Designers need to deal with several uncertainties, before defining the physical characteristics of the truck design best capable of satisfying a certain set of customer requirements. To provide some protection against those uncertainties, designers tend to anticipate those uncertainties by adding a design margin. For example, the product may have the structural strength to carry more load or a system may have enough space to accommodate an extra component, thus increasing the product capability to meet new requirements or to compensate for uncertainty.
In the case study presented in Chapter 4, designers attempt to design for what they call the worst- case scenario. In this scenario, for example, the temperature for which a cooling system is being designed is a single value obtained from simulating the worst-case scenario. The truck is generally used in a drive cycle and the variation across the cycle of the heat that needs to be transferred by the cooling system is quite large. For example, consider the variation between a truck driven in a cold climate on a flat road where the heat that needs to be transferred by the cooling system is low compared to another driving condition where the weather is hot and the roads are rough. Therefore, designers use simulations and take into consideration one, worst- case, operating condition which is going uphill (hence at slow speed) and running the engine at 100% capacity. These extreme driving conditions give them the maximum amount of heat the cooling system needs to transfer. However, if for a specific Engine A, the required temperature (for the ambient air) that needs to be accommodated is 38°C but they take a design variant that can accommodate 46°C, which satisfies the requirement for the worst-case scenario (WCS), the Engine A would have a margin in a normal driving condition (Figure 5-6)
102 Moreover, some design variables that are communicated by a design team and used as inputs to activities performed by other teams are imprecise. Design imprecision is a form of uncertainty formulated in the literature (Wood et al. (1990), Antonsson and Otto, (1995)) to express uncertainty in decision-making during the design of a new product, especially at the early stages of a design process where engineers and designers have not yet acquired sufficient knowledge to decide what is the final value that a design variable would have. Moreover, at this stage, requirements are not definite and are often subject to change. Therefore, engineers work with a range of alternative values for design variables and explore concurrently different concepts for systems, sub-systems and components.
For example, consider the case when the requirements for which a truck is being designed is not a single known value but rather a range of possible values forming a distribution having a mean µ and a standard deviation σ. The capability of the system to carry that probabilistic load is again not a single known value, but a range of possible values forming a second distribution. This can be seen in Figure 5-7 which illustrates the relationship between requirements, capability and margins. There are two capability distributions. The one on the left is the capability of an existing variant whilst the second represents a redesign of the variant with an enhanced capability.
Figure 5-7 Relationship between requirements, capability and margins
Therefore, the probability that the system will meets its requirements can be increased by considering what the design can do to meet the customer requirements and moving the two distributions further as shown in Figure 5.7. The design margin as shown, is effectively added to enhance the probability of meeting the customer requirement.
However, in the company, the capability of a design is not defined in absolute terms, but rather as the capability of a system to work under specific conditions. This makes it difficult to capture the accurate relationship between requirements, capability and margins. In the case of the engine temperature for example, the capability is expressed in terms of requirements, i.e. the engine should not overheat after a certain use time.
On the other hand, trucks have different use profiles, they operate under very different operation conditions from smooth, rough to very rough roads and cross country. They also operate under different temperature ranges from less than – 40 to over +50 degrees C, so that many
104 components need to be heated or cooled even though they would not need this under most circumstances. Thus, to meet the customer requirements a combination of many parameters needs to be considered (Figure 5-8).
In practice designers consider the extreme situations under which a truck should perform. For example, using the air conditioning at a full blast, stop and go driving in a very hot day, can raise the engine temperature above normal. Still, the driver should be able to deliver his cargo, without sustaining long term damage to the truck. These extreme situations correlate with the Worst-Case Scenario (WCS) which are concrete and detailed scenarios that help the engineers to think through potential failure modes. For example, engineers and designers will think of driving a fully loaded truck up a mountain road, like the Sierra Nevada, with a constant slope, in hot summer days, with stop-start traffic. This is similar to what was introduced in the literature by Taguchi and Clausing, (1990) and defined by Clausing, (2004) as the Operating Window which they consider as the boundaries of a critical parameter at which certain failure modes are excited. Therefore, the road conditions mentioned in the example above can be considered as an operating window, since they consider the worst scenarios (high values) which lead to one failure mode and other scenarios (low values) lead to the other failure mode. Hence, designers choose a design to maximise the operating window, i.e. they respond to the worst case scenario. To minimise variety in the product platform many components have traditionally been designed to take the highest demands coming from worst-case scenarios. For example, systems like the fan are generally overdesigned for some installations. Design engineers address this range of possible scenarios, and therefore a range of parameters by considering one point which is the worst-case scenario, which might generate margins for some applications later on in the development process. On the other hand, to be able to more accurately and optimally set the limit of the use of components and their reliability, all external parameters such as temperature,
are virtually tested by simulation and compared to running time/ running distance up until failure occurs. Even if the choice of material, dimensioning and performance is adapted to each transport operation’s driving condition and load condition (Figure 5.8), some components are used in different products, therefore some these components will only have margins for some installations. For example, a component in a truck might be designed initially for a rough road condition and 44 ton GCW (Gross Combination Weight, which determine the total weight of the truck). However, the same component might be also used in trucks with a GCW up to 60 tons, if the road is classified as smooth.
106 To assess the values given to the worst-case scenario, and to make sure that the design will meet the customer requirement, engineers and designers rely mostly on virtual testing (Tahera et al. 2012). These virtual tests or simulations are a form of design analysis, which uses the virtual models of components or systems to test different scenarios. These tests can give an indication where margins might be hidden and have the potential to be an effective way to identify margins. However, since the majority of these tests are virtual, margins may be underestimated through this process. The company does not test a product until failure. This can be problematic for engineers as they might not be able to quantify some margins and can be surprised at later stages in product development. This is a particular issue for the outsourced components. Suppliers test their components and guarantee that the component meets the requirements, but they might not tell their customers by how much. In some cases, even if the suppliers are aware of the margins they have, they do not necessarily communicate them. In this way, they might still be able to sell the same or slightly different components for the next generation of products.