## 1 Introduction
Machine Learning applications are distinguished into supervised, unsupervised and reinforcement learning [10]. Both paradigms, DM and ML, support tasks with many different objectives. An in-depth analysis of all available Digital Engineering methods and tools, see Figure 2, would go beyond the scope of this article. Both share methods and algorithms but differ in scope--just evaluation (in the case of data mining) or trying to predict something based on known data and unknown inputs (in the case of Machine Learning). Figure 1: Categorisation of the terms Machine Learning and Data Mining according to [9]. Figure 2: Overview of Data Mining and Machine Learning methods. **Deep Learning** is a subfield of ML and uses neural networks to analyse large amounts of data. The Deep Learning approach works analogously to the human brain. First, data are extracted and then analysed; finally, findings or predictions are made. The word 'deep' in this context refers to several hidden layers of neural networks [14]. Compared with classical Machine Learning, the main advantage of Deep Learning is that it can extract high-level features automatically from complex data without being developed by humans [10]. For the structuring of Data Mining and Machine Learning projects, procedure models such as the CRoss-Industry Standard Process for Data Mining (CRISP-DM) [15] or Knowledge Discovery in Databases (KDD) [8] can be used [16]. CRISP-DM focuses on the industrial use of data-driven methods and includes a domain-specific view. Figure 3 gives a process overview. **Digital Engineering toolboxes** are necessary for the application of Data Mining and Machine Learning methods. A toolbox describes a collection of different tools within the same context. A **tool** is an action, operation or object that supports or facilitates other actions, operations or objects. Such tools implement algorithms such as k-nearest neighbour, depicted in white boxes in Figure 2[17]. An initial market overview of software for Data Mining and Machine Learning is given by Gartner, Inc. [18]. The authors classified 20 toolbox vendors into four categories based on their applicability and completeness of vision: market leaders, followers, visionaries and niche players. It is essential to keep in mind that the market overview mainly shows commercial toolboxes. In addition, there is also open-source software that is equally powerful. Data Mining and Machine Learning software include stand-alone products and a complementary selection of packages, components, libraries or frameworks. For the broad integration of data-driven methods into existing product development processes, there is a need for specialised employees. Especially, method knowledge--e.g., concerning potentials, weaknesses and prerequisites [19]--is necessary [20]. Moreover, given the continuously expanding number of available toolboxes for Data Mining and Machine Learning, choosing the right software is becoming increasingly challenging [21]. Therefore, introducing methods often fails because selecting methods is complicated, since few best  
Figure 3: CRISP-DM process following [15]. practices are publicly available. Furthermore, possible fields of application are unknown to the companies. In engineering design, the use of methodological development processes is well known. Therefore, several different models are available [22; 23]. These models focus on the mechanical product development process and do not consider electronics or software. Therefore, newer models focus on mechatronic product development processes [24; 25]. In general, product development processes describe the procedure of continuous problem solving, starting with unsolved requirements and leading to the final product [1]. Most of the product development processes are milestone-driven, with an eminent focus on time-frames and deliverables [1]. Although there are several theoretical methods for product development, the industrial practice may differ from those. Nevertheless, the basic ideas remain the same.