Project Outline

Zero Motorcycles

The project was framed as a feasibility study for a company incorporating generative design into one or more of its products. Zero Motorcycles was the company chosen. They are a manufacturer of electric-powered motorcycles and are the only company to have a full range of them on the market.

Another distinguishing feature of Zero Motorcycles is the customizability of their products. This comes in two forms: when purchasing a motorcycle from them, the option for a premium or standard model, an additional power tank or rapid charger is given; and, for three of their models, the SR range, they also offer 'Cypher Upgrades', such as faster charging, extended range, on-dash navigation, parking mode, and heated grips. These aren't ordered and physically installed, these are instead bought digitally and through software, instantly applied to the bike. These further increase the customizability of their motorcycles and provide an important point-of-difference to competitors.

Generative Design

Generative design is particularly applicable to Zero Motorcycles. It allows for the relatively simple creation of multiple versions of a component, each providing a different compromise between mass, stiffness, and cost. Giving the user the choice between them further extends the motorcycle's customizability, the company's key selling point. 

Beyond the customizability, generative design can provide baseline improvements to the product. It mostly affects a product's cost, mass, and stiffness. The overall stiffness of a motorcycle is crucial to its handling capability, and mass reduction improves acceleration, deceleration, range, and max speed too. In a growing, competitive market, cost is an important objective too. Another plus is the marketability of generative design. It provides a visible, tangible example of the product's innovative nature. 

All of this demonstrates the value of generative design for a company like Zero Motorcycles. 

Generative Design Process

Chosen Component

To begin the generative design process, a component to be redesigned must be selected. 

Obstacle and Preserve Geometry

Loading and Structural Constraints

To guide the generative design algorithm, the forces and moments on the structure need to be supplied. For a motorcycle, the loads on the swingarm vary. For this analysis, the loads during the most extreme usage situations were supplied.

Simplified Geometry

Objectives and Manufacturing Methods

As mentioned earlier, the measured objectives are mass, stiffness, and cost. A low mass is desirable and is easily evaluated using the generated component's volume and the material's density. High stiffness is desirable and is automatically calculated by the generative algorithm; when choosing a component iteration for production, FEA can be used to verify this. Low cost is also desirable, but due to the 'optional part', customizability-based business model being suggested, it is lot a large focus. Therefore, it is only estimated comparativly to the original design. The estimate stems from the manufacturing method, material type, and material volume needed.

The options available to the generative design algorithm differ greatly depending on the type of manufacturing method chosen. For this study, a range of manufacturing methods were selected for comparison: 3-axis milling, 5-axis milling, and Metal 3D printing. These all provide their own trade-offs between the range of physical geometries they allow, the cost per part, the range of materials available, and the volume of material needed.

Results

36 versions of the swingarm were created by the generative design algorithm. Below is a selection of the best-performing and most notable component iterations.

Evaluation

The results of this generative design process were mixed. The process performed worse than expected at reducing the component's mass; only one (Outcome No. 17) achieved a significant reduction, and this came at the cost of increased cost per part. However, many iterations (chiefly No. 8 and No. 12) showed far improved stiffness with equivalent mass. These two versions are also distinct from each other in the tradeaoff they find between mass and cost. This array of options with different compromises between the three objectives synergizes well with the customizability selling point. Subjectively, the appearence and novelty of generatively designed components also provide key marketing possibilities, particularly in a field so focused on innovation. 

From these standpoints, the process has been a success. However, looking at it from the perspective of direct productaimprovement (specifically mass-reduction) the results aren't as promising as expected. Though, considering the rapid development of generative design algorithms and additive manufacturing methods, repeating the analysis in the near future is likely to yield better results.

The next steps in this study would be using a combination of finite element enalysis and physical testing to validate the assumptions made, particularly when implementing the load cases. A detailed run-down of these assumptions and their verification methods can be found in the full report.

CAD Mock-ups

Outcome No. 8 on the model of the Zero SR/S

 For access to the full design report, get in touch at Michaelsvanidze0@gmail.com.