Keywords

1 Terminal Strip Assembly: The Need for Improving Picking Automation

Today’s manufacturing processes are characterized by an increasing customer-specific individualization of products and require new process strategies for batch size 1 to remain profitable with increasing process complexity. This market trend is expressed in mass customization and represents a cross-industry challenge for manufacturing companies [1]. However, the desired mass customization of products prohibits the use of inflexible standard automation approaches and requires flexible, economically sensible and technically feasible automation systems. Permanently installed production lines, consisting of fixed conveyor technology, automatic machines, robots and safety equipment, do not offer mass customization the necessary production flexibility for a low-effort redesign of assembly lines and meeting increased customer demands [2].

Like many other industries, switch cabinet manufacturing underlies the described phenomenon of mass customization, as a wide variety of products can be assembled in different ways to meet customer-specific requirements [3]. Switch cabinets are elementary machine and plant components, as they are used to distribute, regulate and control power using switchgear and other components (see, for example, [6, 7]). Phoenix Contact GmbH & Co. KG and the Chair of Production Systems (LPS) have therefore been working together closely since 2016 to develop solutions for smart assembly in switch cabinet production, including the use of crucial Industry 4.0 technologies. As a part of the research cooperation, an assembly system was set up in the LPS learning and research factory (LFF). Actual assembly orders are carried out according to the customer’s needs, and a dynamic technology transfer of new automation concepts from science to industry occurs.

The required small electrical parts have a size of a few centimeters, a weight of 5 to 50 g and are manually picked from a decentral storage rack. Improving the manual picking efficiency is one of the most critical issues in current logistics industry [2]. New warehouse automation concepts must be tailored to their particular needs to reduce manual picking time [4]. Therefore, the terminal strip assembly will be presented as an exemplary field of application for UAVs in automated picking. Thus, the need for automated picking will be examined based on the proportion of manual activities for providing material like terminals and jumpers in a first step. Subsequently, a concept for UAVs in small parts assembly will be presented and discussed. It should be noted that one aim of the described method is general applicability in different production domains. In this context, the terminal strip assembly serves as an exemplary application.

1.1 Material Supply of Terminals and Jumpers

The assembly of terminal strips is carried out in an assembly line. Specific tasks must be carried out at each workstation to finalize the assembly process of terminal strips (see Fig. 1). Relevant in the scope of this paper are picking processes for workstation 1, the terminal assembly, and workstation 3, the assembly of jumpers. Picking parts and preparing workstation 1 is done according to production order in advance. After the number of required parts has been prepared, the terminal strips are assembled directly. Parts at workstation 3, on the other hand, are not picked in advance but are procured from the storage rack as soon as a container of parts is empty.

Fig. 1
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Overview of the assembly line [8]

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In [5], the optimization of workstation 1 was discussed, and the implementation of a workstation concept was presented for the addressed assembly line. Having the material ready and in place for production supports the assembly workflow, as the employee works without interruption and loss of concentration. This, in turn, affects the workstation’s design, as it must offer enough capacity to store material and be minimized to save production area at the same time. Nevertheless, there is still demand for parts with a comparatively low amount that are not stored at workstation 1 but in a separate storage rack. The current production process is first examined to determine the share of picking time for the terminal strip assembly and thus the loss of value-adding assembly time. The first step is to analyze the share of picking time in relation to the total production time, divided into the respective work steps of workstation 1 and workstation 3. Therefore, actual production data from real orders will be investigated. The methodology of data acquisition is examined in Sudhoff et al. 2020 [6, 7].

Based on the production data, an ABC analysis is then carried out. Therefore, components with a considerable proportion of the turnover are assigned to class A, whereas less needed components are assigned to class B or C. As the scope of this paper is to determine the distribution of parts in manual assembly and derive commissioning times for each component, the ABC analysis is conducted in terms of frequency of use. For class A, a range of 0 to 80% is applied. Class B reaches from 80 to 95% and C from 95 to 100%.

Consequently, parts that are frequently used to assemble terminal strips (class A) are usually stored within the worker’s reach. Parts that are not commonly used (class B and C), on the other hand, are typically stored further away because of space requirements. In the underlying example, class B parts are stored in a distance of 8 m, and class C parts are located on a shelf 10 m from the assembly station. It can be assumed that this example rather understates the distance for commissioning B and C parts in most cases, as demonstrated in [9].

1.2 Time-Consuming Picking Process for Assembly of Terminal Strips

The conducted assembly process analysis is based on evaluated data taken from January until November of 2021. In this period, 8,944 actual production orders have been carried out. The complete assembly process of terminal strips can be divided into the operating activities of production, preparation, administration, rework, and logistics. The activities production and preparation account for most of the time spent on the assembly processes and are therefore of interest for further investigation. Production describes only work steps, like clamping on electric parts, wiring, labelling, or executing quality testing. Preparation refers to order-picking required parts, like terminals, jumpers. The necessary parts are picked from a storage rack, transferred to the respective workstation, and placed for production. Many of the components, like jumpers, sometimes need a pre-assembling step. Pre-assembling steps and printing of labelling markers are carried out within the process of preparation as well.

Fig. 2(a) shows the relative proportion of the work steps from the overall assembly process. In total, a period of 997.2 h was analyzed, which is equal to 124.7 working days of eight hours.

Fig. 2
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In (a) time allocation in percent for the work steps, evaluated data taken from January until November of 2021 and in (b) time spent in percent for picking of specific parts or preparing an assembly step

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With 78.4%, the most time-consuming assembly share is the production. However, with 17.7%, preparation takes the second-biggest share. Concerning the period of 997.2 h, the time spent for preparation is equal to 176.5 h or 22.1 working days. Logistics and rework usually hold a minor share, while administration takes a little more time.

A detailed analysis of the preparation time for single tasks is presented in Fig. 3(b). With 43.1%, preprinting of labelling markers takes the most significant share, while 39.1% are spent on picking terminals. 14.3% of the time spent is used for preparing shipping material like cardboard, 1.6% for picking jumpers, and 1.4% for preparing end terminals in an automated robotic clamping application. It may be noted that the observed time spent for the picking of jumpers is unusually low. A reason for that was a lower need for a greater variety of jumpers in the carried out orders, resulting in less spent time for picking those parts.

Fig. 3
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Amount of assembled terminals in 2021

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The resulting values are used to calculate the timeshare for picking terminals and jumpers in the given period. The evaluation showed that 71.84 h were spent for the picking of terminals and jumpers, which is equal to 7.2% of the entire assembly time. If it is possible to reduce this share of lost effort with a new supply concept, the time saved may be shifted to a value-adding activity.

1.3 Demand of Terminals

A huge variety of terminal variants were used to assemble terminal strips, but their actual demand varies strongly, depending on the order situation. Figure 4 shows the demand for terminals in the given period. In total, 56 different terminal variants were used, but only some can be listed here. There were 237,245 terminals of the variant PT 1,5/S-TWIN/1P assembled, but only 600 of the variant USLKG 5, for example.

Fig. 4
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Cumulated amount of terminals by their variant, divided into ABC-categories

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The cumulated amount of different terminal variants is presented within an ABC analysis in Fig. 5. Based on a standardized ABC analysis, class A ranges from 0 to 80%, class B from above 80% to 95%, and class C from above 95% to 100%.

Fig. 5
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Amount of assembled jumpers in 2021

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There are only 7 terminal variants out of 56 that make nearly 80% of the used parts. Conversely, 49 variants must be kept in stock in a separate storage and accessed when needed. The same analysis procedure was also used for the supply of jumpers in the same period. The demand for a specific variant is shown in Fig. 6, and the correspondent ABC analysis is presented in Fig. 7.

Fig. 6
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Cumulated amount of Jumpers by their variant, divided into ABC-categories

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Fig. 7
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Supply of terminal strip parts in an automated picking process. (1) Industrial Robot ABB IRB 120 in a loading station. (2) Assembly stations for terminals and jumpers. (3) UAVs. (4) Dummy for Localization system. (5) Storage rack

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In total, 24,217 jumpers of 25 different variants were assembled. Again, a considerable gap in distribution demand for a particular jumper variant can be observed, which means a comparable stocking situation and, therefore, a similar storage situation as for the terminals. In contrast to the ABC analysis of the terminals, with 9 out of 25 variants, however, a comparatively more significant number of jumpers take a share of class A parts. As the jumper’s size is smaller than the size of terminals, the space requirements remain similar for storing A-parts. But there are still 16 variants of jumpers that are assigned to class B and C.

2 Innovative Picking Automation with UAVs

New picking concepts and automated production units are needed to reduce the effort of providing terminals and jumpers sustainably. In this course, possibilities of material provision with UAVs are discussed, which are now being subjected to a feasibility study in the chair’s learning factory. Integrating an automated UAV-based supply into the assembly line might open possibilities for assembly concepts like just-in-time delivery. These concepts might break up the assembly line to a more flexible structure and even lead to reduced production effort. For this purpose, new supply concepts have to be shaped, and new technologies must be used.

2.1 Automation Concepts for the Material Supply of Terminal Strip Assembly

For the assembly line considered, the use of Automated Guided Vehicles (AGVs) is unsuitable for the provision of small parts since the AGVs would have to constantly avoid employees in a highly flexible working environment or block the narrow paths of the assembly line for the passage of employees [10]. Regarding the flexible nature of terminal strip assembly, fixed conveyor technology is not considered due to the high effort of reconfiguration in every order change [11].

Earlier studies proved UAVs to be an underestimated option for the indoor material supply [10, 12]. The usage of UAVs as transportation units have neither been tested nor documented for an actual scenario in material supply [2, 13]. In this way, manual picking effort could be decreased to a minimum. This approach is innovative for small-part assembly and meets the need for increasing product variability with increasing process flexibility.

2.2 UAVs for Material Delivery Tasks

The term Unmanned Aerial Vehicle (UAV) describes small self-flying vehicles without any pilot controlling the aircraft. In the field of computer science and artificial intelligence, mostly the terms UAV, UAS (Unmanned Aerial System), VTOL UAV (Vertical Take-Off and Landing UAV) or Multirotor UAVs are used [14]. In most cases, four rotors lift the device, enabling the UAV to become a VTOL unit. Besides the Quadcopter UAV, there are also Helicopter UAVs and Fixed-wing UAVs. All of them come with their own strengths and weaknesses, as stated in [15]. For production environments with limited space and high demands regarding safety and reliability, quadcopters are the preferred choice as they are most likely to meet the requirements.

In the course of intralogistics, UAVs are not much discussed yet, though their potential might be huge for industrial applications [10]. UAVs promise to be faster, more flexible, space-saving and more cost-effective than, for example, the material supply with mobile robots [12, 16]. The automated supply of workstations transforms the conveyor line with fixed routes into a highly flexible, multidimensional material supply. A conceptual configured assembly line for automated picking by UAVs is visualized in Fig. 8.

The image shows workstations 1 and 3 being supplemented by a loading station (1), in which UAVs are equipped with the respective order material. The loading station holds the parts of classes B and C that were analyzed in Sect. 1.3, and an industrial robot picks the parts from a storage rack to hand them over to the UAV. As material provisioning is carried out automatically by the UAVs, the loading of UAVs should also be automated. In a first attempt, an industrial robot could fulfill the task by picking material from a storage rack and placing it in a delivery container attached to the UAVs. Although attaching a delivery container or weight to a UAV has been reported in literature [17], UAV-loading concepts must be evaluated in real scenarios. Simulations or preliminary considerations cannot provide a reliable result.

The overall system is integrated into the assembly system’s existing architecture, involving CLIP PROJECT for task planning and management. The employee could be provided with an interface for monitoring and controlling the system. With the help of a localization system, for example, an ultra-wideband system (UWB system), the position of the flying robots could be localized and transmitted to the control system. Localization is a crucial aspect regarding indoor UAVs due to their inability to use GPS. Although indoor localization has been a topic for a long time, it is still an active field of research, as can be seen in [18]. For indoor localization, UWB systems have already proved to be a reliable localization technology for aerial robotics in numerous studies. Besides UWB, motion capture technology is also a reported option in many UAV applications [19].

3 Conclusion and next steps

As the picking for terminal strip assembly time analysis showed, small and lightweight products with a great demand for manual picking processes are predestined to be supplied by UAVs. Production data of 8,944 terminal strips were evaluated, and a share of picking time for terminals and jumpers was close to 7.2%. Within an ABC analysis, it could be shown that many different parts of class B and C cannot be stored directly at the assembly station. These parts must be stored in a separate material rack with a walkway of about 10 m. Manually picking of parts by an employee results in a direct loss in value, as the spent time picking might be used for a value-adding activity in production.

Due to their low weight and size, an UAV might carry terminals and jumpers, and the traditional supply of these parts can therefore be automated. Thus, the first approach for a system structure was presented, and a workflow for automated picking by UAVs was introduced. Afterwards, existing challenges and barriers for implementation were discussed, and research questions were derived. To evaluate the presented approach, the system structure will be implemented in the near future. Because the production takes place for actual customer’s orders, an isolated test field with the discussed configuration will be set up. After the first successful flights, the fulfilment of safety guidelines will be addressed.