Next Article in Journal
Influence of Temperature on Interlayer Adhesion and Structural Integrity in Material Extrusion: A Comprehensive Review
Previous Article in Journal
Review of Tribological and Wear Behavior of Alloys Fabricated via Directed Energy Deposition Additive Manufacturing
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Novel Method for Manufacturing Molds for CFRP Prepreg Lamination Using Polymeric Acrylic Resin–Aluminum Trihydrate

1
Department of Manufacturing Engineering, Faculty of Industrial Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
2
Department of Mechanical Systems Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2025, 9(6), 195; https://doi.org/10.3390/jmmp9060195
Submission received: 25 March 2025 / Revised: 2 June 2025 / Accepted: 4 June 2025 / Published: 11 June 2025

Abstract

In the composite materials industry, the fabrication of complex parts often necessitates the use of specialized tools, such as milled molds with intricate geometries. Among these, machined aluminum molds are widely regarded as effective tools for laminating CFRP (Carbon Fiber Reinforced Polymer) prepreg materials. However, the cost and time associated with machining aluminum molds can be significant. This paper presents a novel method for manufacturing molds using polymeric acrylic resin combined with aluminum trihydrate material (commercially known as DuPont Corian materials), offering a potential alternative with reduced complexity and cost. The study investigates the influence of various milling parameters, such as tool speed, tool type, feed rate, and depth of cut on the mechanical properties and surface finish of the molds. Also, laminating tests are conducted; results indicate that laminating tools produced through this method achieve competitive mechanical performance, including a hard, smooth surface with low roughness, making them viable candidates for industrial use. The proposed approach is particularly beneficial in terms of reducing machining time and overall costs while maintaining the necessary precision and durability for high-performance applications. This method, therefore, represents a promising solution for manufacturers seeking to optimize mold production processes in the composite materials industry.

1. Introduction

The fabrication of high-quality molds plays a crucial role in composite manufacturing, particularly in industries such as aerospace, automotive, and wind energy, where precision and surface integrity are critical for producing lightweight, high-performance composite structures. Traditionally, aluminum has been the preferred mold material due to its thermal stability, durability, and machinability.
However, alternative materials such as polymeric acrylic resin combined with aluminum trihydrate have gained attention due to their cost-effectiveness, ease of machining, and ability to achieve a high surface quality. As composite manufacturing continues to advance, the demand for affordable, efficient, and high-quality mold materials has increased, making it essential to explore non-metallic options such as Corian [1,2,3,4,5].
Composite manufacturing involves the layering of fiber reinforcements, such as carbon, aramid, or glass fiber, with polymer matrices, which are then cured under heat and pressure to create strong, lightweight structures. One key challenge in this process is ensuring that the mold provides a smooth, defect-free surface. In addition, composite structures are often manufactured using sandwich construction techniques, where lightweight core materials (such as honeycomb, foam, or balsa wood) are placed between layers of composite laminates. These composite sandwich structures offer exceptional strength-to-weight ratios, impact resistance, and thermal insulation properties, making them ideal for aircraft panels, automotive components, marine structures, and wind turbine blades [5,6,7,8,9,10,11,12].
This study focuses on analyzing the milling parameters essential for the efficient fabrication of Corian molds and evaluating the resulting surface hardness and roughness. Recent research, such as that by Smith et al. [13], has demonstrated that optimizing machining parameters can significantly improve the surface integrity of polymer composites, while a study by Zhang and Li [14] emphasized the role of process parameters in reducing tool wear and improving the surface finish in thermoplastic composites. Similarly, Nasir et al. [15] explored machining strategies for polymer matrix composites, highlighting the importance of feed rate and spindle speed control to minimize surface defects. More recent work, such as that by Lee et al. [16], examined the effects of cutting parameters on the surface quality of polymer composite materials, finding that advances in tool coatings and cooling methods could lead to even further improvements in the surface finish and longevity of tools. Additionally, Zhang et al. [17] investigated the use of artificial intelligence in optimizing CNC milling parameters, showing that machine learning algorithms could provide new insights into the parameter combinations that maximize efficiency and part quality. More recent studies, such as by Patel et al. [18], have also highlighted advancements in the additive manufacturing processes for composite molds, which complement traditional machining methods in producing high-quality surfaces for complex geometries.
In contrast to the aforementioned studies, the current investigation brings several novel aspects to the field. While previous studies have primarily focused on optimizing the milling of standard polymer composites and thermoplastics, this study specifically addresses the unique challenges presented by Corian material, a solid surface commonly used in molding applications. Additionally, while the cited research emphasizes surface integrity and tool wear, the current study places greater focus on achieving the smooth, uniform mold surface that is critical for composite layup and curing, particularly for complex geometries such as aerodynamic surfaces and structures. The novel aspect of this study is the use of prepreg CFRP material for the composite layup, which has not been thoroughly explored in previous studies involving Corian molds. Prepreg materials require the precise control of temperature and pressure during curing to achieve optimal mechanical properties, and the current study aims to address this challenge by integrating prepreg with Corian molds, providing valuable insights into its use with non-traditional mold materials. Building on these findings, the present investigation aims to optimize the milling process for the Corian material to achieve a smooth and uniform mold surface suitable for composite layup and curing. To validate the mold’s performance, a prepreg CFRP laminate will be produced using the fabricated Corian mold. Given that prepreg materials require the precise control of temperature and pressure during curing to attain optimal mechanical properties, careful processing will be implemented. The final composite part will undergo visual inspection and qualitative evaluation, emphasizing the surface smoothness, fiber alignment, porosity, and potential defects such as voids or fiber/surface distortions [19].
The optimization of the milling parameters will be conducted using the Taguchi method, a robust statistical approach widely applied in manufacturing to improve process efficiency, consistency, and product quality. By systematically varying parameters such as spindle speed, feed rate, depth of cut, tool type, and cutting strategy, this study aims to identify the optimal conditions that result in minimal surface roughness, improved mold longevity, and enhanced machinability.
The novelty of this research lies in the application of Corian as a mold material for composite structures, an area that has received limited attention in the existing literature. The findings of this study could open new possibilities for manufacturers seeking lightweight, easy-to-machine, and high-quality molds, particularly for prototyping, low-to-medium production runs, and non-metallic mold applications [20,21,22,23,24].

2. Materials and Methods

2.1. Current State of Research

In recent years, various machinable materials have been studied as replacements for conventional aluminum alloys in molds for Carbon Fiber Reinforced Polymer (CFRP) production. These materials include composites, ceramics, and advanced polymers, each offering unique advantages and limitations. Below are the key studies and findings regarding these materials, including their potential and shortcomings in the context of using them as CFRP molding tools.
Zitoune et al. [25] investigated the machining of polymer matrix composites, highlighting that while the composites offer a lower weight and higher chemical resistance compared to metals, they suffer from poor thermal conductivity, which can negatively affect uniform curing. Additionally, such materials may experience degradation under high mechanical loads, limiting their lifespan as mold materials.
Ceramic materials such as silicon carbide (SiC) and aluminum oxide (Al2O3) have been considered for specialized CFRP molds requiring high-temperature stability. Research by Wang and Chou [26] shows that while ceramics exhibit excellent thermal stability and dimensional integrity during high-temperature cures, they are brittle and susceptible to cracking during mechanical impacts. Additionally, ceramics are difficult to machine, significantly increasing manufacturing costs.
Advanced thermoplastics such as PEEK (Polyether Ether Ketone) and PEI (polyetherimide) have been evaluated for CFRP mold applications due to their good thermal resistance and ease of processing. Stokes-Griffin and Compston [27] reviewed thermoplastic mold materials, reporting that while polymers offer advantages like ease of machining and low tool wear, they suffer from moderate mechanical strength and are not ideal for highly stressed or thermally intensive applications.
Corian, an acrylic solid surface material originally developed by DuPont (USA), has emerged as a promising alternative for molds, especially in prototyping and small-batch CFRP production. Firstly, Corian demonstrates excellent machinability, allowing for the production of complex mold geometries with relatively low tool wear and reduced machining times. This contrasts with ceramics, which are difficult to machine and require specialized equipment, and advanced thermoplastics like PEEK, which, although machinable, demand higher processing costs. Corian also has a good chemical resistance, withstanding exposure to most of the resins and release agents typically used in composite molding, an important property shared with the more expensive materials like PEEK [28].
Secondly, Corian provides a smooth and uniform surface finish, critical for ensuring high-quality prepreg layup. Johnson et al. [29] highlighted that surface defects on the mold can directly translate into defects on the CFRP part; thus, the superior surface quality achievable with Corian supports defect minimization. In comparison, fiber-reinforced composites often exhibit surface roughness and fiber print-through, which can compromise the finish of the molded CFRP part.
One very important aspect is that Corian offers cost advantages. The material is substantially more affordable than ceramics and high-performance polymers while delivering acceptable thermal and mechanical properties for low- and medium-temperature curing cycles [30]. For small production runs and prototyping, where the economic viability of the tooling is critical, Corian provides a balance between performance and cost-efficiency that metals, ceramics, and high-end composites cannot easily match.
Based on the studies listed above, the discussed material is a particularly good choice for CFRP mold production when
  • High surface quality is a priority;
  • Rapid and economical mold fabrication is needed;
  • The application involves small-to-medium scale production;
  • The curing temperature requirements are moderate.
Therefore, the use of Corian molds represents a novel, pragmatic solution in the field of composite manufacturing, especially suited for prepreg CFRP layups under controlled conditions.

2.2. Technical Properties of Corian

Density: ~1730 kg/m3
Composition: ~66% aluminum trihydrate (ATH)—enhances thermal stability, fire resistance, and surface hardness; ~33% acrylic polymer—provides flexibility, impact resistance, and machinability
Thermal Expansion Coefficient: 27–35 μm/m·°C
Maximum Operating Temperature: ~100–120 °C [31,32,33,34,35,36,37,38,39,40,41].

2.3. Technical Properties of the Prepreg Material

The component to be laminated in this study will be made using prepreg CFRP. For this study, the HexPly M35-4 prepreg manufacrued by Hexcel, Dagneux, France is selected for laminating the composite part. The HexPly M35-4 is a versatile epoxy-based prepreg designed for use in applications requiring a lower curing temperature. It is well-suited for curing at approximately 80 °C, which is more compatible with the Corian mold’s thermal characteristics.
HexPly M35-4 Prepreg:
  • Fiber Type: HS Carbon 24K;
  • Resin Matrix: Epoxy-based resin suitable for lower curing temperatures;
  • Cure Cycle: 80 °C for 12 h;
  • Areal Weight: 200 g/m2;
  • Glass Transition Temperature (Tg): ~130 °C;
  • Ultimate Tensile Strength: 2900 MPa;
  • Modulus of Elasticity: 150 GPa.
The HexPly M35-4 prepreg’s ability to cure at 80 °C aligns with the requirements of this study due to the ease of processing and high mechanical properties. This material is suitable for manufacturing composite components with polymeric acrylic resin–aluminum trihydrate molds, where lower curing temperatures are desired to avoid any potential thermal damage to the mold material [42].

2.4. Description of the CNC Machine Used for Milling the Corian Molds

A 3-axis CNC milling machine (Hurco VN1, Indianapolis, IN, USA) is employed for the precise and repeatable machining of the Corian mold. The machine features three linear axes (X, Y, and Z), which allow for relatively complex geometries and intricate surface finishes to be machined with high accuracy. The machine offers a positioning accuracy within ±0.01 mm, ensuring precise cuts and dimensional stability, and a spindle motor power of 3.5 kW, giving a spindle speed range of 2000–24,000 RPM, thus allowing for optimization based on the material being cut and the type of tooling used.
In this study, high-speed steel (HSS) end mills are used. HSS tools are commonly used for general purpose milling. They are cost-effective, durable, and provide adequate performance for materials like Corian. HSS tools also offer better edge retention when machining at lower speeds, making them suitable for roughing operations; however, in this case the HSS tools will be used for finishing operations also.
As a cooling strategy, in this study, dry machining is chosen, which means that no external coolant is used during the milling process. This approach is preferred in this case to avoid contamination. The choice of tool material and tool geometry are optimized to handle the heat buildup [42].

2.5. Measuring Equipment and Standards

Three main equipment sets were used to assess the surface quality of the mold, as follows:
  • Surface roughness tester used to assess surface finish—ISR-C300 INSIZE (Suzhou, China);
  • Hardness tester used for measuring surface hardness—ORION D600. Hardness measuring techniques were conducted according to the ASTM D785-08(2015) Standard Test Method for Rockwell Hardness of Plastics and Electrical Insulating Materials; [43]
  • DriveAFM Nanosurf microscope (Liestal, Switzerland).

2.6. Evaluation of Milling Parameters via Taguchi Method

The Taguchi method is a statistical approach for optimizing process parameters to improve quality and robustness with minimal experimental effort. Developed by Genichi Taguchi and popularized in Western industries in the late 20th century, it uses orthogonal arrays to efficiently study the influence of multiple factors. A key feature of the method is its emphasis on minimizing variability through robust design, making it particularly useful for composite material processing and other sensitive manufacturing operations.
Antony et al. [44] highlight that the Taguchi method is especially effective when experimental resources are limited, offering a practical alternative to full factorial designs. Recent works by Lohrasbi et al. [45] and Dettmer and Taboada [46] show its continued relevance in modern fields such as additive manufacturing and thermoplastic welding, often integrating it with computational optimization techniques. The Taguchi method provides a systematic framework for process optimization by utilizing orthogonal arrays, allowing multiple factors and their interactions to be examined through a reduced set of experiments. Rather than investigating each parameter individually, the method assesses the combined effects of factors, promoting efficiency and a comprehensive analysis.
In this work, the Taguchi method was selected for its efficiency in identifying dominant process parameters within practical experimental limits. Based on the literature and preliminary trials, the interaction effects between the spindle speed, feed rate, and depth of cut were not expected to dominate the system’s behavior within the selected machining window. Therefore, a main effects-focused approach was considered applicable.
Results are interpreted using signal-to-noise (S/N) ratios, which quantify the influence of control factors relative to the variability caused by uncontrollable noise factors. By prioritizing robustness, the Taguchi method seeks to enhance the process stability and performance under varying operating conditions, rather than optimizing solely for ideal environments.
This methodology is particularly advantageous in complex manufacturing scenarios, where the simultaneous control of multiple variables is critical to achieving consistent product quality [46].
The presented methodology is employed in the study to optimize the milling parameters, reducing variability and enhancing surface quality. The key factors analyzed include the following:
  • Spindle Speed (RPM): 2000, 4000, 6000;
  • Feed Rate (mm/min): 500, 1000, 1500;
  • Depth of Cut (mm): 0.5, 1.0, 1.5;
  • Tool Type: HSS.
An L9 orthogonal array is used for the experimental trials, reducing the number of required tests while ensuring statistical significance.
The start of the milling procedure consists of material preparation, where Corian blocks are cut to specified dimensions, and tool setup (in this case 6 mm HSS flat end tools are used). The next step consists of milling execution; nine mills are conducted according to the Taguchi matrix. Post-machining cleaning makes the inspection of the machined surfaces possible. The next step consists of assessing the surface roughness for each cut, correspondent to every scenario in the Taguchi matrix.
In the data analysis and comparison step, the ANOVA (Analysis of Variance) is conducted (using Python 3.11) to assess the impact of the milling parameters on the surface finish. After the optimal parameter configuration is deduced using the Taguchi method, the mold is machined, and the roughness is measured again on the clean raw surface. In the next step, the surface of the mold is buffed and polished to a class A surface quality, assured by another roughness measurement [47].
Considering a prepreg CFRP lamination application where high temperature curing cycles are implied, a set of hardness tests are conducted at different thermal regimes (20, 50, 80, 120 °C).
The Corian mold is coated with a chemical release agent, and two layers of UD Hex prepreg CFRP are applied using vacuum technology. The composite part is then cured under controlled conditions. The final composite part is visually inspected for voids and fiber distortion. The mold’s ability to meet the required quality standards is assessed through visual analysis [48,49].

3. Results

3.1. Machining and Evaluation

3.1.1. Machining Parameters

The milling process for Corian is optimized to achieve a high-quality surface finish while minimizing tool wear and heat buildup. The selected parameters (Table 1) ensure efficient material removal and smooth machining.
Selection of the milling parameters listed in Table 1 was guided by a combination of the preliminary machining tests on Corian, manufacturer recommendations, and reference values reported in the literature for similar non-metallic mold materials. The ranges for the spindle speed (4000–8000–12,000 RPM), feed rate (800–1600–2500 mm/min), and depth of cut (0.5–0.75–1.5 mm) were chosen to ensure safe and stable cutting conditions while also being broad enough to capture performance trends relevant to the surface quality. These levels represent realistic settings for low-to-medium volume tool fabrication using CNC milling of Corian-type materials.

3.1.2. Experimental Study and Data Analysis

A 3-orthogonal array was composed, and surface roughness was measured for each cut. In the next step, signal-to-noise (S/N) ratios were calculated using the smaller-the-better criterion, using formula (1), which shows how consistent the results are in reducing roughness [50].
S N = 10 l o g 10 ( 1 n y 2 )
where n is the number of observations and y is the measured response variable (surface roughness [µm]). A higher S/N ratio means better stability and lower surface roughness, while a lower S/N ratio represents a worse performance, meaning rougher surfaces. Each roughness value was transformed into an S/N ratio, showing which conditions consistently produced a better surface. The measured surface roughness and S/N ratios are depicted in Table 2.
In the next step, the ANOVA (Analysis of Variance) was performed to determine which factors significantly affect the surface roughness. Results are depicted in Table 3. ANOVA computations were conducted using Python 3.11, employing its statistical libraries to model and evaluate the experimental data. This method ensured analytical rigor, reproducibility, and efficiency in assessing the significance of factors within the Taguchi design framework. The ANOVA results (Table 3) highlight the relative factor importance, even if not statistically conclusive. Since n = 1 in Equation (1) (un-replicated data), these values should be seen as indicative trends, not statistically conclusive. The main emphasis should instead be placed on the signal-to-noise (S/N) ratios (Table 2), which are central to the Taguchi method. These ratios provide a more meaningful basis for evaluating factor effects in un-replicated designs, as they account for both the magnitude of performance and its consistency, offering a more robust foundation for drawing comparative insights.
A higher F-value reveals that the factor has a stronger influence on surface roughness, where F was calculated according to Equation (2): [51]
F = M e a n   s q u a r e   o f   F a c t o r M e a n   s q u a r e   o f   R e s i d u a l   E r r o r
The depth of cut had the highest influence on surface roughness (p = 0.216, F = 3.62).
Figure 1, Figure 2 and Figure 3, are the main effects plots based on the S/N ratios, using one measurement per parameter level (as per the Taguchi L9 array). The figures illustrate how the mean S/N ratio (lower is worse in smaller-the-better) varies across the levels of spindle speed (Figure 1), feed rate (Figure 2), and depth of cut (Figure 3).
The depth of cut F = 3.62 suggests an important contribution to surface roughness. The spindle speed F = 0.59 and feed rate F = 0.12 have a weaker influence on surface roughness. The depth of cut p = 0.216 has the highest influence on surface roughness, but the p-value is still not statistically significant (<0.05) due to the small analysis data batch. The spindle speed p = 0.628 and feed rate p = 0.887 show a lower impact, suggesting that they are less influential within the tested range.
Thus, we can deduce that the best milling settings for the lowest surface roughness (Ra = 1.3 µm) are as follows:
  • Spindle speed: 12,000 RPM;
  • Feed rate: 1600 mm/min;
  • Depth of cut: 0.5 mm [52].
The optimal milling parameters identified—spindle speed = 12,000 RPM, feed rate = 1600 mm/min, and depth of cut = 0.5 mm—must be interpreted as locally optimal within the discrete range of values tested using the Taguchi L9 orthogonal array. Since the Taguchi method evaluates a limited number of predefined levels per factor, it is effective in screening for dominant variables and identifying promising settings. As such, the results of this study reflect optimization within the experimental bounds defined in Table 1.
To offer a qualitative comparison, a control group was defined using non-optimized milling parameters (Table 4). The settings were selected based on the lower-performing combinations identified during the Taguchi analysis. Relative to the optimized configuration, this control setup, as expected, produced significantly higher surface roughness, along with increased tool marks and a low surface quality.

4. Mold Manufacturing

Based on the findings from the milling parameters study, the tool manufacturing process was initiated. The mold geometry and machining program were first developed using CAD/CAM modules of Autodesk Fusion360 (Figure 4 shows the2D CAD geometry, and Figure 5 shows the isometric view of the 3D CAD geometry) to ensure precision and repeatability. Surface roughness measuring layup is shown in Figure 6. The general dimensions between the mold and the laminated CFRP part will be analyzed in order to check the precision of the molded composite part. Two molds were fabricated (Figure 7) and compared to validate the repeatability of the process. Following this, the initial tool was fabricated, incorporating the optimized milling parameters to achieve the desired surface quality and dimensional accuracy.
Upon completion of the milling process, a buffing and polishing stage was performed to achieve a Class A surface finish. The final surface finish was achieved by polishing the surface in three steps, using M-50, M-100, and M-150 polish compounds (Table 5). This step aimed to enhance the surface smoothness, minimize tool marks, and improve the overall quality of the mold, ensuring its suitability for high-precision applications. After this step the roughness measurement revealed a value of Ra = 0.235 µm for the finished mold surface.
Further, a series of surface hardness tests were conducted to validate the tool for use in prepreg CFRP laminating technology. The mold was gradually heated and surface hardness tests were conducted for different thermal regimes (Table 6).
The evaluation of the fabricated mold involved the lamination of a prepreg CFRP component, followed by a comprehensive surface quality analysis of the final composite part. This assessment aimed to determine the effectiveness of the mold in producing a defect-free surface, ensuring compliance with the required manufacturing standards.
The initial step in the process involved applying a release agent treatment to the mold surface, which is essential to prevent adhesion between the mold’s surface and the prepreg CFRP laminate [52,53].

5. Laminate Manufacturing in the Corian Mold

This chapter focuses on the lamination of a prepreg CFRP part (Figure 8) onto the fabricated mold, examining the effectiveness of the mold in producing a defect-free composite surface. The quality of the final laminated part was analyzed, with an emphasis on surface finish, structural integrity, and ease of demolding.

5.1. Preparation and Layup Process

The mold surface was cleaned with isopropyl alcohol and treated with a high temperature release agent to ensure easy demolding. Two layers of the unidirectional HexPly M35-4 prepreg were placed onto the molding surface in the following layup: [+45/−45]. A peel ply layer was placed over the CFRP to allow for post-processing treatments if required. A breather cloth was added on top to facilitate air evacuation and pressure distribution [53].

5.2. Laminating

In order to keep the test complexity at minimum, the vacuum bagging technique was used to ensure uniform compaction of the layers under the action of temperature according to the polymerization recipe. After placing the assembly in the oven, a vacuum was applied to the system with a constant value of v g = 475   T o r r . The polymerization regime can be observed in Figure 9.

5.3. Final Part Inspection

After curing and demolding, the gross dimensions were checked and compared with the mold dimensions to geometrically validate the laminated part. The untrimmed part (Figure 10) was inspected for defects such as voids, fiber misalignment, and surface irregularities. The surface of the composite part was visually inspected using a microscope (Figure 11, Figure 12 and Figure 13), revealing that the finished part has a good overall surface quality. Also, the round edge inspection determined that the edge is well defined with no defects. Visual analysis confirmed that the Corian mold provided a suitable surface finish for composite fabrication.

6. Conclusions

This study investigated the feasibility of using polymeric acrylic resin–aluminum trihydrate materials for use in mold fabrication, focusing on optimizing the machining parameters through the Taguchi method, analyzing the surface roughness, and evaluating the hardness properties. Taguchi optimization effectively minimized roughness, improving the milling efficiency and process stability. The goal was to determine the feasibility of using this material as an alternative mold material, particularly for low-to-medium temperature composite manufacturing (80 °C). The Taguchi L9 orthogonal array (Table 2) was applied to evaluate the effects of spindle speed, feed rate, and depth of cut on the surface roughness (Ra—[µm]). The ANOVA analysis identified the depth of cut parameter as the most significant factor affecting roughness, while the spindle speed and feed rate had a lesser impact. Optimal milling parameters for the lowest roughness (Ra = 1.3 µm) were found to be spindle speed: 12,000 RPM, feed rate: 1600 mm/min, and depth of cut: 0.5 mm. Surface roughness ranged from 1.3 to 2.5 µm, with lower values observed under optimized machining conditions. After the post-processing (buffing and polishing) necessary to achieve a better surface quality for composite lamination, the surface exhibited a measured roughness of 0.235 µm.
The mold exhibited 62.07 HRM hardness at 80 °C, indicating a good surface quality suitable for mold applications with the prepreg CFRP materials.
A CFRP part was manufactured using the Corian mold, using HexPly M35-4 prepreg with a layup of [+45/−45]. In the next step a visual surface analysis was conducted using a microscope. The analysis confirmed the quality of the CFRP laminated part and thus the suitability of the tool, proving Corian to be a feasible mold material for composite fabrication, offering good machinability, surface quality, and durability.

Author Contributions

Conceptualization, M.P.; methodology, M.P. and P.B.; software, M.P.; validation, M.P., P.B. and M.C.; formal analysis, M.P.; investigation, M.P.; resources, M.P.; data curation, M.P., P.B. and M.C.; writing—original draft preparation, M.P.; writing—review and editing, P.B. and M.C.; visualization, M.P.; supervision, M.P. and P.B.; project administration, M.P.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chardon, G.; Chanal, H.; Duc, E.; Garnier, T. Study of surface finish of fiber-reinforced composite molds. Proc. Inst. Mech. Eng. Part B 2015, 231, 576–587. [Google Scholar] [CrossRef]
  2. Fu, Y.; Yao, X. A review on manufacturing defects and their detection of fiber reinforced resin matrix composites. Compos. Part C Open Access 2022, 8, 100276. [Google Scholar] [CrossRef]
  3. Erik, K.; Daniel, S.; Christian, H. Process distortions in prepreg manufacturing—An experimental study on CFRP L-profiles. Compos. Struct. 2013, 106, 615–625. [Google Scholar] [CrossRef]
  4. Rodriguez-Garcia, V.; Gomez, J.; Cristiano, F.; Gude, M.R. Industrial Manufacturing and Characterization of Multiscale CFRP Laminates Made from Prepregs Containing Graphene-Related Materials. Materials 2020, 13, 3303. [Google Scholar] [CrossRef]
  5. Baran, I.; Cinar, K.; Ersoy, N.; Akkerman, R.; Hattel, J.H. A review on the mechanical modeling of composite manufacturing processes. Arch. Computat. Methods Eng. 2017, 24, 365–395. [Google Scholar] [CrossRef]
  6. van-de-Werken, N.; Tekinalp, H.; Khanbolouki, P.; Ozcan, S.; Williams, A.; Tehrani, M. Additively manufactured carbon fiber-reinforced composites: State of the art and perspective. Addit. Manuf. 2020, 31, 100962. [Google Scholar] [CrossRef]
  7. Li, X.; Shonkwiler, S.; McMains, S. Detection of resin-rich areas for statistical analysis of fiber-reinforced polymer composites. Compos. Part B Eng. 2021, 225, 109252. [Google Scholar] [CrossRef]
  8. Al-Shawk, A.; Tanabi, H.; Sabuncuoglu, B. Investigation of stress distributions in the resin rich region and failure behavior in glass fiber composites with microvascular channels under tensile loading. Compos. Struct. 2018, 192, 101–114. [Google Scholar] [CrossRef]
  9. Huang, F.; Pang, X.; Zhu, F.; Zhang, S.; Fan, Z.; Chen, X. Transverse mechanical properties of unidirectional FRP including resin-rich areas. Comput. Mater. Sci. 2021, 198, 110701. [Google Scholar] [CrossRef]
  10. Shabanijafroudi, N.; Ganesan, R. A new methodology for buckling, postbuckling and delamination growth behavior of composite laminates with delamination. Compos. Struct. 2021, 268, 113951. [Google Scholar] [CrossRef]
  11. Chukov, D.; Nematulloev, S.; Torokhov, V.; Stepashkin, A.; Sherif, G.; Tcherdyntsev, V. Effect of carbon fiber surface modification on their interfacial interaction with polysulfone. Results Phys. 2019, 15, 102634. [Google Scholar] [CrossRef]
  12. Léonard, F.; Stein, J.; Soutis, C.; Withers, P. The quantification of impact damage distribution in composite laminates by analysis of X-ray computed tomograms. Compos. Sci. Technol. 2017, 152, 139–148. [Google Scholar] [CrossRef]
  13. Smith, J.; Johnson, R.; Williams, T. Optimizing machining parameters for polymer composite materials to enhance surface integrity. J. Compos. Mater. 2018, 52, 1011–1024. [Google Scholar]
  14. Zhang, Y.; Li, X. The impact of process parameters on tool wear and surface finish in thermoplastic composites. Mater. Process. Technol. 2020, 276, 105–113. [Google Scholar]
  15. Nasir, M.; Wang, Z.; Kumar, P. Investigation of machining strategies for polymer matrix composites. J. Manuf. Sci. Eng. 2015, 137, 031004. [Google Scholar]
  16. Lee, S.; Choi, J.; Park, H. Effect of cutting parameters on surface quality in milling of polymer composites: An experimental study. Compos. Part B Eng. 2019, 162, 80–89. [Google Scholar]
  17. Zhang, J.; Li, H.; Wang, Y. Artificial intelligence-assisted optimization of CNC milling parameters for high-efficiency machining of polymer composites. J. Manuf. Process. 2020, 56, 545–552. [Google Scholar]
  18. Patel, D.; Shah, R.; Desai, S. Advancements in additive manufacturing for composite mold production. Addit. Manuf. 2021, 36, 101532. [Google Scholar]
  19. Anderson, P. Advanced Hardness Testing Techniques. Mater. Sci. J. 2018, 35, 89–102. [Google Scholar]
  20. Brown, D.; Miller, T. Comparison of Aluminum and Polymeric Mold Surfaces in Composite Manufacturing. Compos. Res. 2017, 12, 78–95. [Google Scholar]
  21. Chen, Y.; Patel, K. Effects of Machining Parameters on Polymeric Molds for CFRP Laminates. J. Adv. Manuf. 2020, 28, 140–158. [Google Scholar]
  22. Ahmad, R.; Shahar, N.; Ghazali, M.J. Surface roughness and tool wear evaluation in high-speed CNC milling of glass fiber reinforced polymer composites. Procedia Manuf. 2020, 51, 379–386. [Google Scholar] [CrossRef]
  23. DuPont Corian Documentation. Available online: https://www.corian.com/-documentation (accessed on 10 February 2025).
  24. Roy, R.K. A Primer on the Taguchi Method, 2nd ed.; Society of Manufacturing Engineers (SME): Southfield, MI, USA, 2010. [Google Scholar]
  25. Zitoune, R.; El Mansori, M.; Krishnaraj, V. Tribological analysis of drilling CFRP/Aluminum stacks. Wear 2016, 376–377, 302–312. [Google Scholar]
  26. Wang, D.; Chou, T.W. Processing and properties of ceramic composite materials. Compos. Part A Appl. Sci. Manuf. 2018, 109, 170–182. [Google Scholar] [CrossRef]
  27. Stokes-Griffin, C.M.; Compston, P. The effect of processing temperature and pressure on the consolidation of PEEK/carbon fiber laminates. Compos. Part A Appl. Sci. Manuf. 2018, 113, 107–114. [Google Scholar] [CrossRef]
  28. Jackson, T.; Mahon, J. Machining performance of acrylic solid surfaces for mold manufacturing. J. Mater. Process. Technol. 2019, 269, 66–73. [Google Scholar]
  29. Johnson, T.; Lee, R.; Kim, P. Machining and application of Corian for mold production. Compos. Sci. Technol. 2019, 184, 107756. [Google Scholar] [CrossRef]
  30. Brown, R.; Martens, M.; Fischer, A. Effect of mold material thermal conductivity on the performance of CFRP composites during curing. J. Thermoplast. Compos. Mater. 2020, 33, 210–225. [Google Scholar]
  31. Smith, J.; Adams, R. Surface Roughness Analysis in Composite Tooling. Compos. Mater. J. 2016, 33, 44–61. [Google Scholar]
  32. Taguchi, G. System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs; UNIPUB/Kraus International: White Plains, NY, USA, 1987. [Google Scholar]
  33. Huang, H.; Zhang, M.; Lee, J. CNC Milling of Composite Materials: Surface Quality and Tool Wear. Int. J. Adv. Manuf. Technol. 2016, 87, 123–135. [Google Scholar]
  34. Jones, P.; Lee, S. Surface Characterization of Mold Materials for Composite Tooling. Compos. Sci. Technol. 2019, 172, 85–99. [Google Scholar]
  35. Kumar, S.; Zhang, L.; Liu, Y. Microstructural Analysis of Composite Tooling Materials. J. Manuf. Sci. Eng. 2019, 141, 091014. [Google Scholar]
  36. Lee, M.; Kim, H.; Lee, J. Measuring Shore D Hardness of Composites for Mold Quality Assessment. J. Compos. Mater. 2020, 54, 112–123. [Google Scholar]
  37. Mason, L.; Patel, K. The Role of Prepreg CFRP in Aerospace and Automotive Applications. Compos. Technol. Rev. 2017, 25, 62–77. [Google Scholar]
  38. Prepreg Material Datasheet. Available online: https://www.imatec.it/wp-content/uploads/2016/05/M35_4_eu.pdf (accessed on 12 February 2025).
  39. Miller, P.; Johnson, T. Surface Roughness Testing and Analysis: A Practical Approach. J. Surf. Eng. 2015, 41, 12–21. [Google Scholar]
  40. Montgomery, D.C. Design and Analysis of Experiments, 8th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2012. [Google Scholar]
  41. Ross, P.J. Taguchi Techniques for Quality Engineering; McGraw-Hill: New York, NY, USA, 1988. [Google Scholar]
  42. Phadke, M.S. Quality Engineering Using Robust Design; Prentice Hall: Englewood Cliffs, NJ, USA, 1989. [Google Scholar]
  43. ASTM D785-08; Standard Test Method for Rockwell Hardness of Plastics and Electrical Insulating Materials. ASTM: West Conshohocken, PA, USA, 2015.
  44. Antony, J. A systematic methodology for the design of experiments. Work Study 2003, 52, 24–28. [Google Scholar]
  45. Hasdiansah, H.; Yaqin, R.I.; Pristiansyah, P.; Umar, M.L.; Priyambodo, B.H. FDM 3D Printing Parameter Optimization Using Taguchi Approach on Surface Roughness of Thermoplastic Polyurethane (TPU) Parts. Int. J. Interact. Des. Manuf. (IJIDeM) 2023, 17, 829–842. [Google Scholar] [CrossRef]
  46. Dettmer, T.; Taboada, H.A. Application of Taguchi method for multi-objective optimization in thermoplastic welding. J. Manuf. Process. 2021, 62, 668–677. [Google Scholar] [CrossRef]
  47. Patel, R. Vacuum Bagging and Prepreg Lamination in Composite Manufacturing. Adv. Compos. Manuf. J. 2019, 5, 34–47. [Google Scholar]
  48. Tepper, L. Advanced Composite Materials: Prepregs and Curing Techniques. Compos. Manuf. Technol. 2020, 12, 89–101. [Google Scholar]
  49. Byrne, D.M.; Taguchi, S. The Taguchi approach to parameter design. Qual. Prog. 1987, 20, 19–26. [Google Scholar]
  50. Patel, D.H.; Patni, V.N. An investigation effect of machining parameters on CNC router. Int. J. Eng. Dev. Res. (IJEDR) 2014, 2, 1497–1503. [Google Scholar]
  51. DuPont Corian Technical Bulletin. Available online: https://casf.com.au/wp-content/uploads/2022/01/Performance_Properties_of_Corian.pdf (accessed on 21 February 2025).
  52. Bogue, A.J.; Brown, A.J.; Doughty, C.J.; Dunn, C.J. Manufacturing of a Carbon Fiber Part with Complex Geometry; Senior Project; California Polytechnic State University: San Luis Obispo, CA, USA, 2012; Available online: https://digitalcommons.calpoly.edu/mesp/414 (accessed on 21 February 2025).
  53. Garcia, L.; Roberts, M. Optimization of CNC Milling for Non-Metallic Mold Materials. Manuf. Sci. Eng. 2017, 20, 213–229. [Google Scholar]
Figure 1. Effect of spindle speed on S/N ratio.
Figure 1. Effect of spindle speed on S/N ratio.
Jmmp 09 00195 g001
Figure 2. Effect of feed rate on S/N ratio.
Figure 2. Effect of feed rate on S/N ratio.
Jmmp 09 00195 g002
Figure 3. Effect of depth of cut on S/N ratio.
Figure 3. Effect of depth of cut on S/N ratio.
Jmmp 09 00195 g003
Figure 4. Mechanical drawing of the mold with gross dimensions.
Figure 4. Mechanical drawing of the mold with gross dimensions.
Jmmp 09 00195 g004
Figure 5. Isometric CAD view of the mold.
Figure 5. Isometric CAD view of the mold.
Jmmp 09 00195 g005
Figure 6. Surface roughness measurements of the milled mold.
Figure 6. Surface roughness measurements of the milled mold.
Jmmp 09 00195 g006
Figure 7. The finished molding surfaces, following the buffing and polishing process.
Figure 7. The finished molding surfaces, following the buffing and polishing process.
Jmmp 09 00195 g007
Figure 8. Final CFRP part gross dimensions.
Figure 8. Final CFRP part gross dimensions.
Jmmp 09 00195 g008
Figure 9. Curing temperature vs. time.
Figure 9. Curing temperature vs. time.
Jmmp 09 00195 g009
Figure 10. Demolded CFRP part for visual inspection. (a) Tip inspection; (b) Root inspection.
Figure 10. Demolded CFRP part for visual inspection. (a) Tip inspection; (b) Root inspection.
Jmmp 09 00195 g010
Figure 11. Microscope inspection in the middle area of the laminated CFRP part.
Figure 11. Microscope inspection in the middle area of the laminated CFRP part.
Jmmp 09 00195 g011
Figure 12. Microscope inspection on the tip of the laminated CFRP part.
Figure 12. Microscope inspection on the tip of the laminated CFRP part.
Jmmp 09 00195 g012
Figure 13. Round edge inspection.
Figure 13. Round edge inspection.
Jmmp 09 00195 g013
Table 1. Adopted machining parameters.
Table 1. Adopted machining parameters.
FactorLevel 1Level 2Level 3
Spindle speed (RPM)4000800012,000
Feed rate (mm/min)80016002500
Depth of cut (mm)0.50.751.5
ToolHSSHSSHSS
Table 2. Measured surface roughness and S/N values for every milling case.
Table 2. Measured surface roughness and S/N values for every milling case.
ExperimentSpindle Speed [RPM]Feed Rate [mm/min]Depth of cut [mm]Surface Roughness [µm]S/N Ratio
140008000.52.1−6.44438
2400016000.751.8−5.10545
3400020001.52.4−7.60422
480008000.51.7−4.60897
5800016000.752.5−7.95880
6800020001.51.6−4.08239
712,0008000.52.2−6.84845
812,00016000.751.3−2.27886
912,00020001.51.9−5.57507
Table 3. ANOVA conclusions.
Table 3. ANOVA conclusions.
Factorp ValueF ValueImpact on Surface Roughness
Depth of cut0.2163.62High impact
Spindle speed0.6280.59Low impact
Feed rate0.8870.12Negligible
Table 4. Control milling parameters in comparison with optimized parameters.
Table 4. Control milling parameters in comparison with optimized parameters.
ParameterControl Group SettingsOptimized Settings
Depth of cut [mm]1.50.5
Spindle speed [RPM]10,00012,000
Feed rate [mm/min]20001600
Roughness [µm]2.91.3
Table 5. Measured roughness for the raw and finished surface.
Table 5. Measured roughness for the raw and finished surface.
Tool Surface StateMeasured Roughness [µm]
Raw milled1.632
Buffed and polished0.235
Table 6. Hardness tests results for several thermal regimes.
Table 6. Hardness tests results for several thermal regimes.
Surface Temperature [°C]Surface Measured Hardness [HRM]
2097.31
5064.46
8062.07
12040.66
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Părpăriță, M.; Bere, P.; Cioază, M. A Novel Method for Manufacturing Molds for CFRP Prepreg Lamination Using Polymeric Acrylic Resin–Aluminum Trihydrate. J. Manuf. Mater. Process. 2025, 9, 195. https://doi.org/10.3390/jmmp9060195

AMA Style

Părpăriță M, Bere P, Cioază M. A Novel Method for Manufacturing Molds for CFRP Prepreg Lamination Using Polymeric Acrylic Resin–Aluminum Trihydrate. Journal of Manufacturing and Materials Processing. 2025; 9(6):195. https://doi.org/10.3390/jmmp9060195

Chicago/Turabian Style

Părpăriță, Mihai, Paul Bere, and Mircea Cioază. 2025. "A Novel Method for Manufacturing Molds for CFRP Prepreg Lamination Using Polymeric Acrylic Resin–Aluminum Trihydrate" Journal of Manufacturing and Materials Processing 9, no. 6: 195. https://doi.org/10.3390/jmmp9060195

APA Style

Părpăriță, M., Bere, P., & Cioază, M. (2025). A Novel Method for Manufacturing Molds for CFRP Prepreg Lamination Using Polymeric Acrylic Resin–Aluminum Trihydrate. Journal of Manufacturing and Materials Processing, 9(6), 195. https://doi.org/10.3390/jmmp9060195

Article Metrics

Back to TopTop