison to results obtained by the finite element analysis technique. This paper aims to add valuable findings to observe AM parts being used in a fabricated assembly, rather than testing materials purely in specimen form for laboratory test data.

Selective Laser Melting has been increasingly applied in industrial manufacturing due to a high density of printed products and more free design. However, a jungle of factors affects processes such as machine characteristics and materials properties. Moreover, ranges of crucial factors such as laser power, laser scanning speed, layer thickness hatch distance are wide. Therefore, it is difficult and requires much testing time and cost to select a suitable process parameter for manufacturing a desirable product. An Artificial Neural Network was applied to build an optimization system for finding out optimal process parameters. Inputs of the system are desirable properties of a product such as relative density ratio, and surface roughness while outputs are laser power, laser velocity, hatch distance and layer thickness. Applying the system not only requires less pre-manufacturing expenditure but also helps the printing users to choose approximate process parameters for printing out a desirable product.

Additive Manufacturing (AM) is considered as an essential technology for the new industrial revolution, the use of such technology helps reducing considerably the prototyping cost and complexity. Adding to its recent appearing, the powerful advances of this technology allowed introducing several new tracks, like the completely AM However, the AM suffers from several issues (printing quality, robustness, efficiency, etc.), thus delaying its large-scale adoption in industry. One of the most considered issues in AM is production reliability. We present a data-based predictive analysis approach for detecting printing failure in AM (3D printer). The proposed approach is based on sound analysis to predict the printing conditions. The goal is to predict the last state of object in press (conforming objects or not) based on previously extracted features from collected sounds.

Additive Layer Manufacturing (ALM) is an advanced technology to produce quality gears of metals and plastics. Some significant benefits such as capability to handle complex gear shapes and design, and produce near net-shaped gears; resource efficiency; and rapid product development etc. make this process a sustainable alternate to the other processes of gear manufacturing. This paper sheds light on the development of some of the important additive layer manufacturing processes such as Stereolithography, Fused Deposition Modeling, and 3D Printing to manufacture gears. The article aims to facilitate researchers and encourages them to do further research and development for improved gear quality, process productivity, and sustainability.