jump to content

ASTUTE 2020

Respond to the Current Lockdown Challenge: Time to Reflect, Analyse and Improve your Manufacturing Production with ASTUTE 2020

Respond to the Current Lockdown Challenge: Time to Reflect, Analyse and Improve your Manufacturing Production with ASTUTE 2020

24/06/2020


ASTUTE 2020’s focus is on technologies related to manufacturing, and our academic and technical expertise utilises a multi-disciplinary approach that encompasses research into the product’s whole life cycle, allowing companies to develop sustainable products and services through integrated applications of knowledge, information, equipment, materials, and people.  Connecting with ASTUTE 2020 remotely can help your business to stimulate ideas and address any current manufacturing challenges you may be facing. In this article, we find out about Robust Quality Methods and how it is critical to efficiency.

What Is Robust Quality Engineering and How Does It Work?

Robust quality engineering is a quality control technique which improves the manufacturability and reliability of a product or process, reducing product development and whole-life costs. Based on the Taguchi Design of Experiments (DoE) approach which has its foundation in the Statistical Factorial DoE method, it involves conducting experimental builds to reduce risk in the long term. If the effect different variables have on a system in a manufacturing site is unknown, these methods can identify those effects, allowing you to control or mitigate them.

Quality can be difficult to define, but all manufacturers will be aware that poor quality will cost them and their customers. One way to think of a product’s quality is to think of its features and how well it conforms to those features. In this case, a variation in quality is a result of a variation in any parameters which affect its conformance to those features. These parameters are categorised as controllable – variables which we can change – or noise – random variables we cannot change.

Through conducting designed experiments, the performance of a system and the interaction between controllable and uncontrollable noise variables can be understood. This information can be used in the product development stage to deliver a product which is robust – operating safely within the limits of those variables. The result is a product or process which will reliably perform as expected because it is not sensitive to the effects of sources of variability, be they environmental, mechanical, or any other. Similarly, processes within a manufacturing line can be robustly designed to ensure that they operate far from the limits of failure.

What Are the Opportunities and Challenges Around the Application of Robust Quality Engineering?

Opportunities

Robust quality engineering can:

  • Reduce product development costs and lifetime operating costs.
  • Provide robust product designs allowing products to be used in new environments.
  • Be implemented on-line to diagnose and eliminate the cause of variation during production.
  • Reduce the number of defects coming off production lines.
  • Be implemented off-line to design robust products and processes by pre-emptively identifying causes of variation.
  • Provide a deeper understanding of manufacturing processes or product uses.

Implementing robust quality engineering poses challenges as well, including:

  • Initial experiments will produce unusable parts.
  • Controlling some parameters, during production or in the field, can be challenging.

What Are the Benefits/Impacts of Incorporating this Technology?

These technologies allow manufacturers to minimise defects and be confident in the performance of their product and processes even if there are variations in the conditions they operate under. The benefits include greater customer satisfaction through tighter control of quality.

The use of these tools, in combination with the range of technologies used in an Industry 4.0 environment, allows manufacturers to have greater insight into the performance of their product and processes. Combining robust parameter design with technologies such as cloud computing, machine learning, and advanced manufacturing allows greater control of manufacturing processes with less intervention needed, and even no physical presence at all.

Can ASTUTE 2020 support companies with this Technology while working from home?

Manufacturing companies are currently having to change how they operate in reaction to the Covid-19 pandemic and lockdown complications. Whether that is adapting processes to allow for safe physical distancing, adapting products to serve new markets, or temporarily stopping production and re-opening on a made to order basis, ASTUTE 2020 can support companies with research into how to incorporate robust quality engineering into their operations. Connecting with ASTUTE 2020 remotely can help your business to stimulate ideas and address the current manufacturing challenges you may be facing, providing you with a competitive advantage. 

Often in manufacturing organisations when production is in full flow, manufacturers don’t have time to review and reflect on continuous improvement aspects of products and processes. With production at a standstill or at least on low volumes, now is the ideal time to reflect, analyse and improve. Using this as an opportunity to conduct experimental designs will reduce the commercial and scientific risk in the longer term. If a manufacturer is looking to modify their product or start selling to a different market, the techniques described here can ensure their performance will be optimal even in new environments. In addition, designing products and processes robustly from the outset enables production lines to operate with fewer workers physically present.

How Is ASTUTE 2020’s Expertise Incorporating This Technology into The Welsh Manufacturing Sector?

ASTUTE 2020’s research proficiency within the expertise area of manufacturing systems engineering offers manufacturers the unique opportunity to embrace future manufacturing technologies such as Off-Line Quality Control. Quality control at every stage of manufacturing is a key aspect of the quality management system and through industry-academia collaboration, we can focus on key strengths in future manufacturing technologies by rethinking every aspect of your manufacturing business from a digital perspective, creating further value, improve efficiency and allow the offering of better products, processes and services to a global market. Providing cutting-edge research expertise, Welsh businesses can collaborate with ASTUTE 2020 to address their manufacturing challenges exploiting smart technologies:

Design of Experiments case studies:

Two case studies are presented using two different experimentation techniques to optimise the performance of additive manufacturing (AM) processes. The first case is carried out on a Fusion Deposition Modelling (FDM) AM process using the Design of Experiment (DoE) factorial method whilst the second case is carried out on a Powder Bed Fusion AM process using a one-factor-at-time (OFAT) approach.

Case Study 1:

Introduction:

This case study describes the application of the Design of Experiment (DoE) methodology to a new Additive Manufacturing 3D printer installed in the research laboratories of the University of Mondragon, Spain.  Mondragon University, Operations Management Research Group

The study demonstrates how DoE can help to, 1) gain a thorough understanding of a machine or process and 2) to identify optimum parameters settings for maximising machine performance.

The case study:

The main objective of this study was to develop an understanding and behaviour of the different parameters of Fusion Deposition Modelling (FDM) within an Additive Manufacturing (AM) process. The study was carried out on a new AM/3D printer acquired for the research laboratories of the University of Mondragon, Spain (Figure 1). A robust modelling technique based on DoE was used to accelerate the development of the AM process.

The part created for this study was of a standard design for a tensile test as shown in Figure 2. All parts were made from PLA which is a standard polymer used in AM processes using FDM technology. To evaluate the mechanical properties of the samples, tensile tests were carried out in the laboratory following EN ISO 527 standard.

15 parameters were identified as the most important factors affecting the process. This was reduced to 6 key parameters (controllable factors) used in the DoE experiment to analyse significant effects and their interactions. Two levels for each of the 6 factors were selected and evaluated using a fractional factorial design with two replications (64 experiments). 3 test outputs were measured, namely 1) Young’s Modulus (Gpa), 2) break in tension (Mpa) and 3) Breakage deformation.

 

 

Figure 1: Fused Deposition Modelling Equipment

Figure 2: Dimensions of the sample tested

The results were fitted to a model and the main factor effects and interactions estimated. The analysis was carried out using Minitab 18 software.

Optimum values of the 6 factors were selected based on the DoE analysis. Since 3 output responses were measured, significant factors and levels were identified for each response and the best combination of factors that maximised all 3 responses evaluated. The results were validated by applying the settings to the manufacture of two moulds; one to manufacture skateboards and the other for drone cases.

Case study 1 Conclusions:

The case study demonstrates the importance of the use of experimental design methods (DoE) to optimise a process with minimal experimental effort. The experimental set up and analysis can readily be applied to other AM/3D technologies to identify the best settings of various parameters of the process. In a wider context, the step by step process can also be applied to other manufacturing processes.

Further details of the case study can be found in the following reference:

Eguren, J.A., Esnaola, A. and Unzueta, G. (2020), “Modelling of an additive 3d-printing process based on design of experiments methodology”, Quality Innovation Prosperity, Vol. 24 No. 1, pp. 128–151.

DOI: http://dx.doi.org/10.12776/qip.v24i1.1435

Case Study 2 - ASTUTE Case Study:

Introduction:

This ASTUTE case study of Sandvik Osprey Ltd demonstrates an alternative approach used by the ASTUTE team at Cardiff University to identify the optimum solution for the AM machine at the company. The machine used was a Renishaw AM250 using a powder bed fusion process.

The case study:

This case explored the use of high temperature nickel alloy (H-X) within an AM machine. In this case, 4 parameters were identified as having an influence on the printed results: 1) Laser power, 2) Scanning Speed, 3) Hatch spacing and 4) Powder layer thickness. Based on the experience of the team and from previously published work on this technology, the power was kept at the maximum in order to reduce the optimising variables and at the same time improve production efficiency. A single powder layer thickness was used. To achieve a good powder packing density the ideal layer thickness was found to be slightly higher than the average particle size.

Since the laser power was kept at a maximum, laser scanning speed would have the largest effect of the output. Therefore experiments were performed using 5 different scanning speeds and 3 hatch spacing distances giving 15 experiments. Optimisation was achieved through measuring the relative density of the L-PBF fabricated samples.

Case study 2 conclusion:

In this example, based on previous knowledge of the process, a quick optimisation was achieved by varying only two parameters over 15 experiments.

For new materials, further tests would be needed which would include expanding the scanning speed and hatch spacing ranges as well as using a range of laser power settings. In this case, carrying out one at a time tests would quickly become unmanageable, for example introducing 3 laser power settings would require a minimum of 45 experiments. Under these circumstances the use of DoE methodology would be more efficient and also provide a better understanding of the parameter effects and their interactions.

Further details of case study 2 can be found in:

ASTUTE 2020 Sandvik Osprey Ltd. Case Study

and

Han, Q., Gu, H and Setchi, R., Discrete element simulation of powder layer thickness in laser additive manufacturing, Powder Technology, Volume 352, 2019, Pages 91-102, ISSN 0032-5910,

DOI: https://doi.org/10.1016/j.powtec.2019.04.057

Collaborative research with ASTUTE 2020 plays a critical role in companies meeting their future growth targets through enhancing the resilience of their manufacturing systems and supply chains.

ASTUTE 2020 can support manufacturing companies across a variety of sectors, such as aerospace, automotive, energy generation, oil and gas, medical devices, electronics, foods, etc., stimulating growth by applying advanced engineering technologies to manufacturing challenges driving cutting-edge research and innovation. ASTUTE 2020 collaborations inspire manufacturing companies to improve and streamline their manufacturing processes, manufactured products and supply chain, generating sustainable, higher-value goods and services and bringing them to a global market.

The ASTUTE 2020 operation has been part-funded by the European Regional Development Fund through the Welsh Government and the participating Higher Education Institutions.

Please get in touch to discuss opportunities for working with us; the team at the University of South Wales will be happy to provide support on your Quality Engineering challenges.

email: steffan.james@southwales.ac.uk

Back