Expert-guided optimization for 3D printing of soft and liquid materials.

Sara Abdollahi,John H Miller,Adam W Feinberg,Alexander Davis,Feng Zhao

doi:10.1371/journal.pone.0194890

Sara Abdollahi, John H Miller + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0194890

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Abstract

Additive manufacturing (AM) has rapidly emerged as a disruptive technology to build mechanical parts, enabling increased design complexity, low-cost customization and an ever-increasing range of materials. Yet these capabilities have also created an immense challenge in optimizing the large number of process parameters in order achieve a high-performance part. This is especially true for AM of soft, deformable materials and for liquid-like resins that require experimental printing methods. Here, we developed an expert-guided optimization (EGO) strategy to provide structure in exploring and improving the 3D printing of liquid polydimethylsiloxane (PDMS) elastomer resin. EGO uses three steps, starting first with expert screening to select the parameter space, factors, and factor levels. Second is a hill-climbing algorithm to search the parameter space defined by the expert for the best set of parameters. Third is expert decision making to try new factors or a new parameter space to improve on the best current solution. We applied the algorithm to two calibration objects, a hollow cylinder and a five-sided hollow cube that were evaluated based on a multi-factor scoring system. The optimum print settings were then used to print complex PDMS and epoxy 3D objects, including a twisted vase, water drop, toe, and ear, at a level of detail and fidelity previously not obtained.

Highlights

Additive manufacturing (AM) can bring digital designs into production quickly and inexpensively compared to traditional manufacturing methods
We examined elongation to break of the 3D printed PDMS, and compared this to the cast PDMS, which has an elongation to break of 140% [18], representing the maximum value we could theoretically obtain
Considering that 3D printed polymers are known to have lower elongation to break between printed layers [19], our results demonstrate that we can minimize this issue for PDMS using the expert-guided optimization (EGO) strategy

Summary

Introduction

Additive manufacturing (AM) can bring digital designs into production quickly and inexpensively compared to traditional manufacturing methods. The expert chose a wider range of physical parameters for the 4th generation hill climb, the type of PDMS, the specific type of the support bath Carbomer, and the support bath concentration This resulted in a set of factor-levels that maximized the quality of the resulting cylinder (30/30), which when repeated had an average score of 30 (n = 8). The 4th generation hill-climb produced optimized parameters that achieved a maximum score of 30 that was maintained upon repeated prints (Fig 4A) These parameters worked for the initial calibration cylinder, and enabled scaling of the cylinder from 8 mm × 10 mm up to 21.9 mm × 27.3 mm (diameter × height), with comparable fidelity (Fig 4B). This demonstrates the role that the geometry of the CAD model plays in print fidelity, and reinforces our inclusion of geometry in the parameter space for optimization

Discussion

Findings

Materials and methods