# Machine Learning in Manufacturing towards Industry 4.0: From For Now' to 'Four-Know'
###### Abstract  
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, 'Four-Know' (Know-what, Know-why, Know-when, Know-how) and 'Four-Level' (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments. machine learning; Industry 4.0; manufacturing; artificial intelligence; smart manufacturing; digitization +
Footnote †: journal: Applied  
0000-0001-6000-00001-6000-00001-60000-0001-600001-600  
Machine Learning (ML) is one of the cornerstones for making manufacturing (more) intelligent, and thereby providing it with the needed capabilities towards greater flexibility and adaptability [10]. These advances in ML are shifting the traditional manufacturing era into the smart manufacturing era of Industry 4.0 [11]. Therefore, ML plays an increasingly important role in manufacturing domain together with digital solutions and advanced technologies, including the Industrial Internet of Things (IIoT), additive manufacturing, digital twins, advanced robotics, cloud computing, and augmented/virtual reality [11]. ML refers to a field of Artificial Intelligence (AI) that covers algorithms learning directly from their input data [12]. Despite most researchers focusing on finding a single suitable ML solution for a specific problem, efforts have already been undertaken to reveal the entire scope of ML in manufacturing. [PERSON] et al. presented frequently-used deep learning algorithms along with an assessment of their applications towards making manufacturing \"smart\" in their 2018 survey [13]. In particular, they discussed four learning models: Convolutional Neural Networks, Restricted Boltzmann Machines, Auto-Encoders, and Recurrent Neural Networks. In their recent literature review on \"Machine Learning for Industrial Applications\", [PERSON] et al. [12] identified, classified, and analyzed 147 papers published during a twenty-year time span from Jan. 2000 to Jan. 2020. In addition, they provided a classification on the basis of application domains in terms of both industrial areas and processes, as well as their respective subareas. Within these domains, the authors analyzed the different trends concerning supervised, unsupervised, and reinforced learning techniques, including the most commonly used algorithms, Neural Networks (NNs), Support Vector Machine (SVM), and Tree-Based (TB) techniques. The goal of another literature review from [PERSON] and [PERSON] [14] was to provide a sound comprehension of the major approaches and algorithms from the fields of ML and data mining (DM) that have been used to improve manufacturing in the recent past. Similarly, they investigated research articles from the period of the past two decades and grouped the identified articles under four main subjects: scheduling, monitoring, quality, and failure.