In-Process Digital Monitoring of Additive Manufacturing: Proposed Machine Learning Approach and Potential Implications o
Additive Manufacturing (AM) technologies have recently gained significance amongst industries as well as everyday consumers. This is largely due to the benefits that they offer in terms of design freedom, lead-time reduction, mass-customization as well as
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A. Charles et al.
1 Introduction Industry 4.0, also referred to as ‘I4.0’, is the term that is commonly used to describe the fourth industrial revolution. The term ‘Industry 4.0’ was coined by a German government project that dealt with advanced technologies that promoted the computerization/digitalization of the manufacturing industry. Therefore, one of the underlying aims of I4.0 is to create a network of digital manufacturing assets and highly skilled people along the entire value chain of a production process wherein each step can be controlled autonomously [3, 12, 24]. I4.0 is an umbrella term that is used to group together various automation, digital and social trends. Some of the contributing digital technologies include: The Internet of Things (IoT), Augmented reality/Virtual reality, Autonomous robots, Additive Manufacturing (AM), Big data analytics, Machine learning and AI, Smart sensors and actuators, Cloud computing and Cyber security. Additive Manufacturing is considered to be one of the key enabling technologies (KET) of I4.0 since it is first and foremost a digital manufacturing technology [7, 10, 16]. Since all pre-processing, designing, modelling, simulation, build process generation, monitoring as well as post-processing can all be accomplished using computer/digital systems [20]. Therefore, except for some tasks such as material loading or support removal, an AM machine can work completely autonomously [11]. Moshiri et al. have developed a framework to automate even the material loading and support removal processes for developing a first-time-right smart manufacturing system for tooling production [17]. Compared to conventional manufacturing techniques, AM technologies are considered to be more sustainable, this is due to the capacity of AM to only use the exact amount of material as is required by a part. Whereas in subtractive manufacturing a component usually starts as a block of solid bulk material which is then machined and finally reduced to its final shape. Resulting in large amount of scrap material that is produced and wasted. Niaki et al. have identified and presented the various determinants to clarify the role of sustainability and its benefits in the decision making process for the adoption of additive manufacturing my manufacturing companies. However, their investigations concluded that the biggest driver for the adoption of AM is the design freedom and its sustainability benefits are rarely a driver for adoption, even though literature claim immense benefits for sustainability [18]. While Böckin el al investigated the prevalence of AM in the automotive industry and conducted an assessment of environmental impact. Their findings showed that with the current levels of implementation of AM showed only moderate or negligible environmental improvements and concluded that future implementation of AM should seek to exploit the benefits of the technology as well as potential offered in remanufacturing and repairing [2]. This is in accordance with the view of the authors and this p
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