3D Printed Glitches: Learning From Manufactured Errors

Introduction

The realization of a digital model into a physical object by way of 3D printing is not only popular among sectors of the manufacturing industry or advanced research laboratories but has also become an accessible resource for the consumer market. In higher education, particularly within the design disciplines, students routinely utilize 3D printing to produce representational objects which communicate a design idea, or to quickly prototype a preconceived part for an assembly. In these scenarios, software and hardware resources facilitate the designer’s desired outcome; in other words, the information flows in one direction, from designer to machine. To accommodate the command and reply relationship between a user and a machine, developers and manufacturers of 3D printing equipment create interfaces that aim to minimize undesirable results by guiding the designer through the printing process.

Emerging technologies in the modern classroom are also ubiquitous with teaching. The ease with which students can learn new programs or updated versions of existing software platforms is due, in large part, to robust built-in help menus and a wealth of online tutorial guides. However, these resources rarely provide a glimpse into the inner workings behind the technology or its digital DNA. Every piece of geometry, click of the mouse, or edit of content, generates a coded language that is notating a history of objects, commands, and workflows. It is common, and even expected, for most users to be unaware of the existence of this history of code. The promise of a “walk up and use” technology like 3D printing, one that can serve any audience, demands that expertise in the technology is minimized to a set of best practice principles which are easily digested by a novice user (Alcock, Chilana, & Hudson, 2016). The use and motivation behind these user-friendly technologies becomes prescribed, and the chances for a serendipitous discovery are greatly reduced.

Under these conditions, the user is ignorant as to how the machine translates the digital data or how the hardware operates. Instead, the designer relinquishes their input and knowledge to the equipment during the printing procedures, enabling the machine to complete the requested task independently. However, if the relationship between designer and machine is examined and reinterpreted as a collaboration, rather than a linear process, an alternative scenario emerges where technology can receive and interrogate the information provided by the user and can react to the information with its own unique contribution. The designer can invite the machine to participate in the design’s development as a co-author. This reflexive working process allows the designer to have a deeper understanding of the limits and benefits of the equipment they use. Just as a visual artist develops technical skills and critical thinking through an intimate rapport with their medium, the designer can harness this same connection with emerging technologies such as 3D printing.

This chapter will illustrate the manner in which students engage software and hardware used for 3D printing in order to analyze the predetermined functionality of these tools, and will highlight how students can enter into a comprehensive dialogue with technology as a medium for exploration and authorial exchange. Specifically, this chapter will examine the process of glitching digital code, a hands-on exploration of the properties of a 3D object through manipulation and augmentation of the object’s internal logic (Figure 1). A group of university level architecture and design students completed a series of case studies that investigated the digital DNA found within the virtual source material utilized for 3D printing. After becoming familiar with the coded language and logic behind 3D digital models, the students tested the potential of manufactured glitches through purposeful re-arrangement of the coded language to produce unexpected and unfamiliar results. They were then able to translate the glitches into physical 3D printed forms which prompted unanticipated behavior from the printer. These case studies allowed students to explore authorial exchange between themselves as designers and the machine.

Figure 1.

Example of a manufactured glitch generated by the alteration of digital coded language illustrated by the alternative geometric composition of an existing object

Source: Hannah Liechty, 2016