There are several studies available proposing key challenges of manufacturing on a global level. The challenges manufacturing faces today are different from the challenges in the past.
Examples are the US through ‘Executive Actions to Strengthen Advanced Manufacturing in America’ (White House, 2014) and the European Union with their ‘Factories of the Future’ (European Commission, 2016) initiative. However, in the last years, several initiatives to revamp the manufacturing sector were started. Several mature economies experienced a reduction of the manufacturing contribution toward their GDP over the last decades. Manufacturing is a very established industry, however the importance of it cannot be rated high enough. This provides a basis for the later argumentation of machine learning being an appropriate tool to for manufacturers to face those challenges head on.ġ.1. In the following section, the current challenges manufacturing faces are illustrated. Provide the reader with a high-level understanding of the advantages and disadvantages of certain methods with respect to manufacturing application. Present an overview of the different areas of machine learning and propose an overall structuring Introduce the terminology used in the respective fields
For many manufacturing practitioners, this represents a barrier regarding the adoption of these powerful tools and thus may hinder the utilization of the vast amounts of data increasingly being available. However, the field of machine learning is very diverse and many different algorithms, theories, and methods are available. data mining (DM), artificial intelligence (AI), knowledge discovery (KD) from databases, etc.). One of the most exciting developments is in the area of machine learning (incl. statistical learning) and availability of easy-to-use, often freely available (software) tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. New developments in certain domains like mathematics and computer science (e.g. for quality improvement initiatives, manufacturing cost estimation and/or process optimization, better understanding of the customer’s requirements, etc., support is needed to handle the high dimensionality, complexity, and dynamics involved (Davis et al., 2015 Loyer, Henriques, Fontul, & Wiseall, 2016 Wuest, 2015).
Overall, it can be safely concluded, the manufacturing industry has to accept that in order to benefit from the increased data availability, e.g. distract from the main issues/causalities or lead to delayed or wrong conclusions about appropriate actions (Lang, 2007). However, it has been recognized that much information can also propose a challenge and may have a negative impact as it can, e.g. quality-related data offers potential to improve process and product quality sustainably (Elangovan, Sakthivel, Saravanamurugan, Nair, & Sugumaran, 2015). This increase and availability of large amounts of data is often referred to as Big Data (Lee, Lapira, Bagheri, & Kao, 2013). Industrie 4.0 (Germany), Smart Manufacturing (USA), and Smart Factory (South Korea). Different names are used for this phenomenon, e.g. sensor data from the production line, environmental data, machine tool parameters, etc. These data compromise a variety of different formats, semantics, quality, e.g. The manufacturing industry today is experiencing a never seen increase in available data (Chand & Davis, 2010).
0 kommentar(er)
