Barden has adopted a combination of Design of Experience methods and FAST analysis techniques to optimise the performance and cost of a range of bearing designs for a rotary machine manufacturer.

On a recent design project at Barden based in Plymouth, design engineers adopted a combination of structured design methods and test analysis techniques in order to optimise the performance and cost of a range of super precision angular contact and deep groove bearing for a customer that manufactures high-speed rotary machines.

The project was not carried out in isolation from the customer; the process was collaborative.

The two methods used were the Function Analysis System Technique (FAST) and Design of Experiments; both are popular systems analysis and problem-solving techniques that can be applied not just to bearing design, but to any engineered system, mechanical design or production process.

Nick Dowding, business development manager at Barden, said: 'This is the first time that Barden engineers have used these particular methods together in this way.

'Normally, we would look at a customer's application and then design a bearing solution and then cost that bearing.

'However, on this particular project, the customer wanted us to investigate and develop a range of different high-precision ball bearings for use on its range of high-speed rotary machines.

'The idea was that after the project was complete, the customer could then select the optimal bearing solution for several machine variants with respect to performance and cost,' he added.

FAST was developed during the value engineering revolution in the 1960s and is a rigorous method for understanding complex systems by converting the activities performed in a system to the function performed by the system for its customers.

System engineers and value analysis specialists use this method for product and process improvement, systems design and systems architecture.

Design engineers at Barden used FAST to optimise the cost of the bearings, while adopting Design of Experiments methods to optimise the performance of the bearings.

Dowding said: 'First, we had to look at what the bearings were required to do in terms of their key functions, from an end application viewpoint.

'We had to ask ourselves questions such as what the bearings have to provide the application or machine to make it operate effectively,' he added.

Examples of key functions for a bearing are to minimise machine and shaft-related noise and vibration and to ensure that the machine's life was maximised.

Parasitic losses in the machine's mechanical drive system also had to be minimised.

The bearings also control shaft position and so needed to cope with the operating speeds related to this.

Once the key bearing functions were identified, said Dowding, the chosen method by which the bearing would achieve these functions had to be listed.

He said: 'For example, in order to achieve the required life of the machine, a range of bearing factors come into play, including lubrication, cleanliness, temperature control, method of retaining the lubricant, bearing case design, and so on.

'The basic idea here is to identify what functions the bearing brings,' he added.

Based on this, a list of bearing requirements was drawn up.

The completed list included 25 different bearing requirements, including clearances, run-out and vibration harmonics.

Dowding said: 'What we did next was to put these bearing functions and requirements into a matrix, whereby we could score or weight the functions accordingly.

'In order to score each function, we had to decide, for example, whether the stiffness of the bearing was more important for the application than the bearing clearance, or whether the bearing run-out was more important than both of these.

'Eventually, the completed matrix included a weighting for every bearing feature placed in order of their importance to the application,' he added.

Next, Barden was able to apply FAST cost optimisation techniques to each function.

Dowding said: 'We had to allocate individual costs associated with each stage of manufacture to each function, including raw material costs, inspection and turning of the inner and outer rings.

'More than 30 separate bearing production steps were considered and scored against the bearing functions,' he added.

This exercise enabled the engineers to demonstrate to the customer which bearing features were costing the most, but contributing the least to the performance of the bearing in the specific application.

Dowding said: 'In effect, you end up with the most important bearing functions at the top of the matrix, which also happen to be the most expensive ones to manufacture.

'The features at the bottom tend to be the opposite - the least important functions but inexpensive to produce.

'We focused our efforts on the middle range, where the bearing functions were deemed to be relatively important to the application and where the manufacturing costs were relatively expensive.

'We then looked at the manufacturing steps in detail and refined the process to suit the machine application.

'For example, we decided that it wasn't necessary to carry out 100 per cent functional testing on every bearing we produced.

'Sample functional testing was deemed sufficient.

'Similarly, we agreed with the customers not to inspect all the bearings after the turning process.

'The cost of manufacturing could therefore be reduced while still meeting the bearing performance criteria,' he added.

According to Dowding, the project was a success and resulted in a range of bearing solutions that were optimised in terms of their performance for a range of machine variants, but which also, on average, cost the customer 10-20 per cent less.

For each machine model, the customer was able to select a bearing solution that was most appropriate, for example, a high-cost, high-performing bearing could be chosen for a high-end rotary machine, or a relatively inexpensive, adequate performing bearing for a mid-range model.

Dowding said: 'By adequate, we mean that the bearing's performance was deemed sufficient for the application requirements for machine life.

'In effect, the customer got to choose from a premium bearing solution or a budget option,' he added.

Alongside the adoption of the FAST method, Barden's designers also used Design of Experiments.

Design of Experiments is a structured, organised method for determining the relationship between factors that affect a process and the output of that process.

Frequently used across natural and social sciences, as well as more recently by Six Sigma practitioners, Design of Experiments is a systematic approach to investigating a system or process.

Mark Pritchard, senior product engineer at Barden said: 'As far as I know, Design of Experiments is not being widely used in ball bearing design.

'We used it for this particular customer because it is a robust, reliable method and a rapid approach to bearing design.

'Design of Experiments is a series of structured tests in which planned changes are made to the input variables of a process or system.

'The effects of these changes on a pre-defined output can then be assessed,' he added.

Using a combination of Taguchi screening and a three-level Central Composite design, multiple design factors are investigated simultaneously.

This allows identification of the factors that have a significant effect on the response, as well as the effect of interactions between factors.

'This meant that we were able to rapidly study multiple bearing design variables simultaneously, enabling us to optimise a set of bearing designs based on performance and cost metrics for multiple customer machine models or variants,' Pritchard said.

For this particular project, Pritchard said he had to conduct 108 separate Design of Experiment mathematical models and 72 manual calculations (for validation and verification of optimised settings).

The accuracy of the models proved to be in the region of 99 per cent and the result was four optimised bearing designs for each customer machine variant.

Pritchard said: 'Dynamically, there are lots of variables that affect the performance of deep-groove and angular contact ball bearings.

These include the geometry of the bearing itself, the size and number of the balls, curvatures, materials, speed, radial and axial loads and so on.

These variables are what we focused on in this project.

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