Systematic Development of Sustainability-Oriented Cyber-Physical Production Systems

Manufacturing companies increasingly have to address the risks and contributions related to their environmental impacts. Therefore, more data are needed in order to provide full transparency with regard to production, and to highlight the potential relationships between the process data and the environmental impacts. In order to achieve this data transparency, targeted digitalization is needed that is tailored to the goal of reaching minimized environmental impacts. Cyber-physical production systems (CPPSs) are central for the digitalization of manufacturing. However, they may also come with an initial environmental backpack. Due to unawareness of relevant interdependencies when setting up CPPS, data may be collected which is not helpful or necessary for the development of sustainability-oriented CPPS. Therefore, a critical assessment is required which data is necessary to support sustainable manufacturing and to avoid unreflective data collection. This requires the identification of the relevant factors and their interdependencies within the context of sustainability in production. By identifying the influencing factors, the measurement strategy can be linked to the appropriate sensor technologies that explicitly contribute to the target fulfillment. The design of more sustainable data structures using a cross-impact analysis is illustrated in this paper as a generic methodological approach, which will be applied to a 3D-printing use case

Systematic Development of Sustainability-Oriented Cyber-Physical Production Systems

Manufacturing companies increasingly have to address the risks and contributions related to their environmental impacts. Therefore, more data are needed in order to provide full transparency with regard to production, and to highlight the potential relationships between the process data and the environmental impacts. In order to achieve this data transparency, targeted digitalization is needed that is tailored to the goal of reaching minimized environmental impacts. Cyber-physical production systems (CPPSs) are central for the digitalization of manufacturing. However, they may also come with an initial environmental backpack. Due to unawareness of relevant interdependencies when setting up CPPS, data may be collected which is not helpful or necessary for the development of sustainability-oriented CPPS. Therefore, a critical assessment is required which data is necessary to support sustainable manufacturing and to avoid unreflective data collection. This requires the identification of the relevant factors and their interdependencies within the context of sustainability in production. By identifying the influencing factors, the measurement strategy can be linked to the appropriate sensor technologies that explicitly contribute to the target fulfillment. The design of more sustainable data structures using a cross-impact analysis is illustrated in this paper as a generic methodological approach, which will be applied to a 3D-printing use case.</jats:p

Modeling energy and resource use in additive manufacturing of automotive series parts with multi-jet fusion and selective laser sintering

With additive manufacturing (AM) becoming a competitive manufacturing process for low to medium production volumes, rapid manufacturing becomes an increasingly relevant manufacturing approach. However, regulations and customers demand more eco-efficient life-cycles of products. This requires engineers and designers to pre-select between productive AM processes like selective laser sintering (SLS) and multi-jet fusion (MJF), based on their expected environmental impact in series production. As SLS already debuted in the mid-1980s, researches broadly explored parts' mechanical properties, energy and resource use. The multi-jet fusion (MJF) technology, introduced in 2017, delivers comparable part properties at considerably higher print speeds. However, its energy and resource use is still scarcely covered. To close this gap, this publication develops a model for evaluation of energy and resource utilization based on a case study with an automotive exterior series part using an EOS P396 SLS and a HP 4200 MJF machine. Data from measurements in energy and material consumption as well as the print job shows a good predictability and builds the basis for an environmental assessment. The derived model and its functional blocks allow estimation and comparison of sustainability for different use cases in rapid manufacturing with MJF and SLS. Despite the process similarities, results concerning greenhouse gas emissions and cumulative energy demand are different. The gained insights enhance pre-selection of manufacturing strategies, a suitable printing technology and the evaluation of AM processes during manufacturing according to sustainability aspects. Printer manufacturers and users may find this research insightful for improvements in sustainability and comparability of future AM processes

Development of a Decision Support System for 3D Printing Processes based on Cyber Physical Production Systems

3D printing, an additive manufacturing (AM) technology, potentially provides sustainability advantages such as less waste generation, lightweight geometries, reduced material and energy consumption, lower inventory waste, etc. This paper proposes a decision support system for the 3D printing process based on Cyber Physical Production System (CPPS). The user is enabled to dynamically assess the carbon footprint based on the energy and material usage for their 3D printed object. A CPPS framework for the environmental sustainability of the 3D printing process is presented, which supports the derivation of improved strategies for product design and production. A physical world for 3D printing is used with the internet of things (IoT) devices like sensor node, webcam, smart plugs, and raspberry pi to host printer Management Software (PMS) for real-time monitoring and control of material and energy consumption during the printing process. Experiments have been conducted based on Taguchi L9 orthogonal array with polylactic Acid (PLA) as a filament material to estimate the product-related manufacturing energy consumption with the carbon footprint. The proposed framework can be effectively used by the users to supports the decision-making process for saving resources and energy; and minimizing the effect on the environment