For matching the right potential to the considered product, a detailed product and potential analysis is essential. The optimization potentials of AM can be clustered in six main potentials based on their core characteristics. The two core characteristics are tool less manufacturing and the three dimensional material generation via two dimensional layers set up by one dimensional voxels. Thereby the six main potentials “complexity for free”, “graded materials”, “monolithic design”, “function integration”, “individualization” and
“product piracy prevention” arise as indicated in Figure 2 [2].
Figure 2: The six main potentials based on the two core characteristics of AM [2].
Information gathering is crucial to decide whether a product is worth being redesigned and if a change of traditional, proved manufacturing methods to AM should be conducted. The biggest optimization potential can be exploited by rethinking the product open minded integrating different parts and functions into one. With respect to the chosen sample part this would be a rethinking of the assembly including the upright, the brake system and the other attached elements. By use of special sheets requesting basic information of the assembly and possibly combinable adjacent parts one can identify possible combinations and exclusions of entities (cp. Figure 3) [2].
Name
Material requirementsMechanical/thermal enviroment InterfaceMiscellaneous Potentials of AMAssembly Optimization history
0
Figure 3: InfoForm “key part characteristic” for gathering of part information and potential selection [2].
Furthermore, this information can be used for selection of optimization potentials. Thereto, for each column a rating of the six main potentials of AM is used, channeling the creative energy in the right direction. These sheets are used to determine the specific optimization potentials for the given sample part. The potentials will be described in detail in the following.
3.1 Material efficiency
Material efficiency usually is one of the first goals in product development as, based on the intended purpose, often a lightweight design is stated in the requirements specification. Lightweight design has become more and more
important in recent years, not only due to better performance as for race cars, but also due to environmental aspects as for aircrafts. Lastly, less material consumption during manufacturing and for the part in general saves costs [7].
But material efficiency can be interpreted in other ways, too. Material efficiency can also refer to the waste reduction during production. For highly complex structures made by conventional, cutting manufacturing technologies often huge amounts of raw material have to be invested and numerical solving of FE-models, decision algorithms reduce the number of elements wherever they are not really needed [8]. The result is a material distribution with best material efficiency, as unused material is removed and the remaining material is burdened as high as allowed with regard to stress, stiffness and fatigue constraints.
3.2 Part reliability
AM enables a product design optimized for function and not for manufacturing. The often said mantra “form follows function” can be implemented best with AM. The first main potential of AM as shown in Figure 3 is “complexity-for-free”. There is no need for stating allowances for accessibility of a cutter so that undercuts and holes as well as very complex freeform shapes are possible.
A product optimization with regard to part reliability can achieve optimized shapes that aim at reducing stresses. On the one hand this can be an overall stress reduction, on the other hand this can be an optimized notch geometry with a very low notch factor. Thus, the fatigue strength can be enhanced and the part’s reliability is improved.
Furthermore, the reliability strongly depends on a proper mounting and on correct environmental influences. Hence, a product optimization can be a support for proper mounting like specific aids on part or more simplified mounting mechanisms. A monolithic design would decrease the required mounting processes and thereby reduce the number of fault-prone processes. Furthermore, the environmental influences like temperatures can lead to a decrease of part reliability. Thus, this should be kept in mind and controlled by use of heat radiating elements or protective plates for heat, aggressive media or impacts of debris. These elements can directly be integrated and do not have to be added with additional weight, mounting processes and extra costs, even if they are of highly complex shapes.
3.3 Product optimization
The final product optimization is a combination of the use of the single optimization potentials. A proper mounting of parts, especially for bolted connections can be optimized by use of in-process marking of the parts. An additional printing of the needed tightening torque of each bolt reduces the risk of wrong mounted bolts. Especially for assemblies with many bolts that have to be tightened in a complex procedure with different torques like cylinder heads, a detailed explanation can be printed directly on the part.
Thus, no additional paper instruction is needed and the mechanic has his operation instructions directly at hand.
For hydraulic elements like the brake caliper further optimization is possible when rethinking the routing of the hydraulic pipes. The expected complex shape of topology optimization for weight reduction and stiffness increase can be used to directly include the pipes as the complexity is not restricted and the pipes do not have to be drilled. Thereby, they can smoothly follow the already existing material while avoiding sharp edges. Straight drilled and then perpendicular joining pipes lead to sharp inner corners that increase the danger of stress risings and cracks hampering the structural reliability of the part. The smoother the pipes are designed further optimization is achieved by a better flow of the medium. As less turbulences occur in these areas the pressure drop in the tube is decreased and a higher flow rate can be process a decision support can be applied. The optimization is influenced by various factors from the market, the company and its branch as well as by the application itself. This leads to the problem that at the same time multiple factors have to be taken into account exhibiting different values and significance. For the optimal solution a thorough and rational decision making process is mandatory and improves the companies’ specific added value.
Decision making is an extensive research field at an entrepreneurial level for both, strategic and operational decisions as wrong ones can have a fatal impact on the business success. The prescriptive theory deals with the process of finding a solution to achieve a possibly high degree of fulfillment [9]. In this model the “decision field” comprises the number of possible choices which represent the action alternatives. It contains also the
boundary conditions as well as the consequences. The latter defines the effects of the occurring impacts of the decision variables while the boundary conditions reflect the environment. The target system is defined within the
“decision rule” and encompasses the optimization criterion as a leading command variable to evaluate the criteria and the preferences which allow for the weighting as a transparent evaluation scheme of subjective assessment [10] [6].
Usually, there is more than one objective relevant for product optimization so that the decision making can be classified as a Multi Criteria Decision Analysis (MCDA) problem. The multitude of aims requires the structuring of relevant factors in order to control the rising complexity that develops with every influence factor. It furthermore enables the IT processing of the problem through a defined structure. In order to apply the MCDA method first the problem identification has to be started. Then, the problem structuring provides the basis for the development phase which is followed by the modeling phase and ends with the selection phase during which the decision making provides a proposal for a solution [11].
There are different approaches to solve MCDA problems. They are usually based on the pairwise comparison of an attribute such as the cost-utility analysis which is an often applied approach. Outranking models are similar but they do not use an explicit scale but utilize strict and weak preference values. This is usually beneficial for use cases that exhibit inhomogeneous units and significances for their attributes. THE PROMETHEE approach uses preference functions to choose between single alternatives. Threshold values and indifference regions are defined in order to be able to value the differences [12] [13] [14].
Table 1 shows an excerpt of the decision criteria parameterization where costs, time and weight shall be minimized and the process stability maximized. Depending on the criterion different types of preference functions are chosen determining how the threshold influences the result.
This has been derived with ‘Visual PROMETHEE Academic’ and ‘Microsoft Excel’ and the result is shown in Figure 4. The major development criteria are reliability in combination with stress reduction, weight and waste reduction, production costs and assembly time. Through this choice standardized parts are dispensed and high unit quantities cannot be realized.
Table 1: Parameterization of selected decision criteria.
Criterion Min/
Figure 4: System diagram for the sample part.