Establishing a Crowdsourcing-Based Data Collection Tool to Identify Sources of Build Failure in Novice AM Designs

The maker movement has led to an increase in publicly available makerspaces, where communities can work with design and manufacturing equipment, including additive manufacturing (AM) machines. A low barrier-to-entry is essential to maintaining inclusivity, allowing inexperienced patrons to use AM technology. However, this lack of experience subsequently encourages frequent print failures, which contributes to increased maintenance or user frustration. Build failures are caused by several variables, but commonly stem from build preparation, the slicing process, or user interference. To identify key causes behind failed AM builds in a student-focused makerspace, this paper outlines how a crowdsourcing-based data collection tool was formulated that automatically extracts design and print information through printer files and correlates this information with student evaluations of their part’s build quality. This data allows for common sources of failure within the space to be readily identified. A case study is analyzed to demonstrate the tool’s general effectiveness and applicability.