Search results

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

This study aims to present the optimization of parameters and effects of annealing and vapor smoothing post-processing treatments on the surface roughness and tensile mechanical properties of fused deposition modeling (FDM) printed acrylonitrile butadiene styrene (ABS).

Full-factorial test matrices were designed to determine the most effective treatment parameters for post-processing. The parameters for annealing were temperature and time, whereas the parameters for the vapor smoothing were volume of acetone and time. Analysis of surface roughness and tensile test results determined influences of the levels of parameters to find an ideal balance between mechanical properties and roughness.

Optimal parameters for vapor smoothing and annealing were determined. Vapor smoothing resulted in significantly higher improvements to surface roughness than annealing. Both treatments generally resulted in decreased mechanical properties. Of all treatments tested, annealing at 100 °C for 60 min provided the greatest benefit to tensile properties and vapor smoothing with 20 mL of acetone for 15 min provided the greatest benefit to surface roughness while balancing effects on properties.

Vapor smoothing and annealing of FDM ABS have typically been studied independently for their effects on surface roughness and material properties, respectively, with varying materials and manufacturing methods. This study objectively compares the effects of each treatment on both characteristics simultaneously to recommend ideal treatments for maximizing the balance between the final quality and performance of FDM components. The significance of the input variables for each treatment have also been analyzed. These findings should provide value to end-users of 3D printed components seeking to balance these critical aspects of manufacturing.

Different technologies may currently be used to produce dental prostheses, such as additive manufacturing and traditional milling. This study aims to evaluate and improve the fabrication process for hot-pressed porcelain dental prostheses and compare the use of masked stereolithography apparatus (MSLA) casting to computer-aided design/computer-aided manufacturing (CAD/CAM) casting. The cost-benefit analysis of producing dental prostheses through various technologies, including additive manufacturing and traditional milling, has not been fully explored. The cost of materials and processes used to produce prostheses varies based on complexity of design and materials used, and long-term effects, such as durability and wear and tear, must be taken into account.

Using key elements of part costs and estimation cost models, a multivariable approach was used to evaluate the practicality of the recommended strategy and process improvement.

The research found that MSLA casting provides a higher return on investment than CAD/CAM casting, and the optimized production process could be more suitable for the size and annual demand for prostheses.

Overall, this study highlights the need for a more comprehensive understanding of the cost-benefit analysis of different dental prosthesis production methods and emphasises the importance of evaluating long-term effects on the cost-benefit analysis.

This paper investigates how the adoption of additive manufacturing (AM) impacts upstream supply chain (SC) design and considers the influence of drivers and barriers towards the adoption.

Ten case studies investigating AM adoption by Original Equipment Manufacturers (OEMs) in five industries were conducted. This research is driven by a literature-based framework, and the results are discussed according to the theory of transaction cost economics (TCE).

The case studies reveal four patterns of AM adoption that affect upstream SC design (due to changes in supply base or types of buyer–supplier relationships): make, buy, make and buy and vertical integration. A make or buy decision is based on the level of experience with the technology, on the AM application (rapid manufacturing, prototyping or tooling) and on the need of control over production. Other barriers playing a role in the decision are the high initial investments and the lack of skills and knowledge.

This paper shows how different decisions regarding AM adoption result in different SC designs, with a specific focus on the upstream SC and changes in the supply base. This research is among the first to provide empirical evidence on the impact of AM adoption on upstream SCs and to identify drivers of the make or buy decision when adopting AM through the theoretical lens of TCE.

This study aims to cover the overall gamut of rapid prototyping processes and biomaterials used for the fabrication of occlusal splints in a comprehensive manner and elucidate the characteristics of the materials, which are essential in determining their clinical efficacy when exposed to oral surroundings.

A collective analysis of published articles covering the use of rapid prototyping technologies in the fabrication of occlusal splints, including manufacturing workflow description and essential properties (mechanical- and thermal-based) evaluation of biocompatible splinting materials, was performed.

Without advances in rapid prototyping processes and materials engineering, occlusal splints would tend to underperform clinically due to biomechanical limitations.

Three-dimensional printing can improve the process capabilities for commercial customization of biomechanically efficient occlusal splints.

Rapid technological advancement in dentistry with the extensive utilization of rapid prototyping processes, intra-oral scanners and novel biomaterial seems to be the potential breakthrough in the fabrication of customized occlusal splints which have endorsed occlusal splint therapy (OST) as a cornerstone of orthodontic treatment.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

The advent of artificial intelligence (AI) in the accounting landscape marks a significant shift, promising gains in efficiency and accuracy but also eliciting concerns about job displacement (JD) and broader socio-economic implications. This study aims to provide an in-depth understanding of how AI’s integration in accounting contributes to JD, reshapes decision-making processes and reverberates across economic and social dimensions. It also offers evidence-based policy recommendations to mitigate adverse outcomes.

Leveraging a cross-sectional survey disseminated through Facebook, this research used snowball sampling to target a diverse cohort of accounting professionals. The collected data were subjected to meticulous analysis through descriptive and regression models, facilitated by SmartPLS 4 software.

The analysis revealed a significant correlation between AI’s increasing role in accounting and a heightened rate of JD. This study found that this displacement is not isolated; it has tangible repercussions on decision-making paradigms, economic well-being, professional work dynamics and social structures. These insights corroborate existing frameworks, including, but not limited to, theories of technological unemployment and behavioural adjustments.

Although providing valuable insights, this study acknowledges limitations such as the restricted sample size, the cross-sectional nature of the survey and the inherent biases of self-reported data. Future research could aim to extend these initial findings by adopting a longitudinal approach and potentially integrating external data sources.

As AI technology becomes increasingly ingrained in accounting practices, there is an urgent need for coordinated action among stakeholders. Policy recommendations include focused efforts on talent retention, investment in upskilling programs and the establishment of support mechanisms for those adversely affected by AI adoption.

By synthesising a range of theoretical perspectives, this study offers a comprehensive exploration of AI’s multi-dimensional impacts on the accounting profession. It stands out for its nuanced examination of JD and its economic and social implications, thereby contributing to both academic discourse and policy formulation. This work serves as an urgent call to action, highlighting the need for strategies that both exploit AI’s potential benefits and protect the workforce from its disruptive impact.

Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

The purpose of this study to investigate the organized porous network zinc (OPNZ) scaffolds. Their mechanical characteristics, surface roughness and fracture mechanism were assessed in relation to their structural properties. The prospects of fused deposition modeling (FDM) for printing metal scaffolds via rapid tooling have also been studied.

Zn scaffolds with different pore and strut sizes were manufactured via the rapid tooling method. This method is a multistep process that begins with the 3D printing of a polymer template. Later, a paraffin template was obtained from the prepared polymer template. Finally, this paraffin template was used to fabricate the Zn scaffold using microwave sintering. The characterization of prepared Zn samples involved structural characterization, microstructural study, surface roughness testing and compression testing. Moreover, based on the Gibson–Ashby model analysis, the model equations’ constant values were evaluated, which can help in predicting the mechanical properties of Zn scaffolds.

The scanning electron microscopy study confirmed that the fabricated sample pores were open and interconnected. The X-ray diffraction analysis revealed that the Zn scaffold contained hexagonal closed-packed Zn peaks related to the a-Zn phase, validating that scaffolds were free from contamination and impurity. The range for ultimate compressive strength, compressive modulus and plateau stresses for Zn samples were found to be 6.75–39 MPa, 0.14–3.51 GPa and 1.85–12.6 MPa by adjusting their porosity, which are comparable with the cancellous bones. The average roughness value for the Zn scaffolds was found to be 1.86 µm.

This research work can widen the scope for extrusion-based FDM printers for fabricating biocompatible and biodegradable metal Zn scaffolds. This study also revealed the effects of scaffold structural properties like porosity, pore and strut size effect on their mechanical characteristics in view of tissue engineering applications.

The purpose of this paper is to define the local integration rate and how it is calculated to assess its relevance as a national performance indicator for the Moroccan automotive industry.

The research methodology first followed a systematic review approach through the analysis of published research articles and academic works. This study then followed a qualitative approach based on semi-structured interviews with various actors in the Moroccan automotive industry. Finally, the findings of this work were reinforced by a case study to analyze the supply chain of a locally produced vehicle.

The results indicate that the local integration rate as calculated today overestimates the performance of the automotive industry and does not systematically guarantee a significant creation of value added.

Due to the confidentiality of the data in terms of turnover, payroll and purchase prices as well as the large number of suppliers in the different supply chains of the car manufacturer, the case study focused on only one of the six existing ecosystems.

On the basis of research work on the Moroccan automotive industry as well as interviews with various actors, the local integration rate is unanimously considered as a viable performance indicator. This study has not only led us to the method of calculating this rate by the Ministry of Industry but also demonstrated its limitations while proposing a new method of calculation to increase the value added.