CONFERE 2014 CROATIE 3 – 4 JUILLET 2014, SIBENIK, CROATIE

DFAM IN THE DESIGN PROCESS: A PROPOSAL OF CLASSIFICATION TO FOSTER EARLY DESIGN STAGES Floriane LAVERNE a,b, Frédéric SEGONDS b, Nabil ANWER a, c, Marc LE COQ b a : Université Paris 13, IUT St. Denis, Place du 8 Mai 1945, 93205 Saint-Denis, France. b : Arts et Métiers Paris Tech, Laboratoire CPI, 151 boulevard de l’Hôpital 75013 Paris, France. C : LURPA, ENS Cachan, 61, Avenue du Président Wilson, 94235 Cachan Cedex, France.

ABSTRACT Additive manufacturing (AM) is considered as a new industrial revolution due to the prospect it brings in product manufacturing. This paper seeks to investigate existing design methodologies dedicated during the design process to AM and called Design for Additive Manufacturing (DFAM). The main finding of this paper is the need to provide the designer a methodology suitable for early design stages and by a digital tool and based on a study of industrial practices. Key words: Additive Manufacturing, Direct Manufacturing, DFX, DFAM 1 INTRODUCTION In the 1990’s, Concurrent Engineering (CE) changed the organization of product design. It substituted the sequential design approach (“throw over the wall”) by a parallel processing of activities (Sohlenius, 1992). At the same time, collaborative engineering appeared and brought to CE a collaborative approach. These changes led to a new design support: the Product Lifecycle Management (PLM), which covers the whole product lifecycle. Today, increasing product complexity and the reduction of cost and time to market engage the designer to integrate requirements for this fast-changing market in early design stages. Indeed, decisions made during those stages determine more than 80 percent of the life-cycle cost (Ishii, 1995) even though only 10 percent of the expenses are incurred. Recent developments in AM, specifically in direct manufacturing allow new opportunities in product manufacturing but knowledge about these opportunities is limited (Bourell, Leu, & Rosen, 2009). This brings us to the following research problem: how to design a suitable additive manufacturing product in early design stages? Two more questions are rising up: - How can DFAM be implemented in current PLM tools from a methodological point of view? - How can Intermediate product Representations (IR) (Vinck, 2009), (Mer, Tichkiewitch, & Jeantet, 1995) which have been generated by the additive manufacturing processes be used? And how can problems of difficult computer aided representation be solved? As shown in figure 1, the aim of this work is the improvement of AM by a methodological design approach in a context of direct manufacturing. This article is divided in 3 chapters. First we define AM, PLM and Design for X (DFX). Then we analyze the contribution of current DFAM for the designer and we show the limits in the upstream design stages. Finally we conclude our study by a proposal of research orientation.

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Figure 1 Place of the work in the Hype Cycle relative to growth in direct manufacturing and market expectations, adapted from (Fenn, 2010) and (Wohlers, 2013)

2 STATE OF THE ART 2.1 Additive manufacturing Photosculpture was invented by Willeme in 1860 in order to provide a 3D replica of an object. This method relies on the use of 24 cameras which are arranged circularly in an angle of 15 degrees around a model (figure 2a) and the simultaneous intake of 24 photos. The projection of the photo on a screen is then used to reconstruct with a pantograph in clay 1/24th of the profile model. Topography, meanwhile, is the brainchild of Blanther (1892) and allows the production of tooling for the manufacture of embossed cards. Topography needs to print contour plates wax and then to cut them up (figure 2b). Stacking, bonding and finally smoothing the plates provide the matrices. Then one just needs to press a sheet in the tool to create a topographic map.

Topography (b)

Photosculpture (a)

Figure 2 Photosculpture and Topography’s principles

Both techniques presented above are considered as pioneers of additive manufacturing because product manufacturing results from its decomposition into elementary layers (Bourell et al., 2009) However, it is the sale of layered photopolymer process, called stereolithography, developed by Hull and sold by 3D Systems in 1986 that allows the rise of additive manufacturing. In this technology, a movable platform is immersed at a depth in a tank full of a vat of photopolymer. A UV laser beam scans the resin surface and initiates polymerization. To allow the achievement of the next layer, the platform is dipped into the vat (usually 0.05 mm corresponding to the machine accuracy). The same process is then repeated. The stack of hardens layers leads to the final product. Since the 1990’s, the increasing number of technologies based or adapted from stereolithography led to normalize the expression “Additive Manufacturing”. According to AFNOR (2011) and ASTM (2012), AM is “a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies”. Note here that traditional manufacturing technologies such as subtractive manufacturing (e.g. machining) and formative manufacturing (e.g. casting or stamping) are totally upset.

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We now present the evolution of the application fields of AM since its emergence (figure 3).

Figure 3 Evolution of the AM technologies

From an industrial point of view, rapid prototyping is the reason for the development of additive manufacturing. It is intended to prototype shapes. Indeed, layer based technologies allow to obtain in short time delays and at low cost product representation, which are necessary for the validation of forms, appearances or ergonomics. In the 1990’s, rapid tooling became a second research axis for additive manufacturing (Karapatis, J.P.S, & R, 1998). It results from the need to validate production routings with non-expensive and quickly made tools. It has been later extended to the manufacturing of operational tools with complex shapes for mass production (e.g. plastic injection). AM, due to an increased accuracy of machinery, a wider range of materials, mechanical properties similar to other manufacturing technologies, has reached a level of maturity. Thus, direct manufacturing bears the potential to become a new standard for product manufacturing. It aims to merge benefits of industrial design prototypes and engineering design prototypes into a final functional product. In this context, market share of direct manufacturing in additive manufacturing has been increasing for 10 years. This growing interest is due to opportunities offered by these technologies compared to traditional technologies (Hague, Campbell, & Dickens, 2003), (Rosen, 2007), (Wohlers, 2013): - Structural lightening: solid parts can be removed by lighter structures (e.g. honeycomb, lattice). - Material complexity: layer based manufacturing allows design of functionally graded materials where subtractive technologies are limited to homogeneous materials. - Customization of parts: the product is tailored for customer needs especially for medical implant or in mass customization. - Simplification of assemblies: Complex geometries lead to reduction of the number of parts The designer has to know this large number of prospects given by additive manufacturing. It is also necessary to help him to rethink how to design. Changes in practices require capitalization and dissemination of the additive manufacturing knowledge and processes. Therefore the designer must be provided with suitable methods and tools: it is the role of Design for X in the PLM environment. 2.2 Product Lifecycle Management and Design For X 2.2.1. PLM

According to Ullman and Jones (2003), PLM appeared in 2001 and was used to generically define computer systems that manage product information from cradle to grave. A wider definition presents PLM as a “concept that aims at integrating the various processes and phases involved during a typical product lifecycle with people participating in product development processes” (Sharma, 2005). More broadly, the PLM approach can be defined as “a strategic business approach that applies a consistent set of business solutions in support of the collaborative creation, management, dissemination, and use of product definition information across the extended enterprise from concept to end of life, integrating people, processes, business systems, and information” (CIMdata, 2014). In this respect, the processes are as important as the data (Segonds, Maranzana, Véron, & Aoussat, 2011). From a software point of view, PLM can be considered as a tool for managing data and documents and more generally knowledge of a company. This knowledge is classified into two categories (Garetti, Terzi, Bertacci, & Brianza, 2005) : - “operational knowledge that deals with practices being used in the company in order to run the different types of business processes” - “specific knowledge about specific products and processes related to know how, used in the making processes”

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Among information exchanged in PLM system, we can mention data focusing on the change of the representation of the product during the product lifecycle called the Intermediate Representations. Thus, we define PLM environment as a facilitator of interdisciplinary collaboration, which allows to centralize and to disseminate knowledge, while providing solutions and tools for each step of the design process and leading to a progressive enrichment of IR of the product. 2.2.2. DFX

In the PLM environment, integration of constraints and knowledge related to activities located at later design stages have improved cycle times and productivity in engineering. In this context, Design For X is a “generic name for the members of a family of methodologies adopted to improve design product as well as design process from a particular perspective which is represented by X” (Tomiyama et al., 2009). According to Huang (1996) and Tichem (1997) the X either represents: - A specific property (cost, quality, environmental effects, etc.). - A life-cycle phase of the product (manufacturing, assembly, service, etc.) or one of the subprocesses. This concept of Design For X is “first used by Ford and Chrysler to help with the design of wartime necessities during World War II” (Fitzgerald, Herrmann, & Schmidt, 2010). However it makes sense in the 1960’s with the willingness to approach design from the product architecture. This is Design For Assembly (DFA) whose foundations are laid by Boothroyd (1994). The success of the DFA determined the emergence of new design methods oriented towards specific goals (Design for Manufacturing, Design for Environment, etc). The generic term Design for X was then used to refer to all of these methods. Reasons for the considerable interest in DFX are its easy deployment and its large expected benefits. DFX allows both improving competitiveness (quality, time to market, ...) but also rationalizing decisions in the design process and thus increasing operational efficiency of the design engineers (Kuo, Huang, & Zhang, 2001), (Huang, 1996). DFX implies the earliest consideration of design objectives and their constraints as well as capitalization and dissemination of knowledge (Huang, 1996). Its success then goes through its implementation into PLM and deployment of experts to design the knowledge management systems. 3 DESIGN FOR ADDITIVE MANUFACTURING; A CLASSIFICATION OF EXISTING METHODOLOGIES 3.1 Definition. Because of the youth of additive manufacturing technologies and their use at an industrial scale, few studies were published concerning the breakthrough into the designing process (Vayre, Vignat, & Villeneuve, 2012). Bourell et al (2009) suggest to develop new design methodologies dedicated to additive manufacturing and inspired by Design for Manufacturing and Design for Assembly and calls it DFAM. The most common definition of DFAM is a methodology which “maximize product performance through the synthesis of shapes, sizes, hierarchical structures, and material compositions, subject to the capabilities of AM technologies” (Gibson, Rosen, & Stucker, 2010) and “to best utilize manufacturing process capabilities to achieve desired performance and other life-cycle objectives” (Rosen, 2007). We suggest to extend this definition to the tools that support the DFAM methodologies. Then, DFAM is the set of methodology and tools that help the designer to take into account the specificities of additive manufacturing (technological, geometrical, pre / post processing ...) during the design stage. In next paragraph, we study existing DFAM focused on product. We analyze their scientific basis and place them in the Pahl and Beitz (2007) design process. 3.2 Analyzing DFAM and positioning proposal in design process Huang classifies DFX according to their objectives. Some DFX can lead to the assessment of the proposed concepts or solutions, the other allow decision making in the design (Huang, 1996). We keep this classification applying to the context of AM (figure 4).

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- DFAM can guide the designer in the creation of the concept: within the meaning of Huang, it is decision making. - DFAM can help to study the compatibility of existing technologies with the proposed concept: within the meaning of Huang, it is concept assessment. This positioning of DFAM in the design process will rely on the definition of early design stages given by Segonds (2014) that presents them as stages starting from task clarification to the beginning of the embodiment design and leading to a preliminary layout.

Figure 4 A classification of DFAM

3.3 DFAM for concept assessment DFAM for concept assessment drive designers in their choice of AM technology. The manufacturability of the product is evaluated according to the considered processes, and then a classification is proposed. By analogy with the studies of manufacturability achieved by Toussaint (2010) and made in a context of DFM, we classify existing DFAM in terms of the measurements of the product. There is an assumption relative to all of these DFAM: the manufactured product will fully comply with numerical model (dimensional and / or geometrical tolerances) The first type of DFAM for concept assessment is based on qualitative approach. The designer uses some selection criteria often difficult to quantify but which are representative for the studied technologies. Among these, we can mention cost, production speed, technical performance (surface quality, minimum thickness of a layer, accuracy, …) and available materials (original state, properties, …). The criterion is assessed for each technology with words (poor, average, good, …) or in a binary code (Ishii, 1995). A weighting is then applied according to the particular significance of each criterion. The ranking of the technologies is achieved with tools for decision support; such as Analytic Hierarchic Process, AHP (Lokesh & Jain, 2010), Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS (Byun & Lee, 2005) or Decision Support Problem, DSP (Williams, Mistree, & Rosen, 2005). The manipulated data are here very poorly defined and require approximations or estimations from the designer. Figure 5 represents the positioning of the previous DFAM and also the place of early design stages in the Pahl and Beitz (2007) design process. As we can see, these DFAM (represented in pink in figure 5) are suitable for the first steps of design. The second type of DFAM for concept assessment is based on quantitative (or direct) research

methods and lead to a more realistic measure of manufacturability. This implies to know more specifically the proposed concepts, particularly as regards to the geometry, dimensions and tolerances, in order to define realistic rate routing. Alexander (1998) , Hopkinson and Dickens (2001), Ruffo and Hague (2007) or Atzeni and Salmi (2012) develop methods for classifying the technologies (additive and traditional) by calculating the costs of manufacture. The methodology of Yim and Rosen (2012) is turn towards an assessment of the time of production. This required information can be found only from late design stages (represented in green in figure 5). Thus we consider that these DFAM can’t be implemented until embodiment design where designers specify the geometrical characteristics of product useful for establishing manufacturing program.

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Figure 5 DFAM for concept assessment in design process

To conclude, we specify that, whatever the DFAM for concept assessment, the designer is focused on the choice of the manufacturing process from a specific design product. We propose to place them among the methodologies for choosing an additive manufacturing technology. 3.4 DFAM for decision making We remind that DFAM for making decision aims to help the designer in finding solutions. Three different approaches exist: guidelines, DFAM for design optimization, DFAM for product propertiesand DFAM for geometrical validation (figure 6).We will develop them successively. DFAM Decision Making

DFAM Guidelines

DFAM Design Optimization

DFAM Geometrical Validation

DFAM Product Properties

Figure 6 Existing DFAM approaches for decision making 3.4.1 Guidelines

In DFX, guidelines, also called rules, are general recommendations to be taken into account during the design stages. They are intended to direct or guide the design approach from an existing state of the art. In AM context, guidelines are rules created to give the designer a general framework. They set out in two key principles: - “it is possible to make any complexity of geometry at no extra cost” (Hague et al., 2003) - there is no limitation in shape or in distribution of materials (Hague et al., 2003); (Doubrovski, Verlinden, & Geraedts, 2011). We believe that these rules are suitable for clarifying the task because, during the exploratory phases, they will help designer to consider new concepts or IR. We add that no methodological tools are associated to AM guidelines. 3.4.2 DFAM for product properties

This kind of DFAM helps designers to link material or process properties to product ones. Several approaches exist. Some of these delivers design rules taking into account the specificities of the inherent anisotropy of layer manufacturing by offering a preferential orientation for the mechanical properties (strength, fatigue...) of the part (Ahn, Montero, Odell, Roundy, & Wright, 2002). Other studies are focusing on surface texture and give digital solution (adaptive slicing) to avoid the staircase effect (Ma, But, & He, 2004). The others are related to process limits by defining the standard elements that the machine can build (Adam & Zimmer, 2014), (Gerber & Barnard, 2008). These are varied DFAM and helpful for the conceptual or embodiment design. However it requires having fixed the AM technology and thus can be very restrictive in design stages when creativity is needed. 3.4.3 DFAM for design optimization

DFAM for design optimization are methods intended to create an optimal shape, i.e. for taking advantage of the opportunities of the AM. To obtain it, designers have to start from scratch: knowledge is ineffective because it is based on methodologies developed for traditional process. The overall approach proposed by these DFAM can be divided into four steps summarized in figure 7.

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1. Defining the design 2. Defining the loading 3. Function optimization 4. Characteristics space condition validation Figure 7 Global approach of the DFAM for design optimization: Case of the Compolight project Courtesy of SIRRIS.be – Design by Sirris achieved by MB Proto for Flying Cam

First, the designer defines his design space from the analysis of the geometrical features, the tolerances and the requirements that are expected for the final product. The second step is the set of the loading conditions applied on the product. The next step is the most important. The material is distributed according to the constraints laid down the first two steps and to the criteria that the designer wants to optimize (weight, mechanical properties …) The techniques used are generally parametric optimization (Vayre et al., 2012) or topological optimization (Rosen, 2007), (Fey, South, Seepersad, & Neptune, 2009). In parametric optimization, the variety of possible shapes is reduced by the number of parameters used, but in topological optimization, no restriction exists. The result required to accept a total change in the topology of the final product: here we find the purpose of these DFAM. The last step is the validation of the created geometry towards the external constraints. Rosen (2007), Fey (2009) or Rodrigue & Rivette (2010) rely on finite element calculation, Boyard et al (2013) use tools to prevent failures. The two previous stages are supported by specific tools developed in CAD/CAE software such as Optistruct (Altaïr). Figure 8 illustrates the value of DFAM for design optimization. In the Compolight project, the objective reducing lightweight parts for helicopters. Using DFAM, weight was reduced to 26% for a same mechanical behavior. As we can see, the new shape leaves nothing to assume the original shape or the previous manufacturing process.

Without DFAM With DFAM Figure 8 Comparison of the shapes obtained with and without DFAM: Case of the Compolight project

We conclude that the implementation of such DFAM can only be done from the embodiment design stage. Indeed, the inputs of these methods require having accurate design data (dimensions, material…) and specific tools which are not available in early design stages. 3.4.4 DFAM for geometrical validation

DFAM for geometrical validation help to ensure consistency between digital product and final product. They arise from the statement that the manufacturing strategy will directly impact the final characteristics of the product. We find here the concept of Design for Manufacturablity whose purpose is to identify manufacturing parameters that will influence the manufactured product (Tichkiewitch & Véron, 1998) and that must be integrated during the design stage.

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DFAM for geometrical validation relate to the process modeling and the simulation of the machining paths to determine an optimal realistic geometry (Ponche, Hascoët, Kerbrat, & Mognol, 2012) or to provide the optimal distribution of materials (Muller, Mognol, & Hascoet, 2013). We believe that these methods can only occur late in the design process. It is no longer to define a shape for additive manufacturing but also to answer the question: how to do one’s best? This implies an interest in modeling and simulating the selected process. 3.5 Synthesis The previous study shows that various DFAM exist (figure 9) and among them, little relate to early design stages whereas the greater part of the final cost is determined at this time. Those existing relate only to concept assessment or to the decision making with guidelines. They do not meet the designer needs because there are methods for general frameworks or for the selection of process.

Figure 9 Synthesis of DFAM methods

The second finding is that methodologies for early design stages are rarely supported by digital tools Table 1 summarizes the design stages targeted by each category of DFAM and their associated tools (in green) when they are existing. This is in contrast with the following statements: a DFX is particularly adapted to the designer if it has been associated a tool (Huang, 1996) and “proper design tools need to be developed” to achieve to “radically change products and the product development process” (Doubrovski et al., 2011).

Type of DFAM Concept Assessment Guidelines Product Properties Design Optimization Geometrical Validation

Clarifying the task X X

Adapted for … Conceptual Design Embodiment Design X X X

X X

Detail Design X

X X

Table 1 DFAM methods and DFAM tools in design process

The last observation is that the proposed tools must be supported by Knowledge Base systems such as the Rapid Manufacturing Advice System (RMADS) developed by Munguía (2010) for a DFAM for concept assessment. RMADS requires the retrieval of useful data from two databases: one for the expert rules, the other for system features. Thus DFAM tool must be implemented into PLM to benefit enrichments of knowledge and know-how related to data management. Whereas knowledge about AM is currently held by business experts, figure 9 and table 1 shows that few studies have been performed to make AM knowledge available to designers at the early stages. Therefore, we believe that a methodological proposal, dedicated to the additive manufacturing and adapted to early design stages would allow the designer to fully exploit the potential of this booming technology. 4 RESEARCH OPPORTUNITIES In order to develop, in the current collaborative environment, a DFAM methodology supported by a tool, a study of industrial practices is initiated. Indeed, we recall that AM can no longer be considered

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after the design process but as an input data. It also means that the operational knowledge, hold by AM experts must be tested. To succeed in the extraction of knowledge from the AM expert, we formulate the following assumption: “Knowledge of the procedures and process followed in the AM companies can improve the design of parts intended for direct manufacturing”. It suggests one more assumption: “Procedures and process can be transferable and capitalized”. To succeed in this work, a study, articulated in four steps and adapted from a formalism proposed by (Segonds et al., 2014), is started to describe the AM numerical chain in companies (figure 10)

Figure 10 Experimental approach adapted from (Segonds et al., 2014)

The emphasis is placed on the identification of 3 main data categories exchanged during the process: stakeholders, tools and generated IR. Describing the current industrial approach will help to answer to the following questions: - How is translated knowledge process, owned by AM experts, into product information? - How is conveyed and stored the products information contained in IR into the PLM databases? Comparing the results will drive us to identify which main data must be generalized to enrich our DFAM methodology summarized in the Idef0 diagram (figure 11).

Figure 11 Formalization of the objective

5 CONCLUSION We have shown that the product design is now part of a collaborative world environment whose support is PLM. DFX methodologies, aiming to integrate at the earliest design stages subsequent issues, must therefore be part of this environment. With the novelty of additive manufacturing, especially in rapid manufacturing, current design methods are no longer adequate. It requires a rethinking of the design process followed by designers which leads to Design For Additive Manufacturing. We demonstrated that DFAM exist at all phases of the design process to help the designer to evaluate the solution or to develop a solution. However, it appears from this analysis that few DFAM target early design stages and even fewer offer tool that can be implemented in PLM. That is why we suggest developing an upstream DFAM adopting a dual perspective: methodological seeking to identify ways to put our DFAM in PLM and product offering to manage RI from additive manufacturing.

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