  The widespread consumer-grade 3D printers and learning resources online
enable novices to self-train in remote settings. While troubleshooting plays an
essential part of 3D printing, the process remains challenging for many remote
novices even with the help of well-developed online sources, such as online
troubleshooting archives and online community help. We conducted a formative
study with 76 active 3D printing users to learn how remote novices leverage
online resources in troubleshooting and their challenges. We found that remote
novices cannot fully utilize online resources. For example, the online archives
statically provide general information, making it hard to search and relate
their unique cases with existing descriptions. Online communities can
potentially ease their struggles by providing more targeted suggestions, but a
helper who can provide custom help is rather scarce, making it hard to obtain
timely assistance. We propose 3DPFIX, an interactive 3D troubleshooting system
powered by the pipeline to facilitate Human-AI Collaboration, designed to
improve novices' 3D printing experiences and thus help them easily accumulate
their domain knowledge. We built 3DPFIX that supports automated diagnosis and
solution-seeking. 3DPFIX was built upon shared dialogues about failure cases
from Q&A discourses accumulated in online communities. We leverage social
annotations (i.e., comments) to build an annotated failure image dataset for AI
classifiers and extract a solution pool. Our summative study revealed that
using 3DPFIX helped participants spend significantly less effort in diagnosing
failures and finding a more accurate solution than relying on their common
practice. We also found that 3DPFIX users learn about 3D printing
domain-specific knowledge. We discuss the implications of leveraging
community-driven data in developing future Human-AI Collaboration designs.
