The tools for personal digital fabrication (DF) are on the verge of reaching mass-adoption beyond technology enthusiasts, empowering consumers to fabricate personalized artifacts. We argue that to achieve similar outreach and impact as personal computing, personal fabrication research may have to venture beyond ever-simpler interfaces for creation, towards lowest-effort workflows for remixing. We surveyed novice-friendly DF workflows from the perspective of HCI. Through this survey, we found two distinct approaches for this challenge: 1) simplifying expert modeling tools (AutoCAD →Tinkercad), 2) enriching tools not involving primitive-based modeling with powerful customization (e.g., Thingiverse). Drawing parallels to content creation domains like photography, we argue that the bulk of content is created via remixing (2). In this work, we argue that to be able to include the majority of the population in DF, research should embrace omission of workflow steps, shifting towards automation, remixing, and templates, instead of modeling from the ground up. https://doi.org/10.1109/MPRV.2020.3029650 ©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works PERSONAL FABRICATION (PF) describes the notion that machinery, workflows, and tools for industrial manufacturing become available to consumers. This – ideally – includes not only technology enthusiasts, but also less ”tech-savvy” users. They may still desire to benefit from the Pervasive Computing Published by the IEEE Computer Society © 2021 IEEE 1 opportunities of PF, such as tailored artifacts they are unable to order online easily (e.g., non-standardized attachments [1]–[3]). However, these potential users may not be convinced to invest time in skill acquisition and PF processes. They may not be ”makers” and may likewise not be convinced to learn digital fabrication (DF) for their benefit – especially when their alternatives are ever-improving online shopping ”workflows”. Just like computing itself progressed from centralized use for few, often expert, user groups to personal and ubiquitous computing, (personal) fabrication is likely on a similar path. Ultimately, as described by Gershenfeld, we may have machines able to fabricate anything [4]. Such a device is still constrained by the input it may receive – i.e., ”what to fabricate?”, currently answered by the use of (computer-aided) design software. We want to approach these developments from the perspective of HCI. Namely, the assumption that machines able to fabricate in any material and size will be available to users in the same way powerful word, video, and image processors became available to and actively used by them. This empowers users of PF devices to benefit from digital precision to create and shape matter – both for productive and mundane purposes. However, mere ownership of or access to such devices (e.g., 3D-printers) along with the software needed (e.g., CAD software), does not make a person a user. While increasingly more machine knowledge can be embedded in the hardor software itself [1], users have to precisely express requirements for future artifacts (e.g., dimensions). We consider the established notion of (3D-) modeling – defining shapes based on simple primitives such as lines or voxels – to be a hindrance for widespread adoption of PF. Defining artifacts from the ground up is appropriate for domain experts or users enjoying it and possessing intrinsic motivation for the process itself, not necessarily the result [5]. While paradoxical at first, we argue that PF must provide ways for future users to benefit from intricately tailored, personal artifacts, without resorting to defining them in great detail. Likewise, DF has to provide simple, low-effort tools for content creation that enable users to explore the possibilities of the technology while generating quick, yet viable, results. Our main argument is that these low-effort tools should not be a simplified version of an expert tool which follows a creation paradigm ”from scratch”, but should rather be radically simple interfaces which omit most modeling and required expert knowledge, reducing artifact creation to as few interactions as possible. We propose a model to differentiate modeling and remixing. We see ”remixing” as a gradient between two extremes: ”getting” (purchases in stores) and ”modeling” (designing and defining artifacts from the ground up). We further survey a set of recent literature in PF and categorize them within our gradient that focuses on effort as a core dimension. We partially ground our argument in parallel developments that can be observed in the fields of music, video, or image editing (e.g., GarageBand ⇔ Logic Pro, Instagram ⇔ Adobe Photoshop, and TikTok ⇔ Adobe Premiere). These facets of content creation have non-experts in photography, videography, or music, creating content for communities like TikTok or Soundcloud (Figure 1). By relying on automation and derivative work, users (unlikely to use expert tools) are enabled to explore and generate content without explicit training, high entry barriers, and with low effort. One of the core arguments we propose in this work, is that one reason Instagram and TikTok were able to bring content creation to the masses, is not only their associated communities, but also their radical break with the ”creation paradigm”. Instead of giving users full control over the content, as done by expert tools, creation follows ”getting” and ”remixing” paradigms, where the users select from pre-defined tools and filters, which often leverage automation (e.g., face tracking for videos). Standardizing these processes is an apparent reduction in expressivity. However, the combination of pre-defined operations still offers a sufficient variety to be able to please the needs of the individual (who often even depends on templates to realize and explore what is possible). Flath et al. describe the growth of Thingiverse users as coinciding with the introduction of the customizer [6] – a way to enable novices to create remixes without the skills to model the entire artifact. This customization aspect enhanced a store-like interface (i.e., Thingiverse, ”getting”) with options to tailor artifacts 2 Pervasive Computing General (online) stores (Amazon) Simple content creation (Instagram) Complex content creation (Photoshop) Industrial CAD �AutoCAD� Simplified CAD �Tinkercad)

中文翻译：

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无处不在的个人制造之路：无需建模，而不是越来越简单

个人数字制造 (DF) 工具即将被技术爱好者广泛采用，使消费者能够制造个性化的人工制品。我们认为，为了实现与个人计算类似的推广和影响，个人制造研究可能不得不超越更简单的创建界面，转向最省力的重新混合工作流程。我们从 HCI 的角度调查了对新手友好的 DF 工作流程。通过这项调查，我们发现了应对这一挑战的两种不同方法：1) 简化专家建模工具 (AutoCAD →Tinkercad)，2) 丰富工具，不涉及具有强大定制功能的基于原始建模的工具（例如 Thingiverse）。与摄影等内容创建领域相似，我们认为大部分内容是通过重新混合创建的 (2)。在这项工作中，我们认为，为了能够在 DF 中包含大多数人口，研究应该忽略工作流程步骤，转向自动化、重新混合和模板，而不是从头开始建模。https://doi.org/10.1109/MPRV.2020.3029650 ©2021 IEEE。个人使用这种材料是允许的。在任何当前或未来媒体中的所有其他用途都必须获得 IEEE 的许可，包括出于广告或促销目的重印/重新发布此材料、创作新的集体作品、转售或重新分发到服务器或列表，或重复使用任何受版权保护的组件PERSONAL FABRICATION (PF) 描述了用于工业制造的机械、工作流程和工具可供消费者使用的概念。理想情况下，这不仅包括技术爱好者，但“精通技术”的用户也较少。他们可能仍然希望从 IEEE 计算机协会发布的普及计算中受益 © 2021 IEEE 1 PF 的机会，例如他们无法轻松在线订购的定制工件（例如，非标准化附件 [1]–[3]） . 然而，这些潜在用户可能不会被说服在技能获取和 PF 过程中投入时间。他们可能不是“制造商”，也可能不会被说服为了他们的利益而学习数字制造 (DF)——尤其是当他们的替代品不断改进在线购物“工作流程”时。就像计算本身从少数（通常是专家）用户组的集中使用发展到个人和无处不在的计算一样，（个人）制造可能走上类似的道路。最终，正如 Gershenfeld 所描述的那样，我们可能拥有能够制造任何东西的机器 [4]。这样的设备仍然受到它可能接收的输入的限制——即“制造什么？”，目前通过使用（计算机辅助）设计软件来回答。我们希望从 HCI 的角度来处理这些发展。也就是说，假设用户可以使用能够以任何材料和尺寸进行制造的机器，就像他们可以使用并积极使用强大的文字、视频和图像处理器一样。这使 PF 设备的用户能够从数字精度中受益，以创造和塑造物质——无论是出于生产目的还是日常目的。然而，仅仅拥有或访问此类设备（例如，3D 打印机）以及所需的软件（例如，CAD 软件）并不能使一个人成为用户。虽然越来越多的机器知识可以嵌入硬件或软件本身 [1]，但用户必须精确表达对未来工件的需求（例如，尺寸）。我们认为 (3D-) 建模的既定概念——基于简单的基元（如线条或体素）定义形状——阻碍了 PF 的广泛采用。从头开始定义工件适合领域专家或用户喜欢它并拥有过程本身的内在动机，不一定是结果 [5]。虽然一开始有些矛盾，但我们认为 PF 必须为未来的用户提供从精心定制的个人工件中受益的方法，而无需对它们进行详细的定义。同样，DF 必须提供简单的、用于内容创建的省力工具，使用户能够探索技术的可能性，同时生成快速但可行的结果。我们的主要论点是，这些省力的工具不应该是遵循“从头开始”创建范式的专家工具的简化版本，而应该是极其简单的界面，省略大多数建模和所需的专家知识，从而减少工件创建尽可能少的互动。我们提出了一个模型来区分建模和重新混合。我们将“重新混合”视为两个极端之间的渐变：“获取”（在商店中购买）和“建模”（从头开始设计和定义工件）。我们进一步调查了 PF 中的一组最新文献，并将它们归入我们的梯度中，将努力作为核心维度。我们部分地将我们的论点建立在音乐、视频或图像编辑领域（例如，GarageBand ⇔ Logic Pro、Instagram ⇔ Adob​​e Photoshop 和 TikTok ⇔ Adob​​e Premiere）的并行发展中。内容创作的这些方面没有摄影、摄像或音乐方面的专家，他们为 TikTok 或 Soundcloud 等社区创作内容（图 1）。通过依赖自动化和衍生工作，用户（不太可能使用专家工具）能够在没有明确培训、高进入门槛和低努力的情况下探索和生成内容。我们在这项工作中提出的核心论点之一是，Instagram 和 TikTok 能够为大众带来内容创作的一个原因，不仅是他们相关的社区，而且是他们与“创作范式”的彻底决裂。不像专家工具那样让用户完全控制内容，创作遵循“获取”和“重新混合”范式，用户从预定义的工具和过滤器中进行选择，这些工具和过滤器通常利用自动化（例如，视频的面部跟踪） ）。标准化这些过程会明显降低表现力。然而，预定义操作的组合仍然提供了足够的多样性来满足个人的需求（他们通常甚至依赖模板来实现和探索什么是可能的）。弗拉思等人。将 Thingiverse 用户的增长描述为与引入定制器 [6] 相吻合——一种使新手能够创建混音的方法，而无需对整个工件进行建模。这种定制方面增强了类似商店的界面（即 Thingiverse、