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

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Procedia CIRP 00 (2017) 000–000

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Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

28th CIRP Design Conference, May 2018, Nantes, France

A new methodology to analyze the functional and physical architecture of

existing products for an assembly oriented product family identification

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France * Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu

Abstract

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Keywords: Assembly; Design method; Family identification

1. Introduction

Due to the fast development in the domain of communication and an ongoing trend of digitization and digitalization, manufacturing enterprises are facing important challenges in today’s market environments: a continuing tendency towards reduction of product development times and shortened product lifecycles. In addition, there is an increasing demand of customization, being at the same time in a global competition with competitors all over the world. This trend, which is inducing the development from macro to micro markets, results in diminished lot sizes due to augmenting product varieties (high-volume to low-volume production) [1]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing production system, it is important to have a precise knowledge

of the product range and characteristics manufactured and/or assembled in this system. In this context, the main challenge in modelling and analysis is now not only to cope with single products, a limited product range or existing product families, but also to be able to analyze and to compare products to define new product families. It can be observed that classical existing product families are regrouped in function of clients or features. However, assembly oriented product families are hardly to find.

On the product family level, products differ mainly in two main characteristics: (i) the number of components and (ii) the type of components (e.g. mechanical, electrical, electronical).

Classical methodologies considering mainly single products or solitary, already existing product families analyze the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this

Procedia CIRP 98 (2021) 348–353

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering. 10.1016/j.procir.2021.01.115

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering.

ScienceDirect

Procedia CIRP 00 (2021) 000–000

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering.

28th CIRP Conference on Life Cycle Engineering

Development of a Decision Support System for 3D Printing Processes

based on Cyber Physical Production Systems

Rishi Kumar

a

_{*, Christopher Rogall}

b

_{, Sebastian Thiede}

c

_{, Christoph Herrmann}

b

_{, Kuldip Singh}

Sangwan

a

a_{Birla Institute of Technology and Science Pilani, Pilani Campus, India, 333031}

b_{Technische Universität Braunschweig – Institute of Machine Tools and Production Technology (IWF), Chair of Sustainable Manufacturing and Life Cycle}

Engineering, Langer Kamp 19b, 38106 Braunschweig, Germany

c_{University of Twente Enschede, Overijssel, Netherlands}

*Corresponding author: Telephone: +919879657045, E-mail: p20180035@pilani.bits-pilani.ac.in

Abstract

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.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering. Keywords: Cyber Physical Production System; Carbon Footprint; Decision Support System; Resource Efficiency

1. Introduction

The convergence of physical systems and virtual worlds resulted in a wide variety of parallel developments in the last few decades. Industry 4.0, represented by CPPS, is the convergence of computer engineering, data science engineering and manufacturing engineering [1]. Physical systems such as smart machines, shop floors and production facilities autonomously and independently exchange information, control and monitor the industrial process. Besides digitalization, sustainability is an important topic for manufacturing. A vast amount of resources and energy are Nomenclature

AM Additive Manufacturing PMS Printer Management Software CPPS Cyber Physical Production System ANOVA Analysis of Variance

IoT Internet of Things LH Layer Height PLA Polylactic Acid

DSS Decision Support System

MQTT Message Queuing Telemetry Transport

Available online at www.sciencedirect.com

ScienceDirect

Procedia CIRP 00 (2021) 000–000

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering.

28th CIRP Conference on Life Cycle Engineering

Development of a Decision Support System for 3D Printing Processes

based on Cyber Physical Production Systems

Rishi Kumar

a

_{*, Christopher Rogall}

b

_{, Sebastian Thiede}

c

_{, Christoph Herrmann}

b

_{, Kuldip Singh}

Sangwan

a

a_{Birla Institute of Technology and Science Pilani, Pilani Campus, India, 333031}

b_{Technische Universität Braunschweig – Institute of Machine Tools and Production Technology (IWF), Chair of Sustainable Manufacturing and Life Cycle}

Engineering, Langer Kamp 19b, 38106 Braunschweig, Germany

c_{University of Twente Enschede, Overijssel, Netherlands}

*Corresponding author: Telephone: +919879657045, E-mail: p20180035@pilani.bits-pilani.ac.in

Abstract

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.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering. Keywords: Cyber Physical Production System; Carbon Footprint; Decision Support System; Resource Efficiency

1. Introduction

The convergence of physical systems and virtual worlds resulted in a wide variety of parallel developments in the last few decades. Industry 4.0, represented by CPPS, is the convergence of computer engineering, data science engineering and manufacturing engineering [1]. Physical systems such as smart machines, shop floors and production facilities autonomously and independently exchange information, control and monitor the industrial process. Besides digitalization, sustainability is an important topic for manufacturing. A vast amount of resources and energy are Nomenclature

AM Additive Manufacturing PMS Printer Management Software CPPS Cyber Physical Production System ANOVA Analysis of Variance

IoT Internet of Things LH Layer Height PLA Polylactic Acid

DSS Decision Support System

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consumed that has significant concern for the manufacturing industry as it adversely impacts the environment [2]. An estimation carried out for the carbon emissions resulting from energy consumption forecast that the carbon emissions would most likely cross 36 billion metric tons in 2020 with an expected yearly increment of 1.4% [3]. There is a need to reduce resource and energy consumption by developing new methods and tool or finding alternative solutions. AM technology, especially 3D printing, is considered one of the technologies to deal with this problem. It is one of the technologies within Industry 4.o with the potential of designing and manufacturing products with several complications and challenges restricted only by the imaginations of the individuals [4]. American Society for Testing and Materials (ASTM) defines 3D printing technology as an automatic process by which computer-aided design is used to fabricate a product layer upon layer through material deposition. Conversely, in traditional manufacturing, unwanted material is removed from a workpiece in the form of chips [5]. There are several widely adopted AM technologies, such as fused deposition modelling (FDM), selective laser melting (SLM), selective laser sintering (SLS), and stereolithography. These are widely used for producing customized products; functional, pre-surgical, & conceptual models; highly complex aircraft components, etc. The sustainability advantages of AM are: generation of less waste, fabricating complex as well as improved geometries for lightweight applications, reducing inventory waste [6]. FDM is the most widely used AM technology due to its ability to fabricate products in a user-friendly setting in a clean and safe environment.

The paper is organized as follows: Section 2 provides literature review. Section 3 presents a CPPS framework for 3D printing sustainability with an experimental setup in detail, and section 4 describes the methodology. Section 5 discusses the results obtained, and finally, section 6 summarizes and concludes relevant results with the future scope.

2. Literature Survey

The recent studies reflect a wide range of research topics such as integration of intelligent sensors on 3D printers to make it smarter, improve its efficiencies and effectiveness, investigate the effect of different printing parameters on energy consumption and emissions, incorporate real-time monitoring, quantify and access the environmental impact. A brief review is provided in this section.

A study conducted to explore the effect of printing parameters on responses such as energy consumption supports that a significant amount of energy is consumed for heating the print bed and for maintaining its temperature [7]. Similarly, a framework implemented for eco-design and cleaner production initiatives proved that there is a strong correlation among energy consumption, product design and process parameters. It was suggested to improve the manufacturing energy efficiency of the product at the stages of both design and manufacturing [8]. Furthermore, A LCA study indicated that 3D printed products have lesser environmental impacts when compared with traditional manufacturing for the same product [9]. In addition, recent advancements in IoT technologies have facilitated the monitoring of 3D printing processes with augmented reality glasses. This allowed the user to view and identify potential flaws immediately during the printing

process [10].

Life cycle assessment is considered as a reliable, scientific and comprehensive method to deal with the environmental sustainability of human activities, not only limited to internal and external information supply but also for decision support [11]. Carbon footprint is the measurement of the total amount of greenhouse gases emission for a country, city or product, along its life cycle including inbound and outbound supply chains and disposal at the end of life. As an environmental impact indicator, this is useful for decision support, performance evaluations, and for lawmakers to frame, establish, and implement policies and regulations related to climate changes [12]. Dynamic assessment of carbon footprint based on the energy and material usage for the 3D printed object has several benefits such as (1) Environmental information can be shared with different stakeholders internally and externally leading to robust environmental management system; (2) Sustainability managers can quantify environmental performance and align their activities in compliance with the existing regulation for product and process; (3) Manufacturing staffs can be proactively involved in sustainability thinking in work culture leading to process improvement, organizational, and social changes [13].

A decision support system for the 3D printing process based on CPPS for dynamic assessment of carbon footprint considering energy and material usage for the 3D printed object is still missing in the existing literature. There is still a research gap clearly describing a decision support system (DSS) approach for 3D Printing Processes based on CPPS, one of the promising technologies of industry 4.0. Also, analyzing the effects of input parameters such as design scale, infill and layer height on responses such as energy consumption, build time, and scrap weight is missing in the literatures. Therefore, the effect of printing parameters on the responses is required to be investigated through an analytical model by conducting experiments.

3. CPPS Framework for 3D printing

This paper proposes a decision support system for the 3D printing process based on CPPS through an automated measurement system allowing better communication of resource and energy consumption, thereby reducing the complexity involved in manual approaches. Therefore, A CPPS framework for the sustainability of the 3D printing process has been developed, allowing users to dynamically assess the carbon footprint based on the energy and material usage for their 3D printed object. An operator or a user who is also a customer is enabled by the instant recommendation of parameters (infill and layer height) for each new product design along with specific carbon footprint for every single product. The product-related manufacturing energy consumption with carbon footprint is estimated by conducting experiments based on Taguchi L-9 orthogonal array. The proposed framework based on CPPS provides real-time decision support to change the variables for minimizing carbon footprint.

The statistical analysis has also been performed to investigate the effect of product design and printing parameters on the responses and determine the optimal combination of these parameters to minimize the carbon footprint.

A CPPS Framework for 3D Printing sustainability (both energy as well as material) has been adapted for the 3D printing

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use case from the framework of assessment procedure reported for analysing and developing a CPPS [14]. This integrates four fundamental elements of CPPS, i.e. the physical world, data acquisition, the cyber world and the visualization, as shown in Fig. 1.

3.1. Physical World

Fig. 2 illustrates the physical world consisting of low-cost physical hardware, smart devices, and server systems. The Prusa 3D printer integrated with smart sensors systems is used to measure significant measurands (environmental and process data) in real-time. The sensor node measures real-time environmental data such as vibrations, ambient temperatures, humidity etc. An automated smart plug with current meter measures real-time energy consumption data, and a camera captures video stream for real-time monitoring and control. The scale is provided for weighing the build part, and the scrap produced. Raspberry pi evaluates data resulting from different devices and display, the real-time readout of the data implemented during the printing process.

3.2. Data acquisition

Data groups are classified mainly into three groups, namely product, process and external factors [15]. The data results either from the printing process itself and is read out using PMS or comes from external sensors attached to the printer. At the same time, the entire energy flow of the 3D printer is recorded via a smart plug, which precisely documents the power consumption. The 3D printer is connected to PMS installed on the first raspberry pi and the second raspberry pi is connected to a network via ethernet to serve as a wi-fi access point for all other devices and establishes a connection to the network. All this collected data is transmitted to an MQTT broker, which consolidates and provides the data centrally. Data acquisition is used to gather these influencing factors and state variables

for treatment and further storage in suitable databases. Subsequently, data is stored and data querying for getting the request or demand for specific information or data from databases. Table 1 presents a summary of measured parameters with the measured techniques implemented for the 3D printing use case.

Table 1 Summary of Measured Parameters Measurement

Techniques Measured Parameters

XDK Sensor Node External Temperature Humidity

Acceleration Gyroscope Magnetometer Pressure

Printer Management Software Extruder Temperature Bed Temperature Build Time Total weight

WebCam Live monitoring

Smart Plug Energy flow

Weighing Machine Part and Scrap weight

3.3. Cyber World

The proposed framework can collect and analyse real-time data with machine learning algorithms. Moreover, there is a potential to predict energy demand with an investigation of influencing factors depicting the potential fields of action for improving energy efficiency. Condition monitoring can take place based on the energy, temperature and system health values crossing the threshold values through automatic control actions. This may result in preventing dynamic breakdowns, adaptive adjustments for process parameters, and fault prognosis.

A wide number of cloud platforms such as amazon web services, Microsoft Azure, Google, IBM cloud etc. are available nowadays which can perform these tasks much easier as compared to open source platforms and does not require Figure 1 CPPS Framework for 3-D printing Sustainability (adapted from [11]

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programming knowledge in depth hence there is no need of software programmers to perform these tasks.

3.4. Visualization

The virtual models are utilized as a decision support system for various stakeholders or embedded with automated control through physical actions to take place. The virtual model can be used for design, operation, environmental or economic assessment, and maintenance related use cases. In the case of 3D printing, design recommendations are provided by the customer who is themselves, designer. Process parameters are provided by the customer who is also an operator. In the third use case, decision support for environmental or economic assessment is provided by the customer, manager and environmental engineer etc. Similarly, in the last use case, maintenance actions are recommended by the maintenance engineer. All these use cases require different sensor data, cyber models etc. In the proposed framework, design, operation and assessment are used and plays an important role because all the other steps depend on the sensor data and cyber models. The loop is closed, and the user is required to be in focus for at least through appropriate visualization of what is happening in the system. Depending on the application, there are several ways to visualize the data. On the one hand, the data can be visualized directly on the raspberry pi employing a dashboard, on the other hand, it is possible that the data can be transferred directly from the MQTT broker to a cloud or database environment.

4. Methodology

Two data groups, namely product design and process parameters, have been selected, leaving out external factors. Three input parameters have been selected product design (scale), process parameters (infill, layer height). The reasons for selecting these input parameters is that these factors influence several responses, such as build time, print quality, resolution, and print strength. Infill is the percentage of material filled inside the 3D Printed object. Generally, it varies between 20% and 25% without comprising durability and material consumption, but for the minimum cost, the best infill range is considered between 10% and 15% [16].

Therefore, the best trade-off for infill percentage is selected between 10% to 30% for this use case. Similarly, layer height is every step the 3D printer moves in the vertical direction and its range varies from printer to printer The range for standard smallest layer height generally varies between 50 and 100 microns (0.05 and 0.1 mm) from printer to printer [17]. Therefore, the layer height range is selected from 0.10 mm to 0.20 mm. The printing parameters and their levels are shown in Table 2. PLA is biodegradable; thermoplastic polymer has been considered as filament material. The responses are energy consumption values, build time, scrap weight and carbon footprint produced for 3D printing of spoon as an object. Taguchi L-9 array has been selected to design the experiments so that experimental cost is reduced, quality is improved, and robust design is obtained. Also, all the factors can be simultaneously optimized, and more quantitative information can be obtained from less number of experiments [18]. Table 2 Printing Parameters and Their Levels based on Taguchi L-9 Orthogonal array

Parameters Units Levels

Low High

Infill % 10 30

Layer Height mm 0.10 0.20

Scale % 50 100

4.1 Carbon footprint Calculation

4.1.1. Carbon footprint calculation for the filament Material

As per the referred data, one kg of PLA granules consumes energy in the range of 14 to 17 kWh [19]. Hence, the higher limit value is taken for calculation which is 17 kWh. Also, 0.560 kg of carbon dioxide is emitted per kWh by German Electricity in 2016 [20].

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐸𝐸𝑒𝑒𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒 𝐸𝐸𝑜𝑜 𝐸𝐸𝑚𝑚𝑚𝑚𝐸𝐸𝑒𝑒𝐸𝐸𝑚𝑚𝑚𝑚 (𝑘𝑘𝑘𝑘ℎ) =

(𝑘𝑘𝐸𝐸𝐸𝐸𝑒𝑒ℎ𝑚𝑚 𝐸𝐸𝑜𝑜 3𝐷𝐷 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒𝑚𝑚𝐸𝐸𝐸𝐸 𝐸𝐸𝐸𝐸𝑜𝑜𝐸𝐸𝑜𝑜𝑚𝑚 𝐸𝐸𝑒𝑒 𝑘𝑘𝑒𝑒) × (17 𝑘𝑘𝑘𝑘ℎ) (1) Carbon footprint for filament material (kg Co2) =

(Energy Consumed) × (0.560 kgCo2) (2)

4.1.2. Carbon footprint calculation during 3D printing Process

Carbon footprint (kg Co2) =

(Energy Consumed in kWh) × (0.560 kgCo2) (3)

4.1.3. Total Carbon footprint

It is the sum of Carbon footprint calculation for the filament Material and Carbon footprint calculation during the 3D printing process.

Total footprint (kg Co2) = (2) × (3) (4) Table 3 lists the experimental results along with the product-related manufacturing energy consumption with carbon footprint estimated using equations (1) to (4).

Figure 2 Physical World with hardware and sensor integrations [adapted from 23] Sens or s 3D Printer Physical world Process data gathering Environmental & process data: - energy consumption - material weight - temperatures - vibrations - etc. Scale Current meter Connection point Raspberry Pi

References: 3D printer: prusa3d.de, Raspberry: raspberrypi.org, Bosch XDK: bosch-connectivity.com, Scale: Sartorius.de

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Table 3 Experimental results along with total carbon footprint with PLA as a filament Material In fil l (% ) La ye r H ei gh t ( m m ) Scal e ( % ) En erg y Co ns um pt io n (kW h) Bu ild T ime(m in ) Pa rt W ei gh t (g ) Scr ap W ei gh t ( g) To ta l W ei gh t ( g) To ta l f oot pr int (k g C O 2 ) 10 0.1 50 0.07 32 1.13 1.01 2.14 0.06 10 0.15 75 0.13 60 3.69 2.65 6.34 0.13 10 0.2 100 0.19 94 8.35 5.76 14.11 0.24 20 0.1 75 0.15 71 3.67 2.22 5.89 0.14 20 0.15 100 0.23 111 8.42 5.03 13.45 0.26 20 0.2 50 0.07 30 1.14 1.08 2.22 0.06 30 0.1 100 0.26 138 8.01 4.23 12.24 0.26 30 0.15 50 0.08 32 1.13 1.01 2.14 0.07 30 0.2 75 0.13 60 3.69 2.65 6.34 0.13

4.2. Decision Support System (DSS) Implementation

Appropriate and timely decisions making is a crucial solution for industries to sustain in the era of industry 4.0 [21]. The DSS is found to be useful in obtaining additional information about performance of the shop floor or assembly line, evaluating a wide variety of scenarios for improving responses, and lastly obtaining the optimum combination of decision variables from multiple possible solutions in a comparatively short span of time. DSS forms an important part of cognition level placed at the fourth level of architecture out of five hierarchical stages for implementing cyber-physical systems proposed by Lee et

al. [22].

Consequently, there is an urgent need to develop a Decision Support System (DSS) for 3D Printing Processes based on Cyber Physical Production Systems to provide knowledge, support timely, informed, and enlightened decisions thereby enable managerial responsibilities.

CPPS is generally applied in condition monitoring for process and quality, predictive maintenance etc. providing several advantages in terms of productivity and efficiency improvement as well as enhancing product quality [14]. The CPPS framework comprises four interconnected components namely the physical world, data acquisition, cyber world and decision support system. These components interact with each other to support operational parameters control decisions. The physical world comprising 3D printer integrated with multiple sensors to make it fully automatic system for

data-acquisition of product design (scale), process parameters (infill, layer height) and responses such as energy consumption values, build time, scrap weight. CPPS is required in the present use case to connect the physical world of 3D printer with the cyber world depending on data acquisition.

The current Decision Support System for 3D Printing Processes based on Cyber Physical Production Systems elaborates a generalized framework which is generic in nature for a wide range of use cases. Here this has been applied on the instant recommendation of parameters (infill and layer height) for each new product design along with specific carbon footprint for every single product. The decision support for environmental or economic assessment is provided to the customer, manager and environmental engineer etc. to choose the optimum combination of process parameters minimizing the negative effect on the environment.

5. Results and Discussion

Fig.3 shows the CAD image of the 3D printed spoon on the Prusa slicer. Table 4 illustrates ANOVA results for means of total carbon footprint, build time, and energy consumed. A low P-value (≤0.05) indicates statistical significance for the corresponding response (α = 0.05) or 95% confidence level. Following results can be interpreted. Scale refers to the design parameter and has the most significant effect on all the responses. The scale is fixed by the designer or the operator and can’t be changed anymore. Infill and layer height are process parameters and can be adjusted based on the feedback of the responses. A recommendation for the optimum process parameter (infill and LH) combination, for a 100% scale to minimize carbon footprint is marked bold in row 3 of Table 3 with the infill, and LH values found to be 10 % and 0.2 mm respectively.

The main advantage of CPPS over normal temporal measurements is that a user is enabled by instant recommendation of parameters (infill and layer height) for each new product design along with specific carbon footprint for every single product. Therefore, decision support for environmental or economic assessment is thus provided to the customer, manager and environmental engineer etc. to choose the optimum combination of process parameters.

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Table 4 Analysis of Variance (ANOVA) Results for means of total carbon footprint, build time, and energy consumed

Total Carbon Footprint Energy Consumed Build Time

Sources DF F- Value P- Value F- Value P- Value F- Value P- Value

Infill 2 9.16 0.098 4.00 0.200 1.81 0.356

Layer Height 2 8.72 0.103 4.69 0.176 3.12 0.243

Scale 2 3537.56 0.000 123.31 0.008 58.27 0.017

6. Conclusions and outlook

A brief introduction about Industry 4.0 and the role of IoT in manufacturing have been highlighted. A CPPS framework for the environmental sustainability of the 3D printing process has been proposed supporting the derivation of improved strategies for product design and production. The product-related manufacturing energy consumption with carbon footprint has been estimated so that an operator or a user is enabled by instant recommendation of parameters (infill and layer height) for each new product design enhancing the decision support capabilities to choose the optimum combination of process parameters minimizing the negative effect on the environment. Also, the statistical analysis has also been performed based on Taguchi L-9 orthogonal array. The optimal combination of these parameters is determined to minimize the carbon footprint.

This work is limited to 3D printing with FDM technique on PLA as a filament material with only two process parameters,

i.e. infill and layer height Operating parameters such as

temperature, feed rate, fan speed can also be included in the further study.

The system boundaries for the presented approach include human activities, work culture, organizational, social changes, and environmental performances. The first system boundary deals with human activities responsible for environmental information shared with different stakeholders such as an operator or user, sustainability mangers, and manufacturing staffs. The second system boundary defines work culture, organizational, and social changes with the proactive involvement of different stakeholders in sustainability thinking through dynamic assessment of carbon footprint. The third system boundary defines environmental performances through process improvement and alignment of activities in compliance with the existing regulation.

Acknowledgements

This research is part of the project “JInGEL – Joint Indo German Experience Lab”, a joint project between Technische Universität Braunschweig and Birla Institute of Technology and Science Pilani, funded by the German Academic Exchange Service (DAAD) under grant number 57219215. The authors are thankful for the funding and support.

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