Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters

doi:10.21203/rs.3.rs-4820100/v1

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Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters

https://doi.org/10.21203/rs.3.rs-4820100/v1

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Injection moulding is a widely used method for manufacturing plastic components, with the quality of the final product depending on various process factors managed throughout the procedure. Integrating sustainable manufacturing practices is crucial for mitigating ecological impacts while maintaining product excellence. Manufacturers need to balance product quality, procedural effectiveness, and environmental impact by evaluating how each parameter affects the product's quality and ecological footprint. While many focus on optimising process parameters, fewer consider integrating sustainability competency, which also affects parameter performance. This study aims to advance understanding by conducting experiments and analyses on these factors' influence on product quality. The incorporation of sustainability competency aims to empower individuals and entities to make informed choices that align with environmental, societal, and economic factors for a more sustainable and accountable future. The optimised model, with an error of less than 1%, quantifies the competency value bridging mechanical properties and comprehensive competency by integrating attitudinal factors. Parameter selection through Design of Experiments (DOE) and expert elicitation method contribute to this integration. Evolution from the foundational to the proficient model includes operational team and sustainability competency descriptors, providing context for innovation and knowledge creation highly valued by employers and stakeholders in a productive and streamlined setting. Additionally, this research contributes to the advancement of smart grid and sustainable energy applications by promoting energy-efficient manufacturing processes. By integrating renewable energy sources and smart grid technologies, the injection moulding industry can achieve significant reductions in energy consumption and greenhouse gas emissions. This integration not only enhances the sustainability of manufacturing processes but also supports the broader transition to a more resilient and eco-friendly energy system.

Sustainable Manufacturing

Injection Moulding

Energy Efficiency

Sustainability Integration

Process Parameter Optimisation

In today's manufacturing landscape, sustainability has become crucial, particularly in injection moulding, where sustainability competency integrates environmentally friendly practices, economic viability, and social responsibility. This involves understanding sustainable materials, processes, and design strategies to minimise environmental impact while maintaining efficiency and product quality (Czepiel et al., 2023; Gholami et al., 2021; Nguyen et al., 2024; Selamat et al., 2017). Key areas of sustainability competency in injection moulding include material selection, process optimisation, waste reduction, lifecycle assessment, design for sustainability, regulatory compliance, continuous improvement, and effective communication. This competency not only addresses environmental challenges but also serves as a proactive strategy for long-term success (Cao et al., 2023; Turan, Johan, & Nor, 2016; Vieira et al., 2021). Meanwhile, research trends in sustainability competency encompass interdisciplinary approaches, education and training methods, assessment tools development, behavioural psychology insights, technology integration, corporate social responsibility, circular economy principles, policy influence, social equity considerations, supply chain management, stakeholder engagement, and resilience against environmental changes (D. O. Aikhuele & Turan, 2016; Sahimi et al., 2017; Turan & Johan, 2016). It's important to note that the field is dynamic, and staying updated on recent research through academic sources is advisable.

Several research studies have delved into improving the mechanical properties and production processes of plastic parts through the use of the Taguchi method (Adanan, Mohd Turan, Johan, Md Yusoff, & Yee, 2022; Zhu et al., 2021). For instance, investigations by various researchers focused on optimising parameters like melting temperature, injection pressure, and cooling time to enhance the strength and quality of plastic products (Mehat & Kamaruddin, 2011a). By carefully adjusting these factors, researchers achieved significant improvements in tensile, compressive, and flexural strengths of plastic trays, containers, and other items made from recycled plastics, demonstrating the feasibility of substituting recycled materials for virgin plastics. Integration of the Taguchi method with simulation software like Mould-Flow further enhanced the process optimisation, enabling engineers to identify optimal parameters and improve energy efficiency and product quality (Moayyedian et al., 2021; Wen et al., 2014). Additionally, studies explored the Taguchi method's applicability in minimising defects like weld lines and shrinkage, leading to enhanced quality and dimensional accuracy of plastic parts, which is crucial in industries requiring tight tolerances (Adanan, Mohd Turan, Johan, Md Yusoff, & Xin, 2022).

Furthermore, researchers investigated the Taguchi method's effectiveness in optimising the mechanical properties of new composites, including bio composites and recycled composites, highlighting the potential for sustainable practices by utilising recycled materials and improving composite performance through optimised processing conditions (Haniel et al., 2023). By systematically analysing the impact of various factors on material properties, researchers identified optimal process parameters to achieve desired outcomes, such as improved impact strength and dimensional stability (Chauhan et al., 2021; W.-C. Chen et al., 2016). Additionally, the Taguchi method facilitated the minimisation of part weight and reduction of defects like short shots, further enhancing the efficiency and reliability of injection moulding processes (Panneerselvam & Turan, 2020). Overall, the Taguchi method emerged as a valuable tool for optimising injection moulding processes, enabling improved mechanical properties, reduced defects, and enhanced quality of plastic parts, thereby contributing to sustainable practices and efficient production processes in the plastic industry (Panneerselvam & Turan, 2021).

The excerpt emphasises the importance of sustainability practices and the benefits they bring. It highlights that sustainability practices can enhance brand value, attract customers, employees, and investors, reduce costs, improve business resilience, and create new opportunities for market expansion. The article also discusses the need for comprehensive approaches and frameworks, as well as the use of indicators to measure sustainable development. It mentions the importance of integrating knowledge and consciousness levels in the decision-making process to achieve sustainable development goals effectively. In the context of Malaysia, the article acknowledges the country's involvement in the development of the Sustainable Development Goals (SDGs) and the alignment of the SDGs with the national agenda. However, it also mentions the need for comprehensive approaches, frameworks, and indicators to address the challenges in interpreting sustainable development and setting indicators in Malaysia. Overall, the article emphasises the importance of integrating sustainable business practices, knowledge, consciousness, and comprehensive approaches to achieve sustainable development goals and ensure the success of businesses in Malaysia (Adanan et al., 2021; Turan, Johan, & Abu Sofian, 2018).

Sustainable manufacturing involves employing technologies and practices that adhere to sustainability principles across economic, environmental, and social aspects. It encompasses various outcomes, including prioritizing eco-friendliness to reduce environmental impact, optimising resource use to lower costs and product prices, minimising energy consumption for cost savings and environmental preservation, reducing waste generation for efficient resource utilisation and pollution reduction, prioritising safety in operations to prevent accidents, and enhancing worker health by improving workplace conditions (Sahimi et al., 2018). Meeting the growing demand for consumer products while ensuring sustainability aligns with environmental responsibility and long-term economic competitiveness. Efforts to reduce energy consumption and increase renewable energy usage are crucial, considering that a significant portion of global energy demand and CO₂ emissions stem from manufacturing activities. Achieving sustainability involves comprehensive approaches at both the factory and process levels, including data-driven strategies, technical modifications to conserve energy and resources, and adopting energy-efficient technologies as alternatives to traditional methods (D.-C. Chen et al., 2024; Farbodi, 2017; Mehat & Kamaruddin, 2011b; Turan, Johan, & Omar, 2018).

This research study concentrates on exploring parameters related to knowledge, attitude, and psychological aspects within the context of sustainability. The work of Ramdas & Mohamed [26] provides a suitable reference, as it delves into the interconnectedness between knowledge, attitude, and behavioural intention, drawing insights from the theory of action. This theory suggests that individuals' decisions to engage in certain behaviours are influenced by their perceptions of sustainability practices rather than solely by extreme desires or unconscious motives. The research underscores the importance of quantitatively measuring knowledge while also recognizing the qualitative aspects of attitudes towards best practices. The theoretical framework presented by Ramdas & Mohamed integrates tangible elements like knowledge with intangible factors such as consciousness or attitudes, which serve as motivators for adopting sustainable business practices. Moreover, the theory posits a correlation between willingness to participate in sustainability efforts and factors like knowledge, awareness, attitude, or consciousness, emphasising the interdependence of these elements in fostering sustainable behaviours. Considering the focus on knowledge, attitude, and psychological aspects in this research, the findings from Ramdas & Mohamed (Ramdas & Mohamed, 2014) offer valuable insights and support for the study's objectives, ensuring relevance and accuracy in the analysis and findings.

The literature review underscores various factors influencing the quality of plastic parts manufactured through injection moulding, including material selection, design, and processing parameters. Researchers have focused on minimising defects using the Taguchi method, particularly addressing issues like warpage and shrinkage. Processing parameters have been extensively studied for their impact on part quality, often integrated with simulation packages to optimise their influence. Some studies explore how the Taguchi method enhances mechanical properties, aiming to improve composite material performance. However, this research aims to advance beyond single characteristic optimisation by combining quality characteristics using a composite desirability function, allowing for a more comprehensive assessment of part quality. Expert elicitation proves useful in capturing implicit judgments, particularly in scenarios lacking empirical data, offering valuable insights for decision-making and modelling. Integrating sustainability into the injection moulding optimisation process faces challenges like technical complexity and data availability, requiring a collaborative effort to address these limitations effectively.

This study focuses on the challenges in incorporating recycled materials in plastic manufacturing, particularly in injection moulding, due to the degradation of mechanical properties compared to virgin materials. While injection moulding parameters significantly impact product quality, the current approach often relies on trial and error rather than systematic optimisation, leading to material wastage. Integrating sustainability into this optimisation process faces challenges like technical complexity and data availability. This research aims to investigate how injection moulding parameters affect mechanical properties, optimise these parameters using the Taguchi method, and characterise them with sustainability competency. It emphasises thermoplastic polypropylene (PP) and excludes thermosets, focusing on tensile strength and flexural modulus for both virgin and recycled plastics. The study will utilise the Taguchi method and the desirability function to enhance part quality while considering sustainability principles, aiming to contribute to a more responsible manufacturing future.

In this study, the attitudinal parameter serves as a mathematical representation of the emotional inclinations of design stakeholders or decision-makers, acknowledging that attitudes significantly shape behaviour and decision-making processes. (D. Aikhuele & Turan, 2018; Turan, Johan, Lanang, et al., 2016; Wan Lanang et al., 2017) demonstrate that knowledge levels influence awareness and attitudes, impacting both willingness to pay and environmental literacy. Supported by (Ayasrah & Mohd Turan, 2022), the theory of reasoned action suggests a link between beliefs, attitudes, intentions, and behaviours. Attitudes can be influenced by various factors such as environmental concerns, personal values, and motivations, as highlighted by (D. O. Aikhuele & Turan, 2018). (Ayasrah et al., 2024) propose an attitudinal parameter (𝜆) to capture decision makers' risk attitudes, enabling classification as risk-averse, risk-neutral, or risk-seeking. This parameter facilitates a deeper understanding of decision makers' emotional disposition and its influence on decision-making processes, ultimately impacting sustainability practices. Additionally, expert elicitation proves valuable in capturing implicit judgments, particularly in fields where expert knowledge shapes probability distributions for uncertain variables. This method allows for the incorporation of expert opinions into decision-making analyses, especially in scenarios where empirical data is lacking or traditional data collection methods are impractical. Following a well-defined protocol during the elicitation process ensures that judgments are based on evidence and personal experience, providing scientifically informed insights for decision-making and modelling purposes across various fields. Additionally, embedding sustainable manufacturing practices into the optimisation process will ensure the efficient utilisation of resources and minimise environmental impact, aligning with the broader goal of sustainability in the plastic industry.

2.1 RESEARCH FRAMEWORK

This research is divided into three main phases: selecting process parameters and quality characteristics, optimizing process parameters, and establishing quantitative relationships, and integrating sustainability competency into characterising process parameters. The first part explains how variables are chosen, including process parameters and quality characteristics. Then, it discusses how optimisation is done in two steps: first, through Taguchi experiments and mechanical testing, followed by analysis using the S/N ratio method; second, through the desirability function and verification studies. Next, it outlines how to establish a quantitative relationship between process parameters and quality characteristics. Finally, it explores how sustainability competency is incorporated into characterising injection moulding process parameters. Figure 2.1 shows the proposed framework visually, outlining the structure of the research.

2.2 PHASE 1: PROCESS PARAMETER AND QUALITY CHARACTERISTIC SELECTION

In the first phase of this research, emphasis is placed on the selection of quality characteristics and process parameters essential for injection moulding. Quality characteristics, such as dimensional properties, surface appearance, and mechanical strength, play a critical role in evaluating part effectiveness. Based on an extensive literature review, the primary focus is on assessing the tensile strength and flexural modulus of recycled polypropylene parts. Through thorough research, eight key parameters have been identified: melt temperature, injection pressure, injection speed, injection time, holding pressure, holding time, cooling time, and mould temperature (held constant in this study). Three levels are selected for each factor to enable better parameter analysis and optimization. These parameters are chosen based on industrial standards and preliminary testing to ensure the production of parts with acceptable dimensional properties and mechanical strength. The values of these parameters are detailed in Table 2.1, excluding the mould temperature parameter, which remains constant throughout the study.

Table 2.1

SELECTED PROCESS PARAMETERS
Factors	Process Parameters	Level 1	Level 2	Level 3
A	Melt temperature, ℃	180	220	260
B	Injection pressure, MPa	45	50	55
C	Injection speed, mm/s	20	25	30
D	Injection time, Sec	6	7	8
E	Holding pressure, MPa	20	35	50
F	Holding time, Sec	1	2	3
G	Cooling time, Sec	15	20	25

2.3 PHASE 2: OPTIMISING PROCESS PARAMETERS

In the second phase of the research, the focus shifts to optimising the injection moulding process parameters using the Taguchi optimisation method. When multiple responses share similar optimal characteristics, an overall desirability function becomes crucial. This function transforms each response into a dimensionless value, indicating its desirability. After determining individual desirability functions for each response, they are combined into an overall desirability function using a relative weight scale. Regression analysis is then utilised to establish quantitative relationships between product quality and process parameters. This analysis explains the connection between dependent and independent variables, with linear regression being the preferred method. Coefficients in the regression model indicate the extent and direction of the impact of independent variables on the dependent variable. The R-squared value measures the proportion of variability in the dependent variable explained by independent variables, while the P-value indicates the significance of these correlations. Additionally, the standard error of the coefficient provides insight into the precision of coefficient estimates, with smaller values indicating higher precision.

2.4 PHASE 3: CHARACTERISING PROCESS PARAMETERS

In the third phase, the focus is on characterising process parameters, specifically by incorporating sustainability competency into the model derived from the optimization phase. This collaborative equation, known as the basic model for forecasting sustainability competency value, integrates correlations between injection moulding parameters. Attitude is identified as a crucial factor in this integration, particularly within the demographic context of industry executives and non-executives, which are categorised into operational teams. The qualitative responses from survey questions were transformed into quantitative data using the attitudinal theory, as demonstrated in Table 2.2, aligning with a common practice among previous researchers (Ahmed Shaikh, 2016; Jiménez & San-Martín, 2017; Turan et al., 2017).

Table 2.2

THE INTERPRETATION FROM SCALE TO ATTITUDINAL
Scale	Value factor	Attitudinal response
Yes	1	Risk averse
No	-1	Risk seeking
Neutral	0	Neutral

The incorporation of job design and sustainability competency factors further enhances the model, emphasising knowledge and behaviour within the broader framework of sustainability competency. Functional attributes, termed sustainability competency descriptors, are outlined to encapsulate the dimensions of sustainability competency within different roles. These descriptors are comprehensively outlined in Table 2.3, encapsulating the intricate dimensions of sustainability competency within the context of the staff's role. Additionally, survey distribution and data collection methods are discussed, including the distribution of surveys via postal methods and the subsequent organisation and analysis of collected data. The conversion of qualitative responses into quantitative data, expert elicitation, and regression analysis for sustainability competency value are also described in detail, emphasising the comprehensive approach employed in this phase.

Table 2.3

JOB DESIGN BASED ON SUSTAINABILITY COMPETENCY DESCRIPTORS
Job design	Sustainability competency descriptors
Job design	Day shift	Night shift
Executive	W1	W4
Non-executive	W2	W3

3.1 EXPERIMENTAL RESULTS

The data obtained from both tensile testing and flexural testing of all the specimens were organised into a tabular format. The average values derived from these data sets were then utilised to analyse the mechanical properties exhibited by the recycled material based on the settings of the process parameters. The results for the tensile strength and flexural modulus of each specimen are available in Tables 3.1 and 3.2.

Table 3.1

TENSILE STRENGTH RESULTS
Trial	Test 1 (kgf/cm²)	Test 2 (kgf/cm²)	Test 3 (kgf/cm²)	Average (kgf/cm²)
1	194.86	195.38	191.90	194.05
2	192.93	188.49	191.87	191.10
3	193.89	196.38	195.47	195.25
4	193.00	193.38	193.13	193.17
5	197.71	198.09	195.99	197.26
6	195.90	198.14	195.30	196.45
7	196.23	196.64	195.75	196.21
8	195.88	196.43	196.98	196.43
9	194.77	194.19	195.44	194.80
10	188.69	190.27	186.74	188.57
11	189.20	191.29	186.28	188.92
12	188.46	189.06	190.80	189.44
13	187.70	185.94	184.39	186.01
14	188.09	187.55	189.59	188.41
15	183.21	180.04	183.81	182.35
16	191.39	191.56	189.97	190.97
17	188.86	190.54	189.85	189.75
18	189.92	188.07	187.46	188.48
19	170.36	171.97	171.71	171.35
20	165.45	163.13	166.47	165.01
21	174.70	178.74	173.74	175.72
22	172.59	172.94	172.02	172.52
23	169.45	167.34	168.67	156.48
24	170.90	171.40	169.56	170.62
25	166.57	166.43	168.24	167.08
26	156.48	154.60	154.90	155.33
27	164.69	166.06	166.50	165.75

Table 3.2

FLEXURAL MODULUS RESULTS
Trial	Test 1 (kgf/cm²)	Test 2 (kgf/cm²)	Test 3 (kgf/cm²)	Average (kgf/cm²)
1	9770.61	9611.09	9339.32	9573.67
2	10028.71	9843.2	9740.2	9870.7
3	9815.81	9818.07	9515.52	9716.47
4	10052.5	9269.43	9617.57	9646.5
5	9891.04	9979.72	9756.89	9875.88
6	9573.03	9532.09	9535.96	9547.03
7	9554.02	9827.06	9768.56	9716.55
8	9922.6	10181.62	10166.62	10090.28
9	10076.53	10141.78	10031.23	10083.18
10	10200.89	10067.14	9715.51	9994.51
11	9653.35	9742.88	9366.99	9587.74
12	9281.37	9405.2	9026.06	9238.54
13	9802.49	9482.05	9859.92	9714.82
14	9813.99	9643.14	9453.32	9636.82
15	9762.54	9712.03	10054.24	9842.94
16	9627.91	9827.47	9602.13	9685.84
17	9608.54	9944.02	9794.04	9782.2
18	9776.11	9634.19	9580.49	9663.6
19	9174.61	9372.79	9353.5	9313.63
20	9349.34	9118.98	9352.78	9273.7
21	9267.48	9302.7	9049.11	9206.43
22	9802.11	9601.5	9753.76	9719.12
23	9137.07	9381.04	9064.62	9194.24
24	9901.27	9369.47	9398.59	9556.44
25	9377.89	9171.33	9337.98	9295.73
26	8968.19	9327.65	9203.26	9166.37
27	9449.53	9636.18	9490.64	9525.45

Tables 3.3 and 3.4 display rankings of process parameters' impact on tensile strength and flexural modulus, using signal-to-noise ratios. The delta value, calculated from highest and lowest averages for each factor, measures effect size. Melt temperature ranks highest for both qualities, followed by other factors like injection time and holding time. Injection pressure ranks lowest. For flexural modulus, the same trend is observed, with melt temperature being most influential and holding pressure least.

Table 3.3

RESPONSE TABLE FOR THE SIGNAL TO NOISE RATIO OF TENSILE STRENGTH
Level	A	B	C	D	E	F	G
1	45.80	45.31	45.31	45.11	45.30	45.15	45.32
2	45.49	45.28	45.20	45.34	45.19	45.32	45.28
3	44.50	45.21	45.28	45.34	45.30	45.33	45.19
Delta	1.30	0.10	0.11	0.24	0.11	0.19	0.13
Rank	1	7	5	2	6	3	4

Table 3.4

RESPONSE TABLE FOR THE SIGNAL TO NOISE RATIO OF FLEXURAL MODULUS
Level	A	B	C	D	E	F	G
1	79.82	79.58	79.68	79.64	79.67	79.53	79.63
2	79.72	79.68	79.59	79.60	79.65	79.75	79.69
3	79.43	79.70	79.69	79.72	79.64	79.67	79.64
Delta	0.39	0.12	0.10	0.12	0.03	0.22	0.07
Rank	1	3	5	4	7	2	6

3.2 PARAMETERS OPTIMISATION

After individually optimizing tensile strength and flexural modulus, both responses are optimised together using desirability functions. Each response is treated as an objective function, with specific process parameters identified for maximum tensile strength and flexural modulus. For example, a melt temperature of 180°C, a holding time of 3 seconds, and other parameters yield a tensile strength of 203.82 kgf/cm². Similarly, optimal parameters for maximum flexural modulus include a melt temperature of 180°C and others, resulting in a modulus of 10005 kgf/cm². However, optimising each response separately may lead to different optimal parameters. To address this, a composite desirability function method is used to find a single optimal value that improves all responses together. The outcomes show optimal parameter values where both tensile strength and flexural modulus have the same desirability value. These results are summarised in Table 3.5.

Table 3.5

RESULTS OF MULTI RESPONSE OPTIMISATION
Responses	Factors							Predicted responses (kgf/cm²)	Desirability value (kgf/cm²)
Responses	A	B	C	D	E	F	G	Predicted responses (kgf/cm²)	Desirability value (kgf/cm²)
Tensile strength	180	55	30	8	20	3	25	199	0.9767
Flexural modulus	180	55	30	8	20	3	25	10005	0.9767

A confirmation test serves to validate the research outcomes by ensuring that the optimised process parameters indeed produce the expected quality characteristics. Despite challenges in achieving a perfect match due to repeatability factors, the confirmation experiment was conducted using the established optimal parameters to assess any differences between these parameters and the actual manufacturing process. Maintaining the same material properties as in the initial experiment helped mitigate variations. Three specimens were manufactured using the optimised parameters, followed by tensile and flexural tests. The results were compared with the anticipated values, showing a close match as shown in Table 3.6. For instance, the anticipated and observed tensile strengths were 199 kgf/cm² and 197 kgf/cm² respectively, with a percentage error of 1%. Similarly, the flexural modulus values showed a minimal error of 0.03%. This confirmation experiment underscores the validity of the optimisation approach, affirming the reliability and accuracy of the optimized process parameters in achieving the desired quality characteristics.

Table 3.6

CONFIRMATION TEST OF OPTIMISED PARAMETERS
Responses	Predicted results (kgf/cm²)	Confirmation results (kgf/cm² )	Error (%)
Tensile strength	199	197	1
Flexural modulus	10005	10002	0.03

3.2 PARAMETERS OPTIMISATION

Regression analysis is a statistical method used to explore the relationship between variables. In multiple linear regression, the R-square value, typically between 0.8 and 1, measures how well the model predicts future outcomes based on existing data. It indicates the accuracy of the model in forecasting results. In this study, regression was used to model the relationship between process parameters and quality attributes. Tables 3.7 and 3.8 show the coefficients for each injection moulding parameter, detailing their impact on tensile strength and flexural modulus respectively.

Table 3.7

COEFFICIENT OF PREDICTION MODEL FOR TENSILE STRENGTH
Term	Coefficient	SE Coefficient	T-Value	P-Value	VIF
Constant	253.3	17.0	14.89	0.000
Melt temperature	-0.3373	0.0280	-12.04	0.000	1.00
Injection pressure	-0.173	0.224	-0.77	0.449	1.00
Injection speed	-0.055	0.224	-0.24	0.810	1.00
Injection time	2.31	1.12	2.07	0.053	1.00
Holding pressure	-0.0039	0.0747	-0.05	0.959	1.00
Holding time	1.83	1.12	1.64	0.118	1.00
Cooling time	-0.253	0.224	-1.13	0.274	1.00

Table 3.8

COEFFICIENT OF PREDICTION MODEL FOR FLEXURAL MODULUS
Term	Coefficient	SE Coefficient	T-Value	P-Value	VIF
Constant	9603	735	13.07	0.000
Melt temperature	-5.37	1.21	-4.44	0.000	1.00
Injection pressure	13.71	9.69	1.42	0.173	1.00
Injection speed	1.29	9.69	0.13	0.895	1.00
Injection time	46.5	48.4	0.96	0.349	1.00
Holding pressure	-1.04	3.23	-0.32	0.751	1.00
Holding time	75.9	48.4	1.57	0.134	1.00
Cooling time	1.60	9.69	0.17	0.870	1.00

A mathematical model derived from regression analysis needs validation to ensure its accuracy in real manufacturing processes. To confirm its reliability, an actual experiment mirroring previous setups was conducted, and mechanical tests were carried out accordingly. Table 3.9 shows the results of this verification test. The model predicted tensile strength and flexural modulus as 197.19 kgf/cm² and 9827.05 kgf/cm² respectively, while the confirmed values were 197.26 kgf/cm² and 9875.88 kgf/cm². The error percentages for tensile strength and flexural modulus were calculated at 0.03% and 0.4% respectively, both below 2%. These validation outcomes underscore the practicality and effectiveness of the regression model for both responses.

Table 3.9

CONFIRMATION TEST OF REGRESSION MODEL
Responses	Factors							Predicted results (kgf/cm²)	Confirmation results (kgf/cm²)	Error (%)
Responses	A	B	C	D	E	F	G	Predicted results (kgf/cm²)	Confirmation results (kgf/cm²)	Error (%)
Tensile strength	180	50	25	7	35	2	20	197.19	197.26	0.03
Flexural modulus	180	50	25	7	35	2	20	9827.05	9875.88	0.4

3.3 INCORPORATING SUSTAINABILITY COMPETENCY

The model developed in Phase 2 lacks the comprehensive competency needed to foster innovation due to its oversight of staff roles and behavioural elements like trust and honesty crucial for effective knowledge creation. To address this, a sustainability competency definition is introduced to incorporate staff roles and attitudinal factors. This enhancement aims to predict competency value more accurately by integrating critical elements like "Knowledge" and "Behaviour" into the equation using feedback from questionnaires. These elements are assigned significant weightages, as shown in Table 3.10, to better capture their impact on the knowledge creation process, thus improving the equation's predictive capacity.

Table 3.10

JOB DESIGN WEIGHTAGE FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR
Job design	Weightage
Job design	Knowledge	Behavioural
Executive	0.49	0.52
Non-executive	0.47	0.67

The job design has been characterised by integrating staff functions with the sustainability competency definition, including knowledge and behaviour. This integration is represented by predictive equations in Tables 3.11 and 3.12, forming a comprehensive model that captures the relationship between staff roles, attitudes, and job design. These appendices offer a thorough examination of regression results and their connection to knowledge and behaviour factors.

Table 3.11

CHARACTERISATION OF OPERATIONAL TEAM FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR (TENSILE STRENGTH)
Phase	Operational team	Characteristic (predictive) model
Before characterised	-	TS = 253.3–0.3373 A − 0.173 B- 0.055 C + 2.31 D − 0.0039 E + 1.83 F − 0.253 G
After characterised	W1	Wk1 = 116.72–0.1567 A − 0.0516 B + 0.0058 C + 0.9288 D + 0.0074 E + 0.8097 F − 0.0990 G
	W1	Wb1 = 129.14–0.1733 A - 0.0570 B + 0.0060 C + 1.0280 D + 0.0081 E + 0.8960 F - 0.1090 G
	W2	Wk2 = 125.15–0.1660 A - 0.0749 B - 0.0457 C + 1.0276 D - 0.0090 E + 0.6307 F - 0.0915 G
	W2	Wb2 = 166.39–0.2234 A - 0.0736 B + 0.0083 C + 1.3240 D + 0.0105 E + 1.1543 F – 0.1411 G
	W3	Wk3 = 119.91–0.1596 A - 0.1054 B + 0.0390 C + 1.2713 D - 0.0042 E + 1.0912 F - 0.1537 G
	W3	Wb3 = 170.94–0.2275 A - 0.1503 B - 0.0556 C + 1.8123 D - 0.0060 E + 1.5555 F – 0.2191 G
Wk = knowledge, Wb = behaviour, TS = tensile strength

Table 3.12

CHARACTERISATION OF OPERATIONAL TEAM FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR (FLEXURAL MODULUS)
Phase	Operational team	Characteristic (predictive) model
Before characterised	-	FM = 9603 − 5.37 A + 13.71 B + 1.29 C + 46.5 D − 1.04 E + 75.9 F + 1.60 G
After characterised	W1	Wk1 = 4339 − 2.56 A + 7.36 B − 1.30 C + 38.46 D + 0.56 E + 43.89 F + 1.83 G
	W1	Wb1 = 6186–3.65 A -3.65 B − 1.85 C + 54.80 D + 0.80 E + 62.60 F + 2.61 G
	W2	Wk2 = 4025 − 2.26 A + 13.14 B + 6.79 C + 7.77 D − 1.16 E + 29.65 F + 3.15 G
	W2	Wb2 = 5738–3.23 A + 18.73 B + 9.68 C + 11.10 D − 1.65 E + 42.30 F + 4.49 G
	W3	Wk3 = 5395 − 2.90 A − 0.99 B – 4.05 C + 20.13 D − 0.83 E + 33.87 F − 2.83 G
	W3	Wb3 = 5726 − 3.08 A − 1.04 B − 4.30 C + 21.4 D − 0.88 E + 35.9 F − 3.00 G
Wk = knowledge, Wb = behaviour, FM = flexural modulus

The R-squared (R²) coefficient measures how well a regression model fits the data. It indicates how accurately the predictors within the model explain the response. Table 3.13 and 3.14 show the R² values for the tensile strength and flexural modulus models respectively. Across all models, whether before or after characterisation, those incorporating sustainability competency definitions and attitudinal aspects demonstrate high regression coefficients. This suggests they explain a significant portion of the variability in competency dimensions. The values from the characterized model highlight the strength of competency attributed to integration, where staff attitudes toward knowledge creation interact with mechanical properties based on optimized parameters established in Phase 2.

Table 3.13

R-SQUARED (R²) ASSESSMENT RESULT FOR ALL TENSILE STRENGTH
Job design	Operational team	Before characterisation		After characterisation
		Before characterisation		Knowledge		Behaviour
		R²	R² (adj)	R²	R² (adj)	R²	R² (adj)
Executive	W1	89.00%	85.00%	89.03%	84.99%	89.03%	84.99%
Non-executive	W2			86.58%	82.56%	88.25%	84.72%
Non-executive	W3			85.59%	81.27%	86.49%	82.43%

Table 3.14

R-SQUARED (R²) ASSESSMENT RESULT FOR ALL FLEXURAL MODULUS
Job design	Operational team	Before characterisation		After characterisation
		Before characterisation		Knowledge		Behaviour
		R²	R² (adj)	R²	R² (adj)	R²	R² (adj)
Executive	W1	57.00%	57.00%	56.30%	40.19%	56.30%	40.19%
Non-executive	W2			52.69%	35.26%	52.69%	35.26%
Non-executive	W3			45.70%	25.69%	45.70%	25.69%

3.4 SUMMARY

The experiment followed the Taguchi design, evaluating mechanical properties like tensile strength and flexural modulus, documented in Tables 3.1 and 3.2. Signal-to-noise ratio analysis identified key injection moulding factors affecting recycled polypropylene. Melt temperature was most influential for tensile strength, followed by injection and holding time, while for flexural modulus, it was melting temperature, holding time, and injection pressure. Optimizing both properties simultaneously resulted in 199kgf/cm² tensile strength and 10005 kgf/cm² flexural modulus with parameters like 180°C melt temperature and 8s injection time. A confirmation test validated these parameters with error (%) values of 1% for tensile strength and 0.03% for flexural modulus. Regression analysis developed a mathematical model showing 0.03% and 0.4% error (%) for tensile strength and flexural modulus, affirming model reliability. The characterisation of operational teams for knowledge and behaviour found no significant differences between models before and after, validating the sustainability competency model for self-assessment of knowledge creation.

This research explores the relationship between job design and attitudes using predictive models with integrated algorithms. By analysing data from experiments and questionnaires on process parameters and competency, the study investigates how job design, operational teams, and sustainability competency descriptors interact in injection moulding investigations. These correlations help predict overall competency performance, symbolised as competency value distributions. The characterised model generates metrics for competency performance, termed sustainability competency, capturing the interaction between job design, operational teams, sustainability competency descriptors, and other factors. Additionally, a mathematical model is developed through regression analysis to understand the relationship between injection moulding processes and mechanical properties. The regression analysis shows strong correlations between process parameters and attitudinal elements, enhancing the competency model. Finally, the study's main contributions include identifying optimal injection moulding parameters, simultaneously optimising quality characteristics, and refining the competency model by incorporating attitudinal elements, all essential for achieving competitiveness and innovation in the plastic industrial sector.

This research focuses on optimising the production of recycled materials to improve their strength using Taguchi experimental design and the desirability function approach. Future studies should explore additional aspects of material strength, such as impact and compression, and test different batches of recycled materials for consistency. Advanced techniques like scanning electron microscopy can provide more precise insights into material properties. Understanding how various factors like temperature and machine settings influence the final product is essential, along with investigating the effects of mixing recycled materials with new ones and additives. The newly developed model helps predict how well the manufacturing process integrates sustainability, which is crucial for industry growth and ensuring quality standards.

Ethical Approval

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors' contributions

The authors, Anis Izzati Md Yusoff, Faiz Mohd Turan, and Nur Qurratul Ain Adanan carried out the conceptualisation, drafting of the research work, and editing of the final article. All authors approved the submitted version.

Funding

This research was funded by a grant from Universiti Malaysia Pahang Al-Sultan Abdullah (RDU233016).

Availability of data and materials

Not applicable

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No competing interests reported.

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Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters

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Abstract

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1.0 INTRODUCTION

2.0 METHODOLOGY

2.1 RESEARCH FRAMEWORK

2.2 PHASE 1: PROCESS PARAMETER AND QUALITY CHARACTERISTIC SELECTION

2.3 PHASE 2: OPTIMISING PROCESS PARAMETERS

2.4 PHASE 3: CHARACTERISING PROCESS PARAMETERS

3.0 RESULTS

3.1 EXPERIMENTAL RESULTS

3.2 PARAMETERS OPTIMISATION

3.2 PARAMETERS OPTIMISATION

3.3 INCORPORATING SUSTAINABILITY COMPETENCY

3.4 SUMMARY

4.0 CONCLUSIONS

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