Search Results (25)

Electron beam melting (EBM) technology has gained prominence owing to its ability to enhance production efficiency and meet green manufacturing standards. However, overhang structures are a significant issue for additive manufacturing due to their need for supporting structures during printing. This increases manufacturing time, requiring more material, extra effort, and a more complex engineering procedure. Therefore, this research aims to develop an intelligent optimization method based on AI-ANFIS/Al-ANN and improved NSGA-III, integrating the AM design, 3D printing, and post-processing phases to enhance the performance of block support structures and the quality of the EBM parts produced. To achieve this, statistical analysis was performed to detail the simultaneous influence of block support type, block support structure design, and EBM parameters on fabricating performance, warping deformation, support removal time, and support volume. After that, intelligent models based on ANFIS/ANN and the advanced NSGA-III method were developed for monitoring and optimizing the performance of specified block support structures. The results reveal that the block support type, block support structure design, and EBM parameters simultaneously significantly affect block support structures’ performance. This study illustrated that the AI models based on ANFIS might provide more accurate and reliable estimation models for monitoring and predicting support volume, support removal time, and warping deformation, exhibiting reduced errors of 0.992%, 1.2%, 1.28%, and 1.06%, respectively, in comparison to empirical measurements, ANN models, and regression models. Finally, the developed intelligent method obtains the optimal block support type, block support design, and EBM parameters to enhance the quality of produced parts, reduce material wastage, and reduce the post-processing time of fabricated EBM Ti6Al4V. Henceforth, smart systems may be employed to create innovative solutions that integrate the AM design, 3D printing, and post-processing stages. This will allow for the monitoring and improvement of AM process performance, as well as the fulfillment of Industry 4.0 requirements. Full article

The evaluation of manufacturing processes aims to ensure that the processes meet the desired requirements. Therefore, process capability indexes are used to measure the capability of a process to meet customer requirements and/or engineering specifications. However, most of the manufacturing products have more than one quality characteristic (QC), in which case, the multivariate QCs should be evaluated together using a single capability index. The research in this article proposes a methodology for estimating the multivariate process capability index (PCI). First, the dimensions of the multivariate QCs are reduced into a new single variable using the proportion of the process specification region, by comparing each variable datapoint to its specification limits. Moreover, nonnormal data are transformed to normality using a root transformation algorithm. Then, a large data sample is generated using the parameters of the new variable. The generated data are compared to the specification limits to estimate the percent of nonconforming (PNC). Finally, the capability index of a given process datapoints is estimated using the PNC. Accordingly, managerial insights for the implementation of the proposed methodology in real industry are presented. The methodology was assessed by well-known multivariate samples from four different distributions, in which an algorithm was developed for generating these samples with their given correlations. The results show the effectiveness of the proposed methodology for estimating multivariate PCIs. Also, the results from this research outperform the previous published results in most cases. Full article

Advances in additive manufacturing (AM) processes have increased the number of relevant applications in various industries. To keep up with this development, the process stability of AM processes should be monitored, which is conducted through the assessment of the outputs or product characteristics. However, the use of univariate control charts to monitor an AM process might lead to misleading results, as most additively manufactured products have more than one correlated quality characteristic (QC). This paper proposes a framework for monitoring the multivariate quality characteristics of AM processes, and the proposed framework was applied to monitor a fused filament fabrication (FFF) process. In particular, specimens were designed and produced using the FFF process, and their QCs were identified. Then, critical quality characteristic data were collected using a precise measurement system. Furthermore, we propose a transformation algorithm to ensure the normality of the collected data. After examining the correlations between the investigated quality characteristics, a multivariate exponential weighted moving average (MEWMA) control chart was used to monitor the stability of the process. Furthermore, the MEWMA parameters were optimized using a novel heuristic technique. The results indicate that the majority of the collected data are not normally distributed. Consequently, the efficacy of the proposed transformation technique is demonstrated. In addition, our findings illustrate the correlations between the QCs. It is worth noting that the MEWMA optimization results confirm that the considered AM process (i.e., FFF) is relatively stable. Full article

This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations. Full article

I4.0 promotes a future in which highly individualized goods are mass produced at a competitive price through autonomous, responsive manufacturing. In order to attain market competitiveness, organizations require proper integration of I4.0 technologies and manufacturing strategy outputs (MSOs). Implementing such a comprehensive integration relies on carefully selecting I4.0 technologies to meet industrial requirements. There is little clarity on the impact of I4.0 technologies on MSOs, and the literature provides little attention to this topic. This research investigates the influence of I4.0 technologies on MSOs by combining reliable MCDM methods. This research uses a combination of fuzzy DEMATEL and fuzzy TOPSIS to evaluate the impact of I4.0 technologies on MSOs. The fuzzy theory is implemented in DEMATEL and TOPSIS to deal with the uncertainty and vagueness of human judgment. The FDEMATEL was utilized to identify interrelationships and determine criterion a’s weights, while the fuzzy TOPSIS approach was employed to rank the I4.0 technologies. According to the study’s findings, cost is the most critical factor determining MSOs’ market competitiveness, followed by flexibility and performance. On the other hand, additive manufacturing (AM) is the best I4.0 technology for competing in the global market. The results present an evaluation model for analyzing the relative important weight of multiple factors on MSOs. They can also assist managers in concentrating on the most influential factors and selecting the proper I4.0 Technology to preserve competitiveness. Full article

The manufacturing industry has grown increasingly computerized and complex. Such changes are brought about mainly by adopting Industry 4.0 (I4) technologies. I4.0 promises a future of mass-producing highly individualized goods via responsive, autonomous, and cost-effective manufacturing operations. Adopting I4.0 technologies significantly improves a company’s productivity, efficiency, effectiveness, innovation, sustainable management, and sustainability. As is well known, implementing I4.0 technologies results in smart and sustainable manufacturing outputs. Despite their significance, I4.0 technologies have received less attention in the literature, and their influence on MSOs is unknown. This study analyzes the factors influencing manufacturing strategy outputs (MSOs), adopting I4.0 technologies using the fuzzy DEMATEL method. This research utilizes the fuzzy DEMATEL method to address the vagueness and uncertainties inherent in human judgments. Furthermore, this method is utilized to determine the cause-and-effect relationship and analyze the interdependence of factors. It explores the interrelationships among MSO factors from the perspectives of academic and industry experts. Identifying cause-and-effect aspects boosts the market’s competitiveness and prioritizes them. The results demonstrated that cost, quality, and performance are the most influential factors on MSOs. Full article

Manufacturing is transitioning from traditional and mass manufacturing to mass personalization, fast, and intelligent production. Through full automation in various fields and data sharing, Industry 4.0 (I4.0) contributes to the digitalization of manufacturing by enhancing industrial flexibility and product customization. I4.0 is being utilized as a strategy for advanced manufacturing to counter global competitiveness. A company’s manufacturing strategy outputs (MSOs) are critical to its ability to move forward and remain competitive. Despite their importance, I4.0 technologies have received less attention in the literature, and it is unclear how they influence MSOs. Thus, this study aims to build a powerful hybrid MCDM method for ranking the influence of I4.0 technologies on MSOs by adopting a combination of AHP and fuzzy TOPSIS. The application of fuzzy set theory has addressed the ambiguity in comparing various I4.0 technologies. The AHP was used to calculate the weights of criteria and sub-criteria, and the fuzzy-TOPSIS method was utilized to rank the I4.0 technologies. The results revealed that the cost criterion is the most critical factor when implementing I4.0 technologies. In contrast, additive manufacturing (AM) is the most suitable I4.0 technology for countering global competition. Full article

Driven from real-life applications, this work aims to cope with the scheduling problem of automotive glass manufacturing systems, that is characterized as a two-stage flow-shop with small batches, inevitable setup time for different product changeover at the first stage, and un-interruption requirement at the second stage. To the best knowledge of the authors, there is no report on this topic from other research groups. Our previous study presents a method to assign all batches to each machine at the first stage only without sequencing the assigned batches, resulting in an incomplete schedule. To cope with this problem, if a mathematical programming method is directly applied to minimize the makespan of the production process, binary variables should be introduced to describe the processing sequence of all the products, not only the batches, resulting in huge number of binary variables for the model. Thus, it is necessary and challenging to search for a method to solve the problem efficiently. Due to the mandatory requirement that the second stage should keep working continuously without interruption, solution feasibility is essential. Therefore, the key to solve the addressed problem is how to guarantee the solution feasibility. To do so, we present a method to determine the minimal size of each batch such that the second stage can continuously work without interruption if the sizes of all batches are same. Then, the conditions under which a feasible schedule exists are derived. Based on the conditions, we are able to develop a two-step solution method. At the first step, an integer linear program (ILP) is formulated for handling the batch allocation problem at the first stage. By the ILP, we need then to distinguish the batches only, greatly reducing the number of variables and constraints. Then, the batches assigned to each machine at the first stage are optimally sequenced at the second step by an algorithm with polynomial complexity. In this way, by the proposed method, the computational complexity is greatly reduced in comparison with the problem formulation without the established feasibility conditions. To validate the proposed approach, we carry out extensive experiments on a real case from an automotive glass manufacturer. We run ILP on CPLEX for testing. For large-size problems, we set 3600 s as the longest time for getting a solution and a gap of 1% for the lower bound of solutions. The results show that CPLEX can solve 96.83% cases. Moreover, we can obtain good solutions with the maximum gap of 4.9416% for the unsolved cases. Full article

Triply periodic minimum surface (TPMS)-based lattice structures have gained interest for their outstanding capacity to absorb energy, their high load-bearing capacity, and their high surface-to-volume ratio. This study considered three TPMS cell topologies, including Diamond, Gyroid, and Primitive. The FDM process was used to print the lattice structures with two materials: pure polylactic acid (PLA) and carbon fiber-reinforced PLA (PLA + CF). The influence of carbon fiber (CF) incorporation, unit cell type (topologies) and size, and relative density (RD) on mechanical properties and failure patterns were explored comprehensively under uniaxial compression testing. The results demonstrate a change in the compressive modulus (0.09 to 0.47 GPa), compressive strength (2.98 to 13.89 MPa), and specific energy absorption (SEA) (0.14 MJ/m³/g to 0.58 MJ/m³/g) due to the influence of CF incorporation, cell type and size, and RD. Results indicate that the Diamond structure outperformed both Primitive and Gyroid structures in terms of compressive modulus and strength, and SEA. All the CF-based TPMS structures showed a higher compressive modulus. Compressive strength and energy absorption capacity were both slightly enhanced in most PLA + CF-based Diamond structures. On the contrary, Gyroid and Primitive structures showed better performance for pure PLA-based structures in terms of compression strength and specific absorption energy. Full article

Globalization has created a highly competitive and diverse market, an uncertain and risky business environment, and changing customer expectations. An effective manufacturing strategy reduces complexity and provides organizations with a well-organized manufacturing structure. However, existing research on manufacturing strategies appears scattered, lacking systematic understanding and finding no causal relationship between manufacturing strategies’ outputs (MSOs) and their importance. Therefore, this study is a pioneer in identifying the influential factors of MSOs in the adoption of Industry 4.0 (I4.0) technologies utilizing the decision-making trial and evaluation laboratory (DEMATEL) approach. This method is considered an effective method for identifying the cause-effect relationship of complex problems. It evaluates interdependent relationships among MSO factors from the perspective of academic and industry experts. Identifying cause and effect factors leads to increasing the market’s competitiveness and prioritizing them. To deal with the vagueness of human beings’ perceptions, this study utilizes fuzzy set theory and the DEMATEL method to form a structural model. Results show that customer satisfaction, cost per unit produced, and the number of advanced features are the main factors influencing MSOs. Full article

It is possible to employ printed capacitive sensors in car bezel applications because of its lower cost and higher detecting capabilities. In this paper, a flexible sensor for automotive entertainment applications has been developed using an electrode flexible sensor with an interdigitated pattern printed on it using screen printing and 3D printing fabrication processes. Design concerns such as electrode overlap, electrode gap and width on capacitance changes, and production costs were studied. In addition, a new generation of flexible printed sensors has been developed that can outperform conventional human–machine interface (HMI) sensors. The capacitance of the design pattern may be optimized by using a 15mm overlap and 0.5mm electrode line width. Due to the precision of interpolation, overlap has a larger effect on sensor performance than it would have without it. Full article

Titanium alloy is widely used for orthodontic technology and easily machined using the EDM process. In the EDM process, the workpiece and tool electrode must be separated by a continuous air gap during the machining operation to generate discharge energy in this method. In the present study, an endeavor was made to analyze the effects of a servo feed air gap control and tool electrode in the EDM process. The developed mechanical setup consists of a linear action movement with zero backlash along the X-axis, which can be controlled up to 0.03 mm. It was observed that the suggested air gap control scheme can enhance the servo feed mechanism on a machining titanium alloy. A tungsten carbide electrode can enhance the surface measures owing to its ability to produce tiny craters with uniform distribution. Since it produces a little crater and has a higher melting point, a tungsten carbide electrode can create lesser surface roughness than a copper tool and brass tool electrode. Full article

The last few decades have seen rapid growth in additive manufacturing (AM) technologies. AM has implemented a novel method of production in design, manufacture, and delivery to end-users. Accordingly, AM technologies have given great flexibility in design for building complex components, highly customized products, effective waste minimization, high material variety, and sustainable products. This review paper addresses the evolution of engineering design to take advantage of the opportunities provided by AM and its applications. It discusses issues related to the design of cellular and support structures, build orientation, part consolidation and assembly, materials, part complexity, and product sustainability. Full article

Electron beam melting (EBM) is a relatively new process in three-dimensional (3D) printing to enable rapid manufacturing. EBM can manufacture metallic parts with thin walls, multi-layers, and complex internal structures that could not otherwise be produced for applications in aerospace, medicine, and other fields. A 3D transient coupled thermomechanical finite element (FE) model was built to simulate the temperature distribution, distortion, and residual stresses in electron beam additive manufactured Ti-6Al-4V parts. This research enhances the understanding of the EBM-based 3D printing process to achieve parts with lower levels of residual stress and distortion and hence improved quality. The model used a fine mesh in the layer deposition zone, and the mesh size was gradually increased with distance away from the deposits. Then, elements are activated layer by layer during deposition according to the desired material properties. On the top surface, a Gaussian distributed heat flux is used to model the heat source, and the temperature-dependent properties of the powder and solid are also included to improve accuracy. The current simulation has been validated by comparing the FE distortion and temperature results with the experimental results and other reported simulation studies. The residual stress results calculated by the FE analysis were also compared with the previously reported simulation studies on the EBM process. The results showed that the finite element approach can efficiently and accurately predict the temperature field of a part during the EBM process and can easily be extended to other powder bed fusion processes. Full article

Surface modification is given vital importance in the biomedical industry to cope with surface tissue growth problems. Conventionally, basic surface treatment methods are used which include physical and chemical deposition. The major drawbacks associated with these methods are excessive cost and poor adhesion of coating with implant material. To generate a bioactive surface on an implant, electric discharge machining (EDM) is a promising and emerging technology which simultaneously serves as machining and surface modification technique. Besides the surface topology, implant material plays a very important role in surgical applications. From various implant materials, titanium (Ti6Al4V ELI) alloy is the best choice for long-term hard body tissue replacement due to its superior engineering, excellent biocompatibility and antibacterial properties. In this research, EDM’s surface characteristics are explored using Si powder mixed in dielectric on Ti6Al4V ELI. The effect of powder concentration (5 g/L, 10 g/L and 20 g/L) along with pulse current and pulse on time is investigated on micro and nanoscale surface topography. Optimized process parameters having a 5 g/L powder concentration result in 2.76 μm surface roughness and 13.80 μm recast layer thickness. Furthermore, a nano-structured (50–200 nm) biocompatible surface is fabricated on the surface for better cell attachment and growth. A highly favourable carbon enriched surface is confirmed through EDS which increases adhesion and proliferation of human osteoblasts. Full article