Performance Improvement for Additive Manufacturing of Spur Gear via the Define-measure-analyze-improve-control (Dmaic) Method

doi:10.21203/rs.3.rs-4839713/v1

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Performance Improvement for Additive Manufacturing of Spur Gear via the Define-measure-analyze-improve-control (Dmaic) Method

https://doi.org/10.21203/rs.3.rs-4839713/v1

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Industrial firms increasingly use advanced technologies to navigate market fluctuations, bolster demand, and enhance competitiveness. Six Sigma methodology presents a pathway for these firms to overcome challenges and pioneer new business strategies. This study utilizes the Define-Measure-Analyze-Improve-Control (DMAIC) method to minimize process variability in additive manufacturing (AM) for spur gears. The process commenced by defining customer requirements and collecting data on the gear 3D printing process. Subsequent steps involved analyzing data to identify root causes of defects, implementing solutions for improvement, and establishing process controls for sustainability. The standard deviation reduced significantly by 90.55%, with the process capability index (Cp) improving by 87.15% and the process performance capability index (Cpk) by 92.98%. The application of DMAIC proves invaluable in defect reduction and ongoing performance enhancement within the 3D printing gear sector.

3D Printing

DMAIC

Additive Manufacturing

Spur Gears

Process Improvement

Industrial enterprises have a rich history of utilising gears, dating back to the ancient Greek mechanics in Alexandria during the third century BC. Archimedes, a renowned figure of that era, developed gears that saw widespread use in the Roman world [1]. While the manufacture of toothed wheels was active in the first century BC, recent discoveries, such as those made by the Science Museum in London, have prompted a re-evaluation of the history of mechanisms employing toothed wheels for precise movement transmission [2]. Early examples of gears appeared in China as early as the fourth century BC, further highlighting the global significance of gear technology.

Plastic gears have emerged as vital components in various industries, including food, electrical, and textile, owing to their lightness, corrosion resistance, affordability, and quiet operation [3]. Notably, advancements in 3D printing technology have enabled the easy manufacturing of components like cutters in the textile and food industries, housings and gears in electric motors, and even pulp dentin and implants. Plastic gears initially found application in lighter industries, such as printers, watches, and toys, due to their relatively lower strength and thermal resistance compared to metal gears [4, 5].

With the development of stronger and more consistent polymers, plastic gears are increasingly used to transmit high power. Materials like acetal copolymer, nylon 66, polycarbonate, and polyester are commonly employed in plastic gear manufacturing [6]. The rapid evolution of 3D printing technology, pioneered by Chuck Hull in 1983 with the development of the first 3D printer, has revolutionised AM. From its early applications in aerospace, healthcare, and automation in the late 1990s and early 2000s, 3D printing has become a cornerstone of modern manufacturing [7].

Today, 3D printing technology offers unparalleled advantages, including customisation, rapid prototyping, cost-effective manufacturing, and the ability to create complex geometries precisely. Emerging technologies like bioprinting and 4D printing are opening new frontiers, particularly in medicine, offering unprecedented possibilities for tissue engineering and dynamic material fabrication [8]. As a result, the demand for 3D printing technology continues to soar, driven by its capability to deliver intricate structures with high accuracy and efficiency.

Despite the significant advancements and potential of 3D printing, several challenges persist, particularly in the consistency and reliability of plastic gears. Variability in the manufacturing process can lead to defects and performance issues, hindering the broader adoption of plastic gears in high-stress applications. Furthermore, integrating new materials and manufacturing techniques necessitates rigorous quality control and process optimisation to ensure product reliability and durability.

In this context, applying the Define, Measure, Analyse, Improve, Control (DMAIC) methodology in minimising process variability within AM for spur gears represents a significant step toward harnessing the full potential of advanced manufacturing techniques. By systematically addressing process inefficiencies and variability, the DMAIC approach can enhance the consistency and performance of 3D-printed plastic gears. This study aims to integrate historical insights with contemporary technological advancements to contribute to the ongoing evolution of AM, fostering innovation and excellence in industrial practices.

By addressing the challenges and harnessing the capabilities of 3D printing, this research endeavours to set a new standard for producing plastic gears, ensuring they meet the stringent demands of modern industrial applications. The findings from this study are expected to provide valuable insights into the optimisation of AM processes, driving further advancements in the field and expanding the potential applications of plastic gears in various industries.

Additive Manufacturing (AM) for Gear Production

AM is a transformative technology in gear fabrication, offering unparalleled advantages over traditional manufacturing methods. This innovative approach enables the production of gears with intricate shapes and designs while optimising resource utilisation and expediting product development [9, 10, 11]. The versatility of AM machines, characterised by high resolutions reaching tens of microns along various orthogonal axes, allows for the precise fabrication of gears with complex geometries [9]. This capability is particularly advantageous when traditional gear manufacturing processes, such as helical, spiral, hypoid, or cycloid geometries, are cost-prohibitive or technically challenging [11].

One of the key benefits of AM in gear production is its ability to cater to small-batch or custom manufacturing needs, aligning well with market requirements for short lead times and tailored solutions. This flexibility reduces setup costs associated with traditional manufacturing methods and enhances responsiveness to dynamic market demands [10]. Various AM technologies find application in the gear industry, each offering distinct advantages. For instance, Powder Bed Fusion (PBF) enables the layer-by-layer deposition of powdered material, resulting in high-resolution parts with excellent mechanical properties [12]. Similarly, Fused Filament Fabrication (FFF) and Fused Deposition Modelling (FDM) offer cost-effective solutions for producing functional prototypes and end-use parts with acceptable mechanical properties [12]. Directed Energy Deposition (DED) and Binder Jet Processing (BJP) are other AM techniques that find niche applications in gear manufacturing, offering unique advantages in terms of material flexibility and build speed [12].

The choice of raw materials in AM is extensive, encompassing polymers, ceramics, composites, metals, alloys, functionally graded materials, smart materials, and hybrids [13]. This diversity allows manufacturers to select materials tailored to specific application requirements, balancing mechanical properties, thermal stability, and cost-effectiveness. Polymers like acetal copolymer, nylon 66, and polycarbonate are commonly used in producing plastic gears due to their favourable properties and ease of processing. Metals and alloys, on the other hand, are preferred for high-strength applications where durability and load-bearing capacity are critical.

The evolution of AM technology has opened up new avenues for gear design and production innovation, promising enhanced performance, reliability, and efficiency in various industrial sectors. The ability to rapidly prototype and iterate designs enables engineers to explore and validate complex gear geometries and material combinations that were previously unattainable. This rapid iteration capability accelerates the development cycle, allowing quicker transitions from concept to production.

Furthermore, AM supports the production of lightweight gears with optimised performance characteristics, which is particularly beneficial in industries such as automotive and aerospace, where weight reduction is crucial. The use of topology optimisation and lattice structures in AM allows for material savings without compromising the mechanical integrity of the gears.

As AM advances, emerging technologies like bioprinting and 4D printing expand the possibilities further. Bioprinting, for example, holds the potential for creating bio-inspired gear designs that mimic natural structures for enhanced performance. 4D printing introduces the concept of dynamic, self-assembling gears that can change shape or function in response to environmental stimuli, offering unprecedented adaptability in gear applications [8].

Lean Six Sigma in Additive Manufacturing

The convergence of Lean Six Sigma principles with AM presents exciting opportunities for process optimisation and quality improvement in manufacturing gears and other components. While AM offers unique design flexibility and rapid prototyping advantages, integrating Lean Six Sigma methodologies can further enhance process efficiency and product quality. This integrated approach leverages the strengths of both methods, creating a robust framework for continuous improvement and operational excellence.

Studies have explored applying Lean Six Sigma principles in AM, focusing on optimising processes, reducing defects, and improving overall process capability [14, 15]. By applying Lean Six Sigma tools such as process mapping, root cause analysis, and statistical process control, organisations can identify areas for improvement and implement targeted interventions to enhance AM process performance [16]. For example, process mapping can help visualise the entire AM workflow, pinpointing bottlenecks and inefficiencies, while root cause analysis can delve into the underlying issues causing defects, and statistical process control can monitor and control process variations in real time.

The DMAIC methodology provides a structured approach for continuous improvement in AM processes, enabling organisations to define process objectives, measure performance metrics, analyse process data, implement improvements, and establish controls to sustain process enhancements [17]. This methodology is particularly effective in the AM context, where precise control over process parameters and quality is critical. By systematically applying DMAIC, organisations can achieve significant reductions in lead times, improved product quality, and increased operational efficiency.

Integrating Lean Six Sigma with AM offers several synergistic benefits. Firstly, it enhances the ability to produce high-quality, defect-free products. The statistical rigour of Six Sigma helps in understanding and controlling the variability inherent in AM processes, leading to more consistent and reliable outcomes. Secondly, Lean principles focus on waste reduction and process streamlining, which can significantly reduce the time and resources required to produce parts. This is particularly valuable in AM, where material costs and production times can be high.

Moreover, the integration promotes a culture of continuous improvement and data-driven decision-making. By regularly analysing process data and seeking incremental improvements, organisations can stay competitive and responsive to market demands. This approach also supports innovation, as teams are encouraged to experiment with new materials, designs, and process parameters within a controlled and systematic framework.

In practical terms, integrating Lean Six Sigma with AM involves several steps. Initially, organisations must establish a baseline of current process performance through detailed measurements and data collection. Next, through thorough analysis, they can identify critical areas where improvements will have the most significant impact. Implementing changes based on these insights and then rigorously controlling the new process conditions ensures that improvements are sustained over time.

The combined application of Lean Six Sigma and AM also has broader implications for industry standards and best practices. As more organisations adopt this integrated approach, knowledge will develop, guiding future applications and innovations. This will contribute to establishing more standardised and optimised practices across the industry, benefiting all stakeholders involved.

Polymer Spur Gear Manufacturing and Lean Six Sigma

Polymer spur gears are essential in various industrial applications, from automotive to consumer electronics, due to their lower weight, reduced noise, corrosion resistance, and cost-effectiveness. These benefits make them suitable for applications such as automotive systems, contributing to lighter and more fuel-efficient vehicles, and consumer electronics, where their quiet operation and durability are highly valued. However, manufacturing polymer gears presents unique challenges, particularly in maintaining dimensional accuracy and mechanical strength. The adoption of Six Sigma methodologies in polymer gear manufacturing has garnered significant attention due to its potential to enhance product quality and process efficiency. Several studies have investigated the application of the DMAIC methodology in reducing process variability and improving manufacturing outcomes in the polymer gear industry [18, 19, 20].

The Six Sigma methodology, particularly the DMAIC framework, provides a structured approach to identifying and addressing sources of variation within the manufacturing process. This approach is crucial for achieving the high consistency and performance required for polymer gears in demanding applications. Key steps in the DMAIC process include defining clear objectives for process improvement, such as reducing defects or improving dimensional accuracy, collecting data on current process performance, including critical metrics such as gear dimensions, mechanical properties, and defect rates, identifying root causes of variation and defects through statistical analysis and process mapping, implementing targeted interventions to address identified issues, such as optimising injection moulding parameters or enhancing material formulations, and establishing robust process controls to sustain improvements, including regular monitoring and feedback loops.

Several studies have demonstrated the effectiveness of Six Sigma in the polymer gear manufacturing industry. For instance, [18] applied DMAIC to address critical control points in the polymer gear production process. By systematically identifying sources of defects and implementing targeted improvements, they significantly reduced defect rates and improved overall product quality. [19] focused on reducing scrap rates in polymer gear manufacturing. They minimised waste through rigorous root cause analysis, process optimisation, and enhanced process efficiency. [20] explored using Six Sigma to improve the dimensional accuracy of polymer gears. They achieved more consistent and reliable production outcomes by optimising key process parameters.

The integration of Six Sigma principles in polymer gear manufacturing offers several tangible benefits. By systematically addressing sources of variation, manufacturers can produce gears with more consistent dimensions and mechanical properties, leading to improved performance and reliability in end-use applications. Targeted process improvements can significantly reduce the incidence of defects, leading to lower scrap rates and cost savings. Optimising key parameters enhances overall process capability, allowing manufacturers to meet tighter tolerances and more stringent quality standards. Streamlining manufacturing processes reduces cycle times and increases throughput, enabling manufacturers to respond more quickly to market demands.

In this study, the DMAIC methodology was rigorously applied to enhance the process capability for gear layers, with each phase executed meticulously using qualitative and quantitative techniques, as detailed in Table 1. The DMAIC framework, which stands for Define, Measure, Analyze, Improve, and Control, provides a structured approach to problem-solving and continuous improvement, essential for systematically identifying and addressing process inefficiencies to achieve better performance and organizational success.

The Define phase forms the bedrock of the DMAIC process. During this phase, the project team clearly articulates the problem statement, ensuring it aligns with organizational goals. Objectives are set to delineate what the team aims to achieve, and the project scope is defined to establish the boundaries for the improvement efforts. Identifying stakeholders ensures that all relevant parties are engaged and their perspectives considered throughout the project lifecycle.

In the Measure phase, the focus shifts to data collection and establishing a performance baseline. The project team systematically gathers data from relevant sources, ensuring all necessary metrics are captured to comprehensively understand the problem or process under investigation. Selecting appropriate metrics is crucial as they provide the benchmarks against which progress will be assessed. Establishing a baseline allows the team to evaluate future improvements effectively.

The Analyze phase involves thoroughly examining the data collected during the Measure phase to pinpoint the root causes of the identified issues. Techniques such as root cause analysis and hypothesis testing are employed to uncover underlying factors contributing to the problems. Data validation ensures the accuracy and reliability of the findings, while data visualisation techniques help to reveal patterns and insights within the data.

During the Improve phase, the project team generates, implements, and tests potential solutions to address the root causes identified in the Analyze phase. Based on the insights gained, the team engages in creative problem-solving to develop innovative solutions, piloted in a controlled environment to assess their effectiveness and identify potential challenges or unintended consequences. Successful solutions are then scaled up, with careful monitoring to ensure effective integration and desired results.

The Control phase focuses on sustaining the improvements achieved in the previous phases over the long term. Monitoring systems are established to track key metrics and performance indicators, providing early warnings of any deviations from expected outcomes. Comprehensive documentation of processes, procedures, and lessons learned supports ongoing improvement efforts and facilitates knowledge transfer. Training and communication initiatives are implemented to ensure that employees possess the necessary skills and knowledge to maintain and build upon the improvements.

Table 1

DMAIC phase and steps
DEFINE	MEASURE	ANALYZE	IMPROVE	CONTROL
• Problem Definition • Project objectives • Scope definition	• Data collection • Metric selection • Data analysis tools	• Root cause analysis • Hypothesis testing • Data visualization	• Solution development • Pilot run • Implementation • Check performance	• Monitoring and standardization • Document • Share learning

Case Study

Problem definition

The problem lies in the prolonged production times associated with the 3D printing of gears, despite their critical role in power and motion transmission across various industries. Gears, particularly spur gears, are vital for machinery operation, yet gear collision remains a leading cause of machinery failure. Plastic gears have expanded their applications, offering lightweight and precise alternatives to traditional metal gears. However, 3D printing presents challenges due to the complexities in gear designs and material properties, resulting in longer lead times than conventional methods.

This research project uses 3D printing to manufacture customised spur gears, addressing specific requirements in machinery, automotive, and textile applications. Despite the potential for customisation, current practices often lead to extended production times, hindering efficiency and responsiveness to market demands. Figure 1 illustrates the gears printed with a 3D printer, highlighting the potential for intricate designs and customisation offered by AM techniques.

DMAIC is adopted to optimise printing parameters and leverage advancements in AM technologies. This will unlock the full potential of 3D printing for gear manufacturing, enabling faster production cycles, greater flexibility, and enhanced competitiveness in the market.

Define phase

The research project identifies the challenge of prolonged production times associated with the 3D printing of gears despite their critical role in power transmission across industries. The project focuses mainly on spur gears, essential for machinery operation, and acknowledges that gear collision remains a leading cause of machinery failure. While plastic gears offer lightweight and precise alternatives, the complexities in gear designs and material properties pose challenges in 3D printing, resulting in longer lead times than conventional methods. The project aims to utilise 3D printing for customised spur gears to meet industry-specific requirements in machinery, automotive, and textile applications. However, current practices hinder efficiency and market responsiveness. Through stakeholder engagement, the project defines its scope to optimise printing parameters, addressing challenges related to polymer materials, including issues with printing speed, production efficiency, material filling, adhesion variations, and waste generation, laying the groundwork for targeted improvement efforts.

Measure

The research project meticulously assessed the performance of the existing gear printing process, expanding its evaluation to encompass critical parameters such as standard deviation, lower specification limit (LSL), and upper specification limit (USL). These additions aimed to provide a comprehensive appraisal of process capability. Through meticulous experiments and exhaustive data collection efforts, key variables such as printing speed, layer thickness, and filler paste variation were thoroughly measured and analysed. The standard deviation, calculated at 21.28, provided valuable insights into process variability. The LSL and USL set at 75 and 105, respectively, defined acceptable performance thresholds. Following data collection, a normality test was conducted to validate the distribution of the collected data, ensuring statistical integrity. Subsequently, the Cpk was calculated using these parameters, resulting in a current value of Cpk = 0.16. A Cpk value of 0.16 indicates that the current gear printing process does not meet the desired specifications, as illustrated in Fig. 2. A value below 1 suggests that the process is not effectively meeting tolerance limits, potentially leading to increased defects and customer dissatisfaction. To enhance process capability and achieve higher Cpk values, it is necessary to identify and address sources of variation, optimise printing parameters, and implement quality control measures.

Analyze

A comprehensive examination of the data was conducted to identify the root cause of the high printing time using the Ishikawa diagram, as shown in Fig. 3. This diagram facilitated the identification of the root causes of the issue. It was determined that the primary cause of the extended printing time was the method category, specifically due to inconsistent parameters such as printing speed, layer thickness, and infill settings. The lack of standardized setup instructions exacerbated these issues.

Improve

Drawing on insights from the Analyse phase, the project team promptly implemented corrective measures to address the identified root causes and enhance the gear printing process. Tactics such as fine-tuning printer settings, optimising layer thickness, and adjusting printing and packaging speeds were employed to improve process efficiency and elevate quality standards. The effectiveness of these interventions was thoroughly scrutinised through continuous monitoring and feedback loops, leading to notable reductions in process fluctuations and significant improvements in process capability. This phase included solution generation, pilot testing, implementation, and ongoing monitoring and adjustment, culminating in a new Cpk value of 2.28 and a standard deviation of 2.012. These metrics reflect substantial improvements, affirming the success of the project and improvement initiatives.

The comparison between the previous and current results demonstrates a significant enhancement in both Cpk and standard deviation. Initially, the Cpk value was 0.16, indicating a process capability well below the desired level. However, after implementing corrective measures, the Cpk value increased to 2.28, showcasing a remarkable improvement. Similarly, the standard deviation decreased dramatically from 21.28 to 2.012, signifying a substantial reduction in process variability, as shown in Fig. 4. These improvements validate the success of the project in enhancing process efficiency and quality standards in gear printing.

Control

The emphasis was on implementing measures to sustain process efficiency and quality improvements over time. This included establishing standardised procedures, monitoring key performance indicators, and promptly implementing feedback mechanisms to detect deviations from established standards. The project team aimed to foster a culture of excellence and innovation in gear manufacturing by integrating continuous improvement practices into daily operations. This involved meticulous monitoring systems, comprehensive documentation, and practical training and communication strategies to ensure that the enhanced processes were consistently adhered to and further refined as needed, as shown in Table 2.

Table 2

DMAIC for AM of spur gear
Define Define the problem	Measure Measure the problem	Analyze Find the root cause.	Improve Implement the solution	Control Check and documentation
• Customer complaint about late delivery of Spur Gear. • Low productivity of Spur Gear	• Run the printing process with the current environment. • Cp/Cpk lower, less than 1.	• Tool: Fishbone diagram Potential root causes: • High speed • Increase thickness • High infill	• Fine-tuning printer settings • Optimising layer thickness • Honing printing and packaging speeds Run printing process	• Compare new Cp/Cpk • Documentation and share learning

The investigation into the 3D printing process of spur gears highlighted several inefficiencies and areas for improvement. Despite existing research on 3D printing technologies, there was a noticeable gap in studies specifically addressing the optimisation of process parameters for spur gears within a Six Sigma framework. The key findings of this study underscore the potential for substantial gains in efficiency and quality through targeted process adjustments.

Our analysis revealed that the primary factors affecting printing time and quality were layer thickness, printing speed, and infill settings. By employing the DMAIC methodology, we systematically identified these root causes using an Ishikawa diagram, which facilitated a structured approach to addressing each issue.

The results of the study demonstrate a clear enhancement in the 3D printing process following the implementation of the corrective measures. Optimising layer thickness and printing speed, alongside refining infill settings, led to a marked improvement in process capability, as evidenced by an increase in the Cpk value and a reduction in standard deviation. These improvements indicate a more consistent and reliable process, aligning with Six Sigma principles of reducing variation and improving quality.

The successful application of DMAIC in this context illustrates the effectiveness of the methodology in addressing specific process challenges within 3D printing. By systematically analysing and refining each parameter, the study not only enhanced the efficiency of the printing process but also contributed to the broader understanding of how Lean and Six Sigma principles can be applied to advanced manufacturing technologies.

While the study provides valuable insights, several limitations must be acknowledged. Firstly, the research focused solely on spur gears, which may limit the generalisability of the findings to other types of 3D-printed components. The study was constrained by the specific hardware and material used, which could affect the applicability of the results to different 3D printing systems or materials. Another limitation is the scope of the DMAIC implementation. Although significant improvements were achieved, other potential areas of process optimisation, such as post-processing techniques or material properties, were not explored in this study. Future research could extend these findings by examining additional variables and their impact on the 3D printing process.

The findings from this study offer several directions for future research. Further investigation could explore the application of the DMAIC methodology to other types of 3D printed components, providing a broader understanding of its effectiveness across various applications. The incorporating advanced materials and different printing technologies could offer insights into further optimisation opportunities. Also, need to consider a more comprehensive analysis of post-processing techniques and their impact on the final product quality. This would provide a more holistic view of the 3D printing process and contribute to the development of more refined and adaptable optimisation strategies.

The primary objectives of the study were to enhance the efficiency and quality of the gear printing process, ultimately improving process capability and reducing variability. These objectives were achieved through thoroughly applying the DMAIC methodology and Lean Six Sigma principles. The initial analysis revealed a Cpk below 1, highlighting the need for significant improvements. Targeted corrective measures addressed root causes such as printer settings, layer thickness, and printing speeds. The results were evident, with the Cpk value improving to 2.28 and a substantial reduction in the standard deviation from 21.28 to 2.012, indicating a more consistent and reliable production process.

The managerial implications are significant, illustrating the value of systematic problem-solving approaches in manufacturing. Organisations can foster a culture of excellence and innovation by integrating continuous improvement practices into daily operations. This project demonstrated the importance of proper resource allocation, senior management guidance, and effective leadership in successfully implementing Lean Six Sigma initiatives. The enhanced process capability and product quality achieved in this project reflect the commitment of the organisation to meeting international quality standards and optimising costs.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author Contributions

All authors contributed to the study. Riyadh Makki Hashim, Nur Amalina Muhammad, Ahmed Z. M. Shammari, and Noorhafiza Muhammad carried out material preparation, data collection, and analysis. Riyadh Makki Hashim and Nur Amalina Muhammad wrote the first draft of the manuscript, with comments provided by Noorhafiza Muhammad. All authors reviewed and approved the final manuscript.

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You are reading this latest preprint version

Performance Improvement for Additive Manufacturing of Spur Gear via the Define-measure-analyze-improve-control (Dmaic) Method

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Abstract

Figures

Introduction

LITERATURE REVIEW

Additive Manufacturing (AM) for Gear Production

Lean Six Sigma in Additive Manufacturing

Polymer Spur Gear Manufacturing and Lean Six Sigma

Research Methodology

Case Study

Problem definition

Define phase

Measure

Analyze

Improve

Control

Discussion

Conclusion

Declarations

Competing Interests

Funding

Author Contributions

References

Status:

Version 1