Optimization of the Operation of a Flexible Automated Assembly Line Using the Seven Basic Quality Tools ^†

Abstract

This paper proposes an algorithm for optimizing the operation of flexible automated assembly lines using the seven basic quality tools. Using the proposed algorithm, analysis of a flexible automated assembly line FMS-200 was performed, using several of the seven basic quality tools sequentially. Based on the analysis results, corrective measures for optimization of line operation have been proposed.

1. Introduction

Flexible automated assembly lines (FAALs) are part of modern automated production systems. Their role in the context of Industry 4.0 is continuously increasing. In this regard, ensuring uninterrupted and fault-free operation of these lines is of particular importance.

The operation of modern FAALs is associated with the collection and processing of real-time data. These data enable the use of various quality control tools to achieve precise monitoring, analysis, and decision-making, leading to the optimization of FAAL performance and, consequently, to improved quality of assembled products.

The seven basic quality tools were developed and systematized in Japan during the mid-20th century by the quality guru Kaoru Ishikawa [1].

They are as follows [2,3,4,5,6]:

• Cause-and-effect diagram (identifies the root causes of problems);
• Check sheet (collects and analyze data in a structured form);
• Control chart (monitors process deviations and stability over time);
• Histogram (evaluates the distribution pattern of data);
• Pareto chart (identifies the most significant factors affecting a problem);
• Scatter diagram (analyzes the correlation between two variables);
• Flowchart (visualizes sequential steps in a process).

Despite being developed decades ago, these tools remain highly relevant today. The primary reasons for their enduring relevance are their simplicity, versatility, and effectiveness in solving diverse quality-related problems. Their efficacy increases significantly when applied in combination with modern computer technologies [7,8].

Automated calculations, interactive visualization, and rapid big data analysis transform them into powerful tools for diagnostics and optimization. The seven basic quality tools can be used individually to solve various quality issues, but they are more frequently applied in combination. The objective of this study is to analyze failures in the operation of the FMS-200 FAAL using the seven basic quality tools and, based on the obtained results, to propose optimization measures for its performance improvement.

2. Problem Statement

The FMS-200 FAAL is built on a modular principle consisting of eight different workstations (Figure 1), each equipped with an independent control system [9], dedicated drive mechanisms [10,11], and integrated transport systems [12,13]. Using SIMATIC WinCC PROFESSIONAL software for programming and controller reconfiguration at each position, the real automated process can be simulated, controlled, and managed from a virtual environment. The individual workstations include the following:

• FMS-201 base part loading station (Figure 1, position 1);
• FMS-202 bearing placement and assembly station (Figure 1, position 2);
• FMS-203 hydraulic press for bearing insertion (Figure 1, position 3);
• FMS-204 shaft selection and mounting station (Figure 1, position 4);
• FMS-205 end cap selection and installation station (Figure 1, position 5);
• FMS-206 bolt insertion station (Figure 1, position 6);
• FMS-207 robotic finishing and final assembly station (Figure 1, position 7);
• FMS-208 automated storage warehouse (Figure 1, position 8);
• Transport system.

A 3D model of the assembled unit is shown in Figure 2. It consists of eight components:

• 1 × prismatic base part;
• 1 × bearing;
• 1 × shaft;
• 1 × end cap;
• 4 × bolts.

The parts vary in size and color, which allows for the assembly of different configurations of the assembled units.

The study of the FMS-200 FAAL was conducted over a 5-day work period for 8 operating hours per day (total 40 h). During the study, standard production parts were used.

3. Flexible Automated Assembly Line Optimization Algorithm

Figure 3 shows the developed algorithm for optimizing the existing FAAL, as well as the quality control tools that will be used during its implementation.

The FAAL analysis and optimization process is structured in four sequential steps, each of which uses at least one of the seven quality basic tools.

3.1. Technological Process Analysis

The first step includes a detailed process description and visualization using a flow chart. This allows for clear differentiation of the auxiliary operations embedded in the technological process, the functional relationships between automation devices and technological units, and the possible areas where failures may occur.

3.2. Failure Analysis

Following the technological process analysis, data collection for occurring failures was performed using a check sheet. This tool facilitates systematic organization and quantitative analysis of failure types, their frequency of occurrence, and timing.

3.3. Failure Cause Analysis

This step of the algorithm involved formulating potential root causes for the observed failures. The identified causes were summarized and structured into a cause-and-effect diagram, which visually maps the key factors contributing to system failures.

3.4. Significant Failure Factor Analysis

The last step of the algorithm used Pareto analysis to identify the most significant failure root causes. The approach enables focused attention on the critical few factors (typically 20%) that have the most significant impact on the quality of the technological process.

4. FMS-200 Flexible Automated Assembly Line Optimization

The developed algorithm was used to optimize the operation of the FMS-200 FAAL.

4.1. Technological Process Analysis

Figure 4 shows a flowchart of the assembly process performed on the FMS-200 FAAL.

The FMS-200 FAAL consists of sequentially connected workstations, each performing specific operations. The base part is automatically transported between the different workstations, with components being added at each stage according to the assembly configuration.

FMS-201 serves as the base part loading workstation, where the orientation of the base part (Figure 2, position 1) is verified. When properly oriented, the part is placed on the satellite. Any base part with incorrect orientation is automatically ejected from the FAAL.

In the FMS-202 workstation, the bearing (Figure 2, position 2) is installed into the base part. This station features bearing height selection capability and has a linear potentiometer for precise height measurement. Bearings failing to meet height specifications are automatically ejected from the FAAL.

The FMS-203 workstation has a hydraulic press for simulating the press–fit assembly of bearings to base parts.

In the FMS-204 workstation, the shaft orientation is verified (Figure 2, position 4) before it is assembled to the bearing. The station allows for the use of shafts of different colors. Any improperly oriented shafts are automatically ejected from the FAAL.

The FMS-205 workstation first checks end cap orientation (Figure 2, position 5) before mounting it onto the bearing. Similar to the previous workstation, it also supports different color variants and ejects any incorrectly oriented caps from the FAAL.

FMS-206 verifies bolt orientation (Figure 2, position 6) before inserting four bolts into the base part. Since this workstation has single-position bolt insertion, the system includes an additional device for repositioning the satellite holding the base part. Incorrectly oriented bolts are removed from the FAAL.

In the FMS-207 workstation, an industrial robot with a combined end-effector replaces the cap and/or shaft when a change in the configuration of the assembled unit is required and tightens the four bolts.

FMS-208 serves as an automated storage warehouse workstation, where assembled units are arranged and stored.

The transport system combines the transport systems of the individual workstations and additional components for connecting them into a common system. It ensures the movement of the satellite with the assembled unit between the individual stations.

4.2. Failure Analysis

The check sheet containing the actual data on failures observed during the assembly process is shown in

Table 1. FAAL failure check sheet.

Failure	FMS- 201	FMS- 202	FMS- 203	FMS- 204	FMS- 205	FMS- 206	FMS- 207	FMS- 208	Transport System	Total
Part	Misalignment	Signal Loss	Misalignment	Wrong Part Orientation	Wrong Part Orientation	Misalignment	No Free Slot	False Recognition	Halt on Direction Switch
WW5	1		2						2	5
WG5				1					4	5
WB5						1			3	4
WW10		1			2		I		3	7
WG10		4		2					2	7
WB10	2	1							3	6
GW5						2			1	3
GG5			2		1				I	4
GB5			1		4			1	5	11
GW10		4	1						2	7
GG10		2	2						5	9
GB10		4	2	2					1	8
BW5			1		2	1		2	4	10
BG5	1		2	3	1					7
BB5			1							1
BW10		2	1	1					2	6
BG10		4	1						5	10
BB10		1	1							2
Total	4	22	17	8	10	4	1	3	43	112

. It is structured to record the type of failures, their frequency, and the corresponding workstation where they were registered. The FAAL was monitored for failure occurrences over a period of 40 h, with continuous operation of the line for 8 h across five consecutive days. This provides a quantitative overview of the nature and distribution of failures.

The different part variations are coded by material, size, and color. The first character in the part name indicates the shaft color (W—white, G—gray, B—black), the second character represents the end cap color (W—white, G—gray, B—black), and the numbers at the end specify the bearing height in [mm].

Using the check sheet, the primary causes of failures during normal operation were recorded and categorized. However, the check sheet does not reveal the root causes for their occurrence. To clarify them, an additional qualitative analysis is required, which is the subject of the next step of the FAAL optimization.

4.3. Failure Cause Analysis

The input data for the cause-and-effect diagram were collected through brainstorming sessions and analysis of failures identified in the previous step, observed during normal operation of the FAAL. The constructed diagram (Figure 5) presents potential root causes of failures summarized in six main factor categories: “Measurement”, “Methods”, “Machines”, “Environment”, “Materials”, and “Operators”. They cover a wide range of technical and organizational prerequisites for failures.

Based on analysis and expert evaluation, the “Machines” and “Operators” categories (labeled with priorities 1 and 2 in Figure 5) were identified as having the most significant impact on failure occurrence. This requires further in-depth analysis in these areas.

4.4. Significant Failure Factor Analysis

The Pareto analysis of the “Machines” group follows the 80/20 principle, where a small number of causes lead to the majority of failures. By ranking failure causes by frequency of occurrence and visualizing them in a Pareto chart, the most impactful factors affecting the FAAL’s operation were identified. This enabled targeted optimization efforts in these critical areas for maximum effectiveness. The Pareto analysis was conducted from two perspectives: overall FAAL failure analysis and workstation failure analysis.

4.4.1. Overall FAAL Failure Analysis

This analysis categorizes failures by type, regardless of the workstation in which they occur. It identifies the significant failure patterns across the entire FAAL. The results are presented in the Pareto chart (Figure 6).

The analysis reveals that the most critical failure factors affecting the FMS-200 FAAL are unintended stoppages during satellite direction changes and the misalignment in part positioning. These two issues collectively account for over 80% of total line failures.

4.4.2. Workstation Failure Analysis

Unlike the overall view, this analysis examines failures at each workstation individually, recognizing that identical failure symptoms may have different root causes across workstations. The findings are visualized in a separate Pareto chart (Figure 7).

The data highlight three primary trouble zones: the transport system itself (responsible for 38% of station-specific failures), followed by the FMS-202 bearing assembly station (20%) and the FMS-203 hydraulic press station (15%).

5. Optimization Results

Based on the conducted analysis, specific optimization proposals were formulated to eliminate and minimize the identified root causes of failures in the existing FAAL.

Several of the seven classical quality control tools were applied sequentially to optimize the system. A flowchart was used to define the FAAL’s structure and individual workstations, while a check sheet provided objective data on recorded defects. To gain deeper insight into failure causes, a brainstorming session with technical specialists was conducted, serving as the foundation for constructing a cause-and-effect diagram. This diagram identified key factors leading to production issues, with a primary focus on the “Machines” and “Operators” categories. Finally, Pareto analysis highlighted the significant causes requiring prioritized intervention.

Based on the above analysis, the following specific measures for FAAL optimization are proposed:

• Replacement of the bearing thickness measurement positioning sensor;
• Modification of guide rail design to improve satellite direction changes;
• Adjustment of the location of the base part positioning sensor;
• Calibration of control system parameters for better synchronization between the workstations;
• Operator training programs.

Application of the seven basic quality tools demonstrates their effectiveness in a modern industrial context. Although these methods were systematized decades ago, through the combination of computer-aided analysis and engineering expertise, they remain extremely useful for the diagnosis and optimization of complex automated systems.

The conducted analysis not only localized the FAAL’s core problems but also yielded specific technical recommendations for improvement. This approach can serve as a model for similar systems for ensuring sustainable quality control.

Future work should focus on implementing selected optimization measures and conducting follow-up analysis to validate the proposed FAAL optimization algorithm.

6. Conclusions

An optimization algorithm for FAALs has been developed using the seven basic quality tools. This algorithm was successfully applied to analyze the performance of the FMS-200 FAAL, enabling precise identification of its core operational issues.

Based on the analysis, specific measures have been proposed to optimize the operation of the FMS-200 FAAL.

The proposed algorithm demonstrates universal applicability—it can be adapted to any automated production system in order to optimize its operation and achieve sustainable quality control of the manufactured products.