Error-propagation Based Geometrical Quality Prediction and Control Strategy for Complex Manufacturing Processes Using Parallel Kinematic Machines

Lead Research Organisation: Queen's University of Belfast
Department Name: Sch Mechanical and Aerospace Engineering

Abstract

UK manufacture accounts for 13% of GDP, 50% of exports and directly employs 2.5 million people. Parallel Kinematic Machines (PKM) are a new type of machine tools and have been identified as a key technology that fills in the gap between computer numerical controlled machines and industrial robots due to their superior dynamic performance, flexibility and versatility to large-scaled parts machining. The use of PKMs creates more flexibility and dexterity in manufacturing processes while achieving high precision and high speed. This contributes significantly to the economy by improving efficiency, reducing product defects, and saving time/money/energy.

The PKM integrated manufacturing system would inevitably introduce errors due to stiffness and motion of the components in the system. These errors will be accumulated through the production chain, and influence the geometrical quality of the machined parts. Predicting part quality based on error propagation in the PKM manufacturing processes represents a step change in managing production processes, as it removes the current cumbersome trial-and-error processes and enables rapid reconfiguration of production systems. Other benefits would include 20% reduction of part defects and rework, leading to a significant cost saving.

Part quality resulted from interaction of manufacturing systems and machining processes, with intertwined machining errors and their propagation through multiple operations, machine tools, and fixtures and jigs. At the moment, there is no robust industrial or international standard to evaluate machining capability of PKM tools with these errors. Current trial-and-error based approach that requires a large amount of time, materials and energy, is not sustainable and suitable for future smart factories to meet frequent changes with reconfigurability. Therefore new analytical methods are urgently needed.

The proposed research is adventurous in creating a new quality prediction capability for PKM based flexible manufacturing processes by revealing the relationship between manufacturing system errors and part or assembly quality. This leads to an effective error discrimination control strategy to achieve a better process control while ensuring the required product quality.

Error propagation in a production process is to be explored by investigating the role of stiffness characteristics of a PKM in influencing the machining process. This will lead to the development of machining load-models in both milling and drilling on a specific machining process. Experiments are to be implemented at QUB's PKM laboratory and KCL PKM laboratory, and a map between errors and part quality is to be created through modeling and testing. This will deliver an enhanced understanding of errors and their propagation mechanism thereby leading to the identification of potential strategies for reducing individual, propagated, and residual errors.

An integrated validation system that consists of a kinematic/dynamic analysis module, kinetostatic model, CAD module, and FEM module will be implemented in a virtual environment and in a manufacturing site. The project will access expertise from world-leading groups in advanced PKM machining processes.

The research is highly transformative in its nature of connecting academic cutting-edge research to the practical issues encountered in complex PKM manufacture processes. Key results are to be generated and fundamental science is to be revealed in the collaborative work, training and workshops with support of AMRC, MTC and Tianjin University. The research will benefit the academic community in manufacture and robotics, and industrial sectors who will gain knowledge for reduction of errors particularly propagated errors in manufacturing processes integrated with PKMs.

Planned Impact

Knowledge impact: The investigators have established worldwide network which will warrant for knowledge dissemination to a much wider community, including researchers in flexible manufacture, parallel kinematic machines, robotics, machine tools, machining, process control, and smart manufacturing. Collaborations with project partners are integral part of this proposal and will contribute to the advance of understanding of machine tool user requirements as well as accelerating the knowledge transfer from academia to industry. A plan has already been in place to ensure the maximum dissemination of the project results, via internet, social media, open-access publications, special issue journals, topic symposium at international conferences, and dissemination events.

Economic impact: The economic impact will be resulted from the utilisation of the new error minimization strategy in manufacturing processes, which leads to efficiency improvement, product defects reduction, and time/money/energy savings. Collaboration with the two UK catapult centres (AMRC and MTC) in high value manufacturing will ensure the fast transformation of the project outcome to industry through tailored workshops at the two centres open to their industrial partners. The investigators will also work closely with the commercial development teams at QUB and KCL to protect and exploit any intellectual property resulted from the project. The expected impact would be 50% rework reduction, 12% reduction on non-conformance management activities, and 20% of cost reduction.

People development: The proposed project will create forefront knowledge in flexible manufacturing technologies, which will be transferred to next generation engineers, through teaching, training, and outreach activities. The researchers appointed to the project will gain not only the cutting-edge knowledge in PKM machine tools and manufacturing processes, but also soft skills such as project management and team working throughout the project. The theoretical advances, models and methods will be integrated into Dr Jin's and Prof. Dai's teaching modules, which will be delivered to their undergraduates and postgraduates. A generic case study will also be developed for both education and industrial training. Collaboration with Tianjin University China and organizing workshop and symposium overseas will create further impact on people at international level.

Societal impact: Using the proposed technology to control error propagation and optimize manufacturing process will lead to energy and materials savings, as well as reduction of carbon emissions. In addition, quality prediction method will help to reduce the cumbersome trail-and-error processes, which will improve the working conditions of shop floor workers. The investigators have rich experience of publication engagement, and activities will be conducted by the investigators and researchers of this project to engage the public through school talks, public lectures, and science festivals, as shown in the pathways to impact.

Publications

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López-Custodio P (2019) Design of a Variable-Mobility Linkage Using the Bohemian Dome in Journal of Mechanical Design

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McKenna V. (2017) Variation model and analysis of spatial assembly with multiple closed chains in Advances in Transdisciplinary Engineering

 
Description A new method is developed for process optimisation based on the variation propagation model. Specially, the cost models are established and first time linked to the variations, so that alternative manufacturing processes could be assessed, and the trade of between part variations, process variations and assembly variations can be analysed. Therefore, this method will help product designers and process planners to understand the variation propagation effects through the entire manufacturing processes from part features to the key characteristics of the final assemblies, in order for them to optimise the tolerance design and manufacturing/assembly processes planning and control.
Exploitation Route - The variation propagation model for over-constrained assembly can be well utilised by the research community in Variation Propagation and Quality control.
- Linking the cost with variations is proposed for the first time, which will open an new avenue of research. Our method of cost modelling accounting variations are useful not only for researchers but also for industrialist, who can apply the method to manage their production cost.
- The developed methodology on cost-oriented process optimisation will be useful for product designers to allocate suitable dimensions and tolerances, and production planners to arrange the fabrication and assembly processes in an optimised manner.
Sectors Aerospace, Defence and Marine,Chemicals,Construction,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Environment,Financial Services, and Management Consultancy,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology,Transport

URL https://www.sciencedirect.com/science/article/pii/S0736584518302357
 
Description The new error propagation modelling method has been applied to aircraft assembly processes and validated via a wing spar assembly from Bombardier Aerospace. Follow-up research has been awarded by Bombardier Aerospace to predict the gap volume in aircraft wing assembly based on our variation propagation modelling method. Other dissemination activities have also been conducted recently, including the plenary speech at IMechE event on Robotics and Automation in Manufacturing in Coventry on 20 Nov. 2018, invited speech in Northern Ireland Expo at Belfast on 13 Feb 2019, and plenary speech at IEEE RAS conference at London on 19 Feb 2019.
First Year Of Impact 2018
Sector Aerospace, Defence and Marine,Education,Manufacturing, including Industrial Biotechology
Impact Types Economic

 
Description Error-propagation Based Geometrical Quality Prediction and Control Strategy (Q-PreMan)
Amount £669,561 (GBP)
Funding ID EP/P025447/1, EP/P026087/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 07/2017 
End 06/2020
 
Description European and Chinese Platform for Stacked Aero-Structure Drilling Process and Equipment
Amount € 859,500 (EUR)
Funding ID 734272 
Organisation European Commission H2020 
Sector Public
Country Belgium
Start 01/2017 
End 12/2020
 
Description Funding from industrial enterprise
Amount £39,368 (GBP)
Organisation Bombardier Inc. 
Sector Private
Country Canada
Start 01/2019 
End 04/2019
 
Title Cost-oriented process optimisation method through variation propagation management 
Description A variation propagation modelling method for overconstrained assemblies, and a novel modelling method to connect variations with production costs were developed. Based on these methods, a novel process optimisation method is created with the ability to analyse the trade-offs between the cost and achievable variation limits of the entire manufacturing chain in order to minimise the overall manufacturing cost. The developed methods will be useful for product designers to allocate suitable dimensions and tolerances, and production planners to arrange the fabrication and assembly processes in an optimised manner. 
Type Of Material Technology assay or reagent 
Year Produced 2019 
Provided To Others? Yes  
Impact - The methods open a new research avenue in the community of Variation propagation by linking to the production costs, as evidenced by the request for our published papers from Prof. D. Ceglarek. - The methods have attracted attention from industry. Bombardier Aerospace Belfast has already awarded us for predicting the gap volume in aircraft assembly based on our developed variation propagation method. 
URL https://www.sciencedirect.com/science/article/pii/S0736584518302357
 
Description Project partner at Tianjin University 
Organisation Tianjin University
Country China 
Sector Academic/University 
PI Contribution We have conducted knowledge exchange by sending my PhD students to Tianjin University, and hosting PhD and ECRs from Tianjin University. Also, we have been collaborating for joint funding applications including the successful EU H2020 grant (Ref No 734272) and composed a number of joint publications. Apart from the research collaborations, Queen's University Belfast and Tianjin University have signed MoA for joint educational programmes (e.g. 2+2), and both universities are members in the government supported UK-China Consortium on Engineering Education and Research.
Collaborator Contribution Prof. Qin's research group in Tianjin University provided drill cutters, helped conduct fatigue tests for the drilled samples, and provided office room and office facilities to host my PhD student (Vincent McKenna), as well as hosting my frequent visit. Prof. Qin also shared knowledge in drilling and helical milling processes, which is useful for composing the joint publication. Prof. T. Huang's group provided the expert advice in design, control and calibration of parallel kinematic machines, which help us to be aware of the state of the art in the field.
Impact Joint publications: 1. https://doi.org/10.1016/j.rcim.2018.12.009 2. https://doi.org/10.1007/s00170-017-0842-8 3. https://doi.org/10.1007/s00170-017-1117-0 4. https://doi.org/10.1016/j.compstruct.2016.09.051 Joint grant awarded: 1. EU H2020 (Ref No 734272)
Start Year 2012
 
Description Project partner: Prof. Jian Dai at King's College London 
Organisation King's College London
Country United Kingdom 
Sector Academic/University 
PI Contribution The Q-PreMan project is funded by EPSRC to support both QUB and KCL team to working collaboratively to address the research challenge. The QUB team focuses on error analysis, modelling, discrimination and optimisation of manufacturing processes, as well as experimental validation and demonstrator development on WP2-6.
Collaborator Contribution The KCL team works on stiffness modelling, error analysis and discrimination of PKM machine tool, and also for experimental test, model refinement and demonstrator development on WP1, 3-6.
Impact It is still early stage of the project. The outputs will be uploaded once some concrete results are obtained.
Start Year 2017
 
Description IEEE UK&I RAS Conference 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact The aim of the annual IEEE UK&I RAS conference is to improve the communication of its members and other researchers, young students and industrial professionals who are interested in the activities of RAS in research, development and education, share the knowledge, latest research achievements and technologies in RAS, and promote collaboration and knowledge transformation. Experts in the UK and broad were invited to give their recent research outcomes and exploration in the RAS area. Poster presentations by research students were also presented.
Year(s) Of Engagement Activity 2017,2018,2019
URL https://communities.theiet.org/communities/events/item/67/39/22594
 
Description IMechE event on Robotics and Automation in Manufacturing 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact I was an invited speaker and panel member in the discussion session in the one day seminar on Robotics and Automation in Manufacturing, which aims to help industrial enterprises to gain insight into state-of-the-art robotic technologies in order to improve efficiency in their production lines, as well as discuss the UK's journey with robotics with engineering managers, robotics specialists, designers and manufacturers.50+ people from industry, research organisations, and universities attended the event. There are great interest from the audience in the field given the context of Industrial 4.0. The best practice and the UK's position as well as relevant policy were also hotly discussed.
Year(s) Of Engagement Activity 2018
URL http://events.imeche.org/ViewEvent?code=SEM6752
 
Description Northern Ireland High Tech Manufacturing & Precision Engineering Expo on 13 Feb 2019 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact I was an invited speaker at the Northern Ireland Expo, which attracted over 4000 registered delegates, comprised of twelve seminar sessions and many stand exhibitions. The aim of the expo is to showcase the innovative approaches in practice and disseminate the cutting edge research. I gave a talk on Variation Management for Process Optimisation in Complex Assembly, where about 40 people attended. The audience echoed my presentation very well and they were very interested in the topic.
Year(s) Of Engagement Activity 2019
URL http://www.northernirelandmanufacturing.co.uk/
 
Description Sir Bernard Crossland Poster Competition 2017 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact Sir Bernard Crossland Poster Competition is an annual event for all PhD students in School of Mechanical & Aerospace Engineering at QUB to present their work both orally and in a poster format. The presentation takes 10 minutes followed by a number of questions mainly from academic staff, and the event was attended by about 50 people including postgraduate students and academic staff. The posters will be exhibited for a week before academic staff vote in order to rank them based on their quality. About 100 people including both students and staff will view these posters during the exhibition.
Year(s) Of Engagement Activity 2017