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# Toward Automated Design for Manufacturing Feedback

Wonmo Kim, Timothy W. Simpson

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Wonmo Kim, Timothy W. Simpson. Toward Automated Design for Manufacturing Feedback. 20th Advances in Production Management Systems (APMS), Sep 2013, State College, PA, United States. pp.40-47, 10.1007/978-3-642-41266-0_5 hal-01452151

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Distributed under a Creative Commons Attribution 4.0 International License
# Toward Automated Design for Manufacturing Feedback

Wonmo Kim¹, Timothy W. Simpson¹

¹Department of Industrial & Manufacturing Engineering, The Pennsylvania State University,
University Park, PA 16802 USA
{wzk106, tws8}@psu.edu

**Abstract.** Iterative loops and rework between design, manufacturing, and testing delay the development lead time for complex products like vehicles. This research focuses on creating an automated design for manufacturing (DFM) feedback system (ADFS) framework that reduces these iterations by providing early, fast, and informative feedback on manufacturability to designers. The proposed ADFS analyzes manufacturability in terms of part geometry with respect to a given set of process capabilities based on DFM guidelines for vehicle manufacturing. In order to increase the fidelity of the search, a heuristic approach to obtain manufacturing process fitness with respect to a part design is introduced. The proposed system framework will help to identify suitable manufacturing processes more quickly as well as provide visual feedback for geometric advice at the feature level with regard to the selected processes.

**Keywords:** manufacturing assessment system, design for manufacturing, manufacturing feedback

# 1 Introduction

## 1.1 Background

Designing a complex product like a motor vehicle is often delayed due to modifications along with subsequent verifications [1]. These iterative loops between design and testing teams make the whole new product development linger at the design stage. It has been reported that the design process can usually take up to 24 months in North America [2] and 15 to 20 years [3] for military vehicles particularly from the concept initiation to the production phase.

It would be desirable if these iterative loops can be shorten or ultimately, eliminated. If the manufacturing feedback information can be delivered to designers timely by adopting a stand-alone feedback system tool, then it can significantly reduce product lead time, in general, and vehicle development, in particular.

However, so far no manufacturing assessment system (MAS) has been developed that takes multiple manufacturing processes related to vehicle production into account simultaneously [4]. Adopting design for manufacturing (DFM) guidelines into a MAS can be an alternative to respond such difficulty [5]. If geometric features of a design
satisfy DFM guidelines of a manufacturing process, then it can be said that the design
is manufacturable in terms of the corresponding manufacturing process.

Though a design may be judged as manufacturable, some potential hindrances such
as visibility issues, fixturing, set-ups, etc. can still occur during the actual manufactur-
ing execution of a design. Those details can only be investigated by the high-level
assessment for a particular process. Likewise, an analysis of DFM guidelines alone
cannot determine which manufacturing process is the most appropriate to consider.
Therefore our goal is to provide feedback information to designers for multiple
processes rather than pick a single candidate process.

1.2 Research Objective

A robust systematic framework is needed to feed-forward appropriate manufacturing
processes to the next higher-level assessment as well as to provide feedback to de-
signers based on this manufacturability assessment. It is clear that reducing iterative
loops requires a stand-alone system in automated fashion to interact with designers in
a timely manner. Hence, this research aims to develop an automated DFM feedback
system (ADFS) framework to provide early, fast, and informative feedback for manu-
facturability to designers as well as feed-forward for detailed investigation. There are
two main deliverables as objectives:

1. Feed-forward: the ADFS helps determine which manufacturing processes are the most appropriate for the detailed level assessment for more information.

2. Feedback: the ADFS provides geometric advice at the feature level in an intuitive and visual manner.

2 Literature Review

## 2.1 Manufacturability Assessment System

In the context of concurrent engineering, generating detailed manufacturing feedback
in an interactive way is critical in MAS to reduce development time span [6]. With
the widespread of CAD systems, studies regarding stand-alone manufacturability
assessment tools were also spurred with the support of feature recognition methodol-
ogy through CAD software [7]. However, those researches were mostly limited to
only machining process and concentrated to calculating manufacturing cost by gene-
rating adequate process plans [8]. According to Kalpakjian et al. [9], rough statistical
analysis of published works shows that approximately 92% of the researches are fo-
cusing on the applicability of MAS to machining processes only.

## 2.2 Utilizing DFM Guidelines

The main benefit of practical use of DFM guidelines is that each guideline of a manu-
facturing process can serve as one of several criteria, so that they can be also utilized
as a source for generating redesign recommendations for failing features. As an ex-
ample, Jacob et al. [10] present a geometric reasoning methodology by adapting cross-comparisons of geometric features to coded design rules for grinding. When the system recognized salient features, corresponding design rules are invoked to generate manufacturing feasibility decisions automatically. Providing redesign recommendation was implemented as parametric ranges in text format verified by a knowledge base. However, the application was also bound to only single process perspective.

# 3 Proposed Methodology

## 3.1 Fast Heuristics

The capability of DFM guidelines enables a “Fast Heuristics” approach that can assess manufacturability without cost estimation analysis. Although calculating manufacturing cost is the most complete measure of manufacturability [9], investigating it is not an easy task in the early stages of design. According to Özbayrak at el. [11], estimating a manufacturing cost for a unit of product design based on activity-based costing approach requires 15+ factors to be considered. Moreover, performing cost estimation becomes worse if there are multiple candidate processes to investigate.

**Process Filtering.** Therefore, a heuristic filtering approach is proposed that aims to analyze manufacturability faster without losing robustness. In the system shown in Fig. 1, the part geometry extracted from submitted designs will be assessed with respect to a given set of process capabilities collected from the DFM guidelines. Here, those capabilities serve as filters that rule out infeasible manufacturing processes.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Filtering system</figcaption></figure>

A different manufacturing process must have its own filter set, as design rules for each manufacturing process are different. When a converted CAD file in XML format comes into the system, a trigger invokes a manufacturing process and its corresponding filter set from the knowledge base. Then each feature's geometry is evaluated against the appropriate filter element in a set. The output database stores “Go”&“No-go” results of every process with respect to a design as well as information of failing features. The system loop iterates until all of the manufacturing processes are analyzed for the submitted design. Through this filtering system framework, it is ex-
pected that a series of analyses toward candidate manufacturing processes delivers only suitable manufacturing processes (feed-forward) so that design advice at the feature level can be provided later according to DFM guidelines.

**Table 1** shows an example filtering analysis for the bracket shown in **Fig. 2**. For this preliminary analysis, minimum/maximum section thickness and minimum corner radii allowance are used for the filtering. The reason for the target thickness to be machined (4 in.) is because the machining process removes chunk of raw materials for both sides. For casting, the section thickness limits the width of center and bottom pillars. Also, it is presumed that forging and stamping require fixturing to position the datum B' to the ground. Processes that are grayed out at **Table 1** are infeasible processes to manufacture this example part.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Example bracket</figcaption></figure>

<table><caption>**Table 1.** Example filtering 1: manufacturing capabilities of various processes</caption><thead><tr><th rowspan="2">Process</th><th colspan="3">Target thickness to be machined = 4". For casting processes, 0.394". Minimum corner radii = 0.4"</th></tr><tr><th>Min Section Thickness (in.)</th><th>Max Section Thickness (in.)</th><th>Minimum corner radii (in.)</th></tr></thead><tbody><tr><td>Metal Stamping</td><td>0.0001</td><td>0.79</td><td>N/A</td></tr><tr><td>Fine-blanking</td><td>0.005</td><td>0.5</td><td>N/A</td></tr><tr><td>Draw bending</td><td>0.035</td><td>0.109</td><td>1.5 - 8</td></tr><tr><td>Powder Metal</td><td>0.06</td><td>0.22</td><td>No limitation</td></tr><tr><td>Forging</td><td>No limitation</td><td>No limitation</td><td>No limitation</td></tr><tr><td>Abrasive-Jet Machining</td><td>0.005</td><td>0.032</td><td>0.004</td></tr><tr><td>Water-Jet Machining</td><td>No limitation</td><td>18</td><td>No limitation</td></tr><tr><td>Electron-Beam Machining</td><td>0.001</td><td>0.25</td><td>No limitation</td></tr><tr><td>Laser-Beam Machining</td><td>No limitation</td><td>0.5</td><td>No limitation</td></tr><tr><td>Plasma-Arc Cutting</td><td>No limitation</td><td>1</td><td>No limitation</td></tr><tr><td>Electroforming</td><td>0.001</td><td>0.5</td><td>No limitation</td></tr><tr><td>Metal Injection Molding</td><td>No data</td><td>0.25</td><td>0.015 or 0.5*WT</td></tr><tr><td>Machining</td><td>No limitation</td><td>No limitation</td><td>Vary (tool diameter, 1/16")</td></tr><tr><td>Wire EDM</td><td>No limitation</td><td>No limitation</td><td>No limitation</td></tr><tr><td>Sand mold casting</td><td>0.118 (3mm)</td><td>No limitation</td><td>equal to 0.5*ave. wall thickness</td></tr><tr><td>Permanent mold casting</td><td>0.079 (2mm)</td><td>1.96 (50mm)</td><td>equal to 1*ave. wall thickness</td></tr><tr><td>Plaster-mold casting</td><td>0.039 (1mm)</td><td>No limitation</td><td>equal to 0.5*ave. wall thickness</td></tr><tr><td>Investment casting</td><td>0.039 (1mm)</td><td>2.95 (75mm)</td><td>0.03</td></tr><tr><td>Ceramic-mold casting</td><td>0.039 (1mm)</td><td>No limitation</td><td>0.03</td></tr><tr><td>Die casting</td><td>0.019 (0.5mm)</td><td>0.47 (12mm)</td><td>equal to 1.5*ave. wall thickness</td></tr></tbody></table>
**Process Fitness Function.** The filtering process yields a series of “Go” and “No-go” responses for the manufacturing processes that were assessed. To increase the fidelity of the evaluation for the feed-forward, we propose to develop a process fitness (PF) function that is derived heuristically. If the number of processes in the knowledge base is 16, for example, then the system generates 16 stacks of response data of each process. The fitness will be derived from the statistics of all 16 responses and is presented as percentage value of “Go” proportioned to the total number of filtering attempts. Therefore, each PF provides a capability metric for each manufacturing process with respect to a given design; hence, it can be used to re-sequence filters in a set to derive the feed-forward faster. The modeling of the PF is shown in Fig. 3.

<figure><img src="image_5.png" /><figcaption>Fig. 3. PF modeling</figcaption></figure>

Suppose that the knowledge base contains 16 manufacturing processes that are available. The group of manufacturing processes can be represented as:

$$P = \{P_1, P_2, P_3, \dots, P_{16}\}$$

The number of filters N in a filter set is different among manufacturing processes. A filter $F_{ij}$ of a manufacturing process $P_i$ can be represented as:

$$F = \{F_{i1}, F_{i2}, F_{i3}, \dots, F_{ij}\}, \text{ where } i = 1 \text{ to } 16, j = 1 \text{ to } N_i$$

Note that the number of filters is depicted as $N_i$ since it is dependent on the ith manufacturing process. Toward any of submitted design $D_k$, where $k = 1$ to M, performing a filtering analysis with a set of filters F of a manufacturing process $P_i$ bears a response that can be represented as:

$$F_{ij}(D_k) \rightarrow \text{"Go" or "No-go"}$$

Suppose that the system performs filtering analyses for a design $D_1$ with a designated set of filters for process $P_1$. After a loop of analyses is performed, the filtering system generates a stack of responses as described in Eq. 1.

$$\begin{bmatrix} F_{11} \\ F_{12} \\ F_{13} \\ \vdots \\ F_{1j} \\ \vdots \\ F_{16} \end{bmatrix} \times [D_1] = \begin{bmatrix} G \\ N \\ G \\ \vdots \\ G \end{bmatrix} \qquad (1)$$
Process fitnessPF<sub>i</sub> of process P<sub>i</sub> with respect to the designD<sub>k</sub> is derived by:

$$ = \frac{\text{total number of "Go" from } F_{ij} \text{ for a process } P_i \text{ toward a design } D_k}{j} $$

when i = manufacturing process index, 1 to 16.

For a group of M designs, the filtering system produces following a response matrix extended from Eq. 1 as follows:

$$ \begin{bmatrix} F_{11} \\ F_{12} \\ F_{13} \\ \vdots \\ F_{1j} \end{bmatrix} \times [D_1, D_2, D_3, \dots, D_M] = \begin{bmatrix} G & N & G & \dots & G \\ N & \vdots & \vdots & \dots & \vdots \\ G & \vdots & \ddots & \dots & \vdots \\ \vdots & \vdots & \ddots & \ddots & N \end{bmatrix} \quad (2) $$

In this matrix, each column of the response matrix stands for the results from filtering analyses for the ith process P<sub>i</sub> toward a designD<sub>M</sub>.

**Sequencing Filters.** If there is an important feature property that has a small allowance range, then a filtering analysis using that feature geometry would rule out the corresponding manufacturing process frequently. Performing that analysis before any other filters would reduce the number of remaining candidate processes quicker, so that it helps to extract a process faster and increase fidelity of the framework especially for the feed-forward.

If there is enough number of sample designs, it is possible to derive a series of PF values for a process P<sub>i</sub> toward M number of designs from the matrix in Eq. 2. From the response matrix, analysis results of a filter F<sub>ij</sub> toward M number of designs are the jth row of the matrix. The importance of a filter F<sub>ij</sub> can be simply derived from the proportion of “No-go” responses to the number of all filtering attempts toward M designs as following:

$$ = \frac{\text{Weight of a filter } F_{ij} \text{ total number of "No-go" from } F_{ij} \text{ for a process } P_i \text{ toward } M \text{ designs}}{M} $$

Instead of counting the number of positive responses, negative responses are collected because the effectiveness of filtering infeasible candidates is the matter; a filter has a higher capability if it rules out the process more often.

## 3.2 System Framework

The proposed methodology framework for generating automated DFM feedback is presented with two separated phases. At the first phase, collected sample designs are applied to the filtering system to derive filter sequences for manufacturing process in the knowledge base. The information flow of the first phase is presented at Fig. 4.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Phase 1: Derive sequences of filters</figcaption></figure>

When the sequencing is done, the filtering system will be refined by adopting the
results from Phase 1 as described in **Fig. 5**.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Phase 2: Generate feed-forward/ feedback outputs</figcaption></figure>

The PF serves as an internal metric in the ADFS. If a manufacturing process obtains 100% of the PF value, then it will be judged as manufacturable in terms of the process and can be passed forward for more detailed assessment. Else, a process with the highest PF will be selected and visual feedback on its failing features will be delivered back to the designer.

4 Closing Remarks

Through the proposed stand-alone ADFS, it is expected that designers can refine their designs for manufacturability from visualized feedback so that iterations can be reduced. Detail development of the visualization feedback is currently ongoing work.

Additionally, there are many research items that can be extended from this re-
search. Although the proposed ADFS is built based on filtering methodology, violat-
ing a DFM design rule (i.e., a filter) does not always mean that the design is not ma-
nufacturable. In fact, a non-manufacturable design for a selected process may become
manufacturable by adding additional processes. In this research, those “superset” of
multiple processes and its sequencing are currently not considered. Also, providing
dynamic visual geometric recommendations at the feature level would provide richer
options that enable designers to “tweak” feature parameters visually on the screen.
Along with such techniques, designers can search various parameter options on spe-
cific CAD software while it does not hurt the proposed part functionality.

Evaluation of the ADFS is ongoing and will be performed through benchmarking
simulation for various design inputs. Any designs revealed as manufacturable (contain
100% PF) will be submitted for detailed evaluation in commercial software (e.g.,
[12]) so that the decision that ADFS has made can be investigated.

**Acknowledgements.** We acknowledge support provided by the Defense Advanced Research Projects Agency (DARPA/TTO) under contract HR0011-12-C-0075 iFAB Foundry. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA or the U.S. Government.

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