Short answer

When designing for additive manufacturing, explicitly model and test how design parameters like orientation and hatching affect surface quality to prevent defects.

Field: Modelling
Source: IEEE Sensors Letters (2018)
Method: Design of Experiments (DOE) with post-build inspection and regression modelling.
Evidence: Strong effect

The intricate details of a design, specifically parameters like recoating orientation, hatching pattern, and width, significantly influence the surface roughness and overall quality of components produced via laser powder bed fusion. This modelling research insight is drawn from a 2018 study published in IEEE Sensors Letters. Using Design of experiments (doe) with post-build inspection and regression modelling., researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing for additive manufacturing, explicitly model and test how design parameters like orientation and hatching affect surface quality to prevent defects.

Study

ModellingHigh ImpactStrong effect

Design Complexity in Additive Manufacturing Directly Impacts Build Quality

IEEE Sensors Letters · 2018

Key Findings

01Edge roughness in thin-wall structures is sensitive to recoating orientations.
02Edge roughness is sensitive to the width parameter.
03Edge roughness is sensitive to hatching patterns.

Application

Design takeaway

When designing for additive manufacturing, explicitly model and test how design parameters like orientation and hatching affect surface quality to prevent defects.

How to apply

Before finalizing a complex design for AM, simulate or experimentally test the impact of different recoating orientations, hatching strategies, and feature widths on critical quality attributes like surface finish.

Project actions

01When investigating a design process, clearly define the specific parameters you will manipulate and measure.
02Use imaging techniques to objectively assess the outcome of design choices.

Method & Evidence

AimHow do design parameters such as recoating orientation, hatching pattern, width, and height influence the edge roughness of thin-wall structures in additive manufacturing?

MethodDesign of Experiments (DOE) with post-build inspection and regression modelling.

ProcedureExperiments were conducted on a laser powder bed fusion machine. Post-build inspection involved collecting X-ray computed tomography (XCT) images of the final builds. These images were then registered with computer-aided designs to characterize the edge roughness of each layer in thin-wall structures. An analysis of variance (ANOVA) was performed on the design parameters, and a regression model was developed to predict the impact of design on edge roughness.

ContextAdditive Manufacturing (Laser Powder Bed Fusion)

Variables

IV["Recoating orientation","Hatching pattern","Width","Height"]

DV["Edge roughness"]

CV["Material (e.g., specific metal powder)","Laser power","Scan speed","Layer thickness"]

Strengths & Limitations

Strengths

+Utilized advanced imaging (XCT) for detailed post-build analysis.
+Developed a predictive regression model linking design parameters to quality metrics.

Limitations

The complexity of the experimental setup (XCT imaging, specialized AM machine) might be difficult to replicate in a typical design project. The focus on a single AM technology limits generalizability.

Reliability & validity

Reliability could be enhanced by repeating the experiments multiple times and ensuring consistent machine calibration. Validity is supported by the use of objective XCT imaging and statistical analysis to link design parameters to measured outcomes.

Think critically

To what extent can predictive models, like the regression model developed here, be generalized across different additive manufacturing machines and materials?

Design Principles

"Design for Manufacturability (DFM) in Additive Manufacturing: Proactively account for process-specific sensitivities to ensure desired build quality."

Understanding these relationships allows designers to proactively optimize designs for additive manufacturing processes, mitigating potential quality issues before production. This leads to more reliable and predictable outcomes, reducing waste and rework.

What This Means for Your Design

Making parts with 3D printers can be tricky. This research shows that the way you design the inside details, like how the printer moves and fills in layers, really affects how smooth the final part is. Changing these design details can make the part better or worse.

How to use in your project

1.Reference this study when discussing how design choices influence the manufacturability and quality of prototypes or final products, particularly in additive manufacturing contexts.

Add to My Project

Quick Cite

Paragraph starter

This research highlights the critical link between design complexity and build quality in additive manufacturing. By investigating laser powder bed fusion, the study demonstrated that specific design parameters, including recoating orientation, hatching pattern, and width, significantly influence the edge roughness of thin-wall structures. This underscores the necessity for designers to consider these process-specific sensitivities during the design phase to optimize for desired outcomes and minimize potential defects.

Source

IEEE Sensors Letters

From Design Complexity to Build Quality in Additive Manufacturing—A Sensor-Based Perspective

journal · 2018

View source

Questions About This Research

What does the research say about design complexity in additive manufacturing directly impacts build quality?: When designing for additive manufacturing, explicitly model and test how design parameters like orientation and hatching affect surface quality to prevent defects. Evidence: IEEE Sensors Letters (2018).
Why does "Design Complexity in Additive Manufacturing Directly Impacts Build Quality" matter for design?: Understanding these relationships allows designers to proactively optimize designs for additive manufacturing processes, mitigating potential quality issues before production. This leads to more reliable and predictable outcomes, reducing waste and rework.
How can designers apply this research?: When designing for additive manufacturing, explicitly model and test how design parameters like orientation and hatching affect surface quality to prevent defects.
What were the main findings?: Edge roughness in thin-wall structures is sensitive to recoating orientations.. Edge roughness is sensitive to the width parameter.. Edge roughness is sensitive to hatching patterns.
What research method was used?: Design of Experiments (DOE) with post-build inspection and regression modelling..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2018 journal from IEEE Sensors Letters.
What should I do differently in my next project?: Before finalizing a complex design for AM, simulate or experimentally test the impact of different recoating orientations, hatching strategies, and feature widths on critical quality attributes like surface finish.
What are the limitations?: The study focused on thin-wall structures and a specific AM process (L-PBF); findings may vary for different geometries or AM technologies. The analysis was limited to edge roughness as a quality metric.