Journal-Development of a systematic approach to identify non-value-adding

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ScienceDirect

www.elsevier.com/locate/procedia Procedia CIRP 104 (2021) 1613–1618

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

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 54th CIRP Conference on Manufacturing System

Keywords:Additive manufacturing; process optimization; process recording; laser beam melting

1. Introduction

Additive manufacturing processes enable a tool-free and cost-effective production of complex and individual components [1]. By integrating additional functions, exploiting light- weight construction potentials, or consolidating several components, added value can be generated in additive-manufactured components [2]. This added value is also necessary to be able to use additive manufacturing increasingly economically in the industrial environment despite the often high material and process costs [3]. One reason for the high costs of additive manufacturing processes is the large number of operations within the additive process chains with only minor value creation for the manufactured component [4].

Laser beam melting (LBM) is considered the most fre- quently used additive manufacturing process for metallic components [5]. Typical applications of this manufacturing process are found particularly in the aerospace, automotive, and me- chanical engineering sectors [6]. One focus of current research in this additive manufacturing process is on in-processing. Most

investigations include manufacturing parameters and influenc- ing factors, the achievable component properties, the increase of the build-up rate, and the processability of new materials [7–

9]. The present paper aims to increase the potential of LBM by visualizing non-value-adding (NVA) operations across the entire LBM process chain. For this purpose, a holistic approach was developed to identify, e.g., the required time and tools as well as the degree of automation for each operation.

2. State of the Art

2.1. Elimination of waste in lean production systems

The current understanding of production systems is based in large parts on the development of the Toyota Production Sys- tem (TPS) in the 1950s [10,11]. A lean production system serves as a conceptual framework for the production processes of a company which defines how to produce and how to be constituted by high-level principles and concrete methods [11–

13]. The configuration of production systems is characterized

54

^th

CIRP Conference on Manufacturing Systems

Development of a systematic approach to identify non-value-adding operations in the LBM process chain

Hajo Groneberg

^a,

*, Jan Koller

^a,b

, Alexander Mahr

^a,b

, Frank Döpper

^a,b

aUniversity of Bayreuth, Chair Manufacturing and Remanufacturing Technology, Universitätsstraße 30, 95447 Bayreuth, Germany

bFraunhofer Institute for Manufacturing Engineering and Automation IPA, Universitätsstraße 9, 95447 Bayreuth, Germany

* Corresponding author. Tel.: +49 921 78516-229; fax: +49 921 78516-105.E-mail address:[email protected]

Abstract

Additive manufacturing becomes increasingly important in industrial production. Laser beam melting (LBM) prevails amongst metal additive manufacturing and enables tool-free production of complex prototypes and functional parts. From setting up the LBM system to post-processing of manufactured parts, the LBM process chain contains manual and time-consuming process steps without added value. Therefore, a systematic approach for the analysis and evaluation of the LBM process chain was developed with regard to criteria such as time required and the degree of automation. The main influences are identified to enable a more sustainable production and a reduction of non-value-adding operations.

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by strategic objectives to meet market and customer requirements [14–16]. The primary objectives typically consist of the dimensions time, cost and quality, also known as holy trinity, and can be extended by further dimensions like flexibility or sustainability [16–18]. Companies pursue these strategic objectives simultaneously and therefore have to balance conflicting objectives. In this respect, production systems are a methodical system of rules to ensure and increase the competitiveness of the company [19]. Often it is the small and medium-sized enterprises that have not yet implemented a production system in a systematized and structured form [20]. In lean production systems, the identification and avoidance or elimination of waste is one of the central elements to ensure sustained realiza- tion of profit and to achieve the strategic objectives [12,19,21].

In general, three types of deviation pertaining the inefficient allocation of resources in terms of a lean production can be distinguished: waste (jap. muda), unevenness (jap. mura) and overburden (jap. muri) [11,22]. According to OHNO, seven types of waste can be distinguished: transport, inventory, mo- tion, waiting, overproduction, over processing and defects [11,21,22]. Furthermore, the unused skills of operators are considered as an additional type of waste [11].

Through continuous improvement of the processes (jap. kai- zen), all types of waste should be eliminated, and if possible only value-adding (VA) operations should be carried out. Im- proving efficiency and eliminating waste must be implemented throughout the production system [19]. Therefore, in a manufacturing context, three types of operations can be differenti- ated and categorized into [23,24]: VA, necessary but NVA and NVA.The avoidance of waste does not mean that this has to be at the expense of performance, but rather that only those costs have to be avoided that do not add value [21,25]. From a customer-perspective, VA operations produce goods or provide services for which the customer is willing to pay. This involves the conversion or processing of raw materials or semi-finished products in processes like assembly of parts or painting a product [23]. VA operations should be combined and optimized.

Other operations might be NVA by a strict definition, but are necessary according to the current production methods. An example for this type of operation in the context of additive manufacturing is the removal of support material, which does not increase customer benefit, but is a technical necessity for the production of certain geometries. Those operations should be modified and reduced to a minimum. [12,23]

NVA operations, which are not necessary, should be eliminated completely, since they are considered as waste and involve unnecessary actions as described in the seven types of waste. This includes, e.g., the post-processing of components due to errors that occurred in the additive manufacturing process [12].

2.2. NVA operations in laser beam melting

LBM is characterized by a large number of manual and complex operations with a currently low degree of automation [26].

NVA operations do not only occur in infrequently performed process steps, such as maintenance, replacement of process gas

filters, or changing processed metal powder alloy [27]. The re- active and/or toxic effect in combination with the small particle size distribution of the hazardous metal powder leads to a large number of NVA operations, even in standard process steps within the LBM process chain [28,29]. Therefore additional operations must be performed to protect the operator. Examples include putting on and taking off personal protective equipment, such as antistatic wrist bands [29]. Another reason for NVA operations in the LBM process chain is due to necessary quality assurance measures, such as performing measurements to characterize the flow properties of the metal powder or re- placing the coating blade [30]. Additive manufacturing processes require additional process- and application-specific operations, such as inserting the process chamber or ensuring that adhering powder particles have been removed from the cavities of the manufactured components [31]. These NVA operations occur not only during the preparation of the LBM system, but also when transporting the components among process steps or when using of suction equipment during cleaning or removing of metal powder [32].

NVA operations are an obstacle to a broader industrial application of the LBM process. In order to reduce the amount of NVA operations, they must be systematically analyzed in a company-specific manner [33]. This is due to the fact that their share is strongly influenced by the LBM system (e.g. size of the building volume, special designs), by the periphery (e.g. in- terfaces), by the components to be manufactured (e.g. size, complexity, and material) as well as by operational conditions.

2.3. Value-stream mapping

To analyse deviations of an optimal production in a company, it is not sufficient to focus on an individual production process, e.g., a single machine, since only a holistic view of all processes leads to a lean production system [12,34].

The best-known form of process representation is the value stream analysis, which became known especially through ROTHER AND SHOOK[21,34]. The value stream method was developed in the course of the TPS and provides two complemen- tary tools: value stream mapping to determine the actual situation and the value stream design to develop a target state in terms of a lean production system [21].

Value stream mapping is a systematic approach to represent all VA and NVA processes and operations to manufacture a product [34]. Therefore, the whole process chain within a company, from supplier to customer is considered [34]. The visual representation documents the current state of the value stream, enables the identification of potential improvements (especially regarding waste reduction) and forms the basis for the development of a future target state and an improved value stream [21]. For each production process, characteristic parameters, like required resources, cycle time, batch sizes, set-up times, etc. are recorded on site [21]. This data can be used to calculate and analyze other key performance indicators such as production lead time and flow rate or Every Part Every Interval [21].

According to FELDMANN AND GORJ, the process chain of additive manufacturing consists of the generic pre-processing, the

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in-processing and post-processing [12]. The pre-processing includes the creation of a digital model, conversion to a printing format like .stl, preparing and transferring the file to the printer, and setting up the printer. The in-processing includes the actual physical fabrication of the part by solidification material layer- by-layer to generate the object. The post-processing phase includes operations such as removal, cleaning and, if necessary, reworking of the object. [12] This classification makes it diffi- cult to detect waste in the additive manufacturing process because it is not clear how the operator-specific processes are composed. The different paths and simultaneously running processes are only taken into account to a limited extent. In addition, some of the key figures collected using the value stream method are less relevant for additive manufacturing, which generally does not require time-consuming machine set-ups and allows customized production.

2.4. Business Process Model and Notation (BPMN 2.0) Comparable to value stream mapping, BPMN 2.0 can be used for a graphical representation of processes and their inter- relationships. BPMN 2.0 is a graphical specification language, which allows to model and document business and work processes with the help of standardized symbols. One of the primary objectives of BPMN 2.0 is providing an understandable notation for all business users [35].

The international standard ISO/IEC 19510 describes an amalgamation of best practices to define the notation and se- mantics of collaboration, process and choreography diagrams [35]. In general, BPMN 2.0 consists of five elements to create understandable models, while handling the complexity of business processes: flow objects, data, connecting objects, swim lanes and artifacts [35,36].

BPMN 2.0 concentrates on the information flow between involved actors. An important difference to the value stream method is the consideration of different branches in the process flow, e.g. in the form of AND (parallel) and OR (exclusive) branches. This variability is usually not considered in value stream mapping. To continuously improve existing processes, enhancement patterns are repeatedly used, which represent specific process patterns [37].

Approaches to identify NVA operations in the LBM process chain are only briefly described in the literature due to the in- novative process and current focus on performance improvement of the LBM system. There is a need for a systematic approach that considers not only the actual manufacturing process but also operations along the entire process chain. Visualizing how the processes are interrelated can help to identify further potential for improvement. Existing balancing methods are only partially suitable for the application in additive process chains. The investment in time is often too high or involve over-simplification. By applying this method on a reference process chain, optimization measures could be derived, e.g. to simplify workflows, to increase reproducibility, or to reduce the working time of one operator per component.

3. Approach and Methodology

An analysis of the current state of the art showed that no existing method meets the requirements. This includes aspects like a detailed recording of complex and simultaneous processes in different paths along the entire process chain, various periphery, components to be manufactured and operating systems or conditions. The present systematic approach is characterized by a holistic view of the entire LBM process chain, taking into account all operations from setting up the LBM system to component-specific post-processing.

Following the state of the art the present approach focusses on the main target criteria of SCHNEIDER ET AL. [38]: process design (material/information flow, resources), process understanding, process performance and process logic.

Derived from these target criteria, a concept was developed to support the identification of NVA operations along the LBM process chain. In order to apply this approach, it is recom- mended, in line with the Single Minute Exchange of Dies (SMED) lean method, to put together a team of machine operators, lean experts and production planners to carry out the process recording. The process should be recorded Gemba (on site) with the designated operator in order to achieve the most accurate representation of the situation [39].

The developed approach consists of process steps that inter- act with each other. For standardization and greater clarity, each process step has the same structure and uses a uniform acronym defined in a legend. As with BPMN 2.0, process steps are linked to each other with symbols, arrows and connectors as shown in Table 1.

Table 1. Example tools and connectors

Acronyms Tools Connectors

AK Allen key

CL Cloth Parallel Exclusive

CS Zero-point clamping system CT Cleaning tools (brush, cloth) FG Feeler gauge

GL Gloves HF Hall flowmeter PC Powder container PT Post-processing tools

(chisel, file, hammer, pliers) Arrow connector SA Scales

TI Timer US USB-storage

VI Vice

WS Wet separator

Fig. 1. shows an example process step. The grey shaded boxes are for information purposes, whereas the white shaded boxes have to be filled in by the operator. In the first column every sub-step is listed in the same order as the procedure requires, starting by the first sub-step. The final row is blocked for the analysis. Data for the final process step analysis is to be recorded and filled in the white data columns. Nine data columns are integrated in the developed approach. It is recom- mended to fill in the sub-step terms and information for the data columns while recording the process chain.

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Component removal from build platform with band saw

Sub-steps

Degree of VA operations Degree of necessary NVA operations Degree of not necessary NVA operations Degree of automation Build job dependency System dependency Simultaneous to LBM pro- cess Required tools Time [s]

Prepare and connect wet separator 0 0 1 0 0 0 1 WS 63

Attach build platform to band saw 0 1 0 0 1 0 1 AK 48

Adjust band saw 0 1 0 0 0 0 1 73

Switch on band saw and wet separator 0 1 0 0 0 0 1 WS 27

Sawing process 1 0 0 1 1 0 1 WS 550

Switch off saw and vacuum cleaner 0 1 0 0 0 0 1 WS 26

Remove component and build platform 0 1 0 0 1 0 1 AK 55

Clean saw 0 1 0 0 1 0 1 WS 298

Store away wet separator 0 0 1 0 0 0 1 WS 20

Analysis 48% 45% 7% 48% 82% 0% 100% 1.158

Fig. 1. Example process step 3.1. Degree of VA and NVA operations

Each sub-step is evaluated according to its contribution to the value added of the component on a binary scale with the value zero for no contribution and one for contribution. The degree of VA/NVA operations is defined as the time share of all VA/NVA sub-steps in relation to the total time of the process step. For example, the degree of not necessary NVA operations is calculated by dividing (1×63 s) + (1×20 s) by the total time of 1.158 s, which equals approx. 7 percent. The degrees of VA and NVA operations are calculated analogously.

3.2. Degree of automation

The dependence of the process on the degree of automation is a central lever for increasing the efficiency of a process step [19]. The current degree of automation in one process step is estimated as percentage of automated sub-steps. The more steps are automatized, the higher is the degree of automation in this process step.

3.3. Build job dependency

As with the degree of automation, the build job dependency of one process step is related to the individual sub-steps. The build job dependency describes the influence of a build job on the time consumption of the sub-steps. The time required var- ies, depending on, e.g. geometry, complexity and size of the component. On the one hand, a low build job dependency im- plies a high potential for standardization in this process step.

On the other hand, a high dependency on build jobs indicates potential by component design.

3.4. System dependency

Due to the dependence of the sub-steps and process steps on the system used, the transferability of information to different systems can be analysed. The system used is determined by the production process and the design of the production plant. The general validity of solution alternatives and improvements can be derived in this way.

3.5. Required tools

The analysis of the required tools aims to identify the fre- quency and intensity of the use of certain tools. With advanced methods such as 5S, NVA operations like search and handling effort as well as changeover time can be reduced [40,41]. Space for productive areas can be created by removing unused or rarely used tools.

3.6. Simultaneous to LBM process

Following the approach of the SMED method, the efficiency of the process steps is to be increased by performing certain sub-steps during in-processing, due to the fact that the actual build job usually runs autonomously and does not require manual intervention by the operator [10,12]. Parallel running sub- steps increase the capacity utilization and thus the productivity of the system.

3.7. Time

Closely linked to the identified wastes of the other process analysis criteria, the importance of the respective sub-steps is investigated by the context of the time recordings. The combination of the time criterion with other criteria offers a great potential to identify relevant sub-steps and aligned measures.

4. Validation

The validation of the developed approach is based on the additive manufacturing of a cube-shaped component with maximum external dimensions of 20×20×20 mm³. The component has internal cavities, requiring additional steps to remove the loose powder [42].

Titanium alloy TiAl6V4 is used as material, which is classi- fied as flammable solid according to the safety data sheet [43].

This means that additional operations, such as connecting and disconnecting the antistatic wristband, must be carried out [29].

For the validation of this approach a process chain was se- lected, which is applied in an industrial environment. For the production itself, the LBM system Orlas Creator is used, which has a maximum build volume of Ø 100×100 mm³ and requires largely manual powder handling.

An overall presentation of all process steps and sub-steps is omitted for reasons of clarity but can be requested from the au- thors. An outline of the recorded process chain without evaluation and analysis is shown in Fig. 2. and contains simultaneously performed process steps like Data import LBM sys- tem (via USB-drive) and Setting up LBM system, which are linked by AND-connectors. The splitting path in the lower part of Fig. 2. shows two possible continuations of the process chain, of which only one path can be taken. This decision is symbolized by a XOR-connector known from BPMN 2.0.

Each process step from Fig. 2. is built up according to the structure of Fig. 1. and contains a row for analysis of the recorded data. The example process step Component removal from

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build platform with band sawhas a degree of VA operations of 48 percent and a degree of necessary NVA operations of 45 percent. In the example process step not necessary NVA operations amount to 7 percent of the time of the process step. The degree of automation in this process step is restricted to the sawing process. More than half of the sub-steps are performed manually, which indicates a great potential for increasing efficiency. The high build job dependency indicates that the respective sub-steps have to be designed flexibly and optimized depending on the build job. The band saw works independently of the LBM system in use, meaning that no sub-step is dependent on the system or needs to be performed outside the LBM process time. In several sub-steps the tools wet separator and Allen key are used. Consequently, these tools should be easily accessible for rapid deployment to eliminate waste through search and handling.

An overview of the analysis results in Fig. 3. shows that VA operations are performed 95 percent of the time of all process steps. In contrast, only 10 percent of all sub-steps are VA in terms of the number of sub-steps. These results correlate with the fact that the few, but very time-consuming sub-steps like heat treatmentor LBM processare VA. A large number of sup- porting operations such as pre- and post-processing of the build jobs are necessary but NVA. In total, 17 percent of all operations have been recognized as waste and should be eliminated.

Fig. 3. Analysis of the total process chain regarding VA operations in dependence of time consumption (A) and number of sub-steps (B)

Similar to the VA operations, the degree of automation is very high due to the automated time-consuming sub-steps heat treatment, LBM processand sawing process. All other of the 71 sub-steps must be performed manually. This is consistent with statements in literature [26]. As shown in Fig. 4., a similar situation arises for the build job dependency. The mid-range system dependency can be explained by the fact that many operations and systems such as the oven can be used flexibly and are therefore not dependent on the LBM system. To increase the productivity and efficiency of the process chain, it should

simultaneously with the LBM process. This potential is only utilized in 73 percent of the sub-steps. It should therefore be examined to what extent it is possible to run simultaneously in further process steps.

Fig. 4. Analysis of the total process chain

5. Conclusion and Outlook

For the identification of NVA operations in the LBM process chain it is necessary to consider the LBM system not as an individual element but as part of the production system. Ac- cordingly, the upstream and downstream operations must also be analysed. [12]

For this purpose, a new, systematic approach based on the established methods of value stream mapping and BPMN 2.0 was developed, which allows to quantify the share of VA operations in the LBM process chain. This is a necessary basis for process improvements. In addition, further key figures are calculated on the basis of the specific circumstances. These are:

degree of VA operations, degree of automation, build job dependency, system dependency, required tools, simultaneous to LBM process and time. With the help of these key figures, tar- geted optimizations can be carried out in the next step.

The approach has been validated by additive manufacturing, pre- and post-processing of cube-shaped components made of TiAl6V4 under laboratory conditions. According to this only 10 percent of all sub-steps are VA, whereby VA operations are performed 95 percent of the time of all process steps. The reason for this high value is especially due to the use of the approach under laboratory conditions. After the successful use under laboratory conditions, the developed approach should be applied and validated in an industrial context. The prerequisites are given, because on the one hand the developed approach is 95%

4% 0,4%

A

10%

73%

17%

B

Degree of VA operations

Degree of necessary NVA operations

Degree of not necessary NVA operations

95% 97%

50% 50%

4%

39% 37%

73%

0%

50%

100%

Degree of

automation Build job

dependency System

dependency Simultaneous to LBM-process Total regarding time Total regarding number of sub-steps

Flow rate measurement

+

Component removal

from LBM system Setting-up LBM system

Data import LBM system

+

LBM process Powder removal in blast

cabinet

Stress relief heat treatment in oven Component removal

from build platform with band saw Component removal

from build platform by Manual removal of hand

support structure Microblasting in blast

cabinet

Preparing post- processing

x x

Fig. 2. Outline of the recorded process chain

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map different procedures for the removal of the support structure. On the other hand, it is also conceivable that the procedure developed specifically for the LBM process chain could be transferred to other polymer- or metal-based additive manufacturing processes such as binder jetting or material extrusion.

However, the analysis is only the basis for reducing the amount of NVA operations. The next step is to adapt existing optimization methods from the field of lean production, such as One Piece Flow, SMED, etc. and use them to optimize the additive process chain. In the long term, it is also important to not only apply individual optimization methods, but also to develop generic design guidelines based on value stream design with a focus on additive manufacturing.

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