Journal of Advanced Manufacturing Systems

The Johnson–Cook model is the popular constitutive relation for the simulation of metal cutting process because of the availability of various sets of parameters for different materials. Different sets of Johnson–Cook parameters are observed to be available for a particular material due to dissimilarity in the initial condition of the material and experimental methods utilized for the calibration. Hence, it is difficult to choose an accurate parameter set for modeling the behavior of a material for the finite element simulation of its cutting process. In this regard, a strategy is proposed and validated in this study for choosing an accurate Johnson–Cook parameter set for Inconel 718. Twelve sets of Johnson–Cook parameters were collected from the literature for Inconel 718 and their flow stress predictions were investigated by comparing with experimental flow stress values for wide ranges of strain rates and temperatures as encountered during the cutting process. The Johnson–Cook parameter sets corresponding to the predicted flow stress curves that agree with the experimental values are chosen for modeling the behavior of Inconel 718. This comparison also helps in indexing the parameter sets according to the initial condition of the material since it is not reported mostly in the literature. The chosen material parameter sets are validated further for the orthogonal cutting simulation of Inconel 718 for a wide range of cutting conditions by comparing the cutting force and chip thickness predictions with the experimental values. Thus, the analysis of predicted flow stress values helps in choosing the accurate Johnson–Cook parameters for an Inconel 718 specimen which in turn helps in conducting accurate orthogonal cutting simulations. The finite element simulation of metal cutting helps in identifying the optimum cutting parameters which would help to reduce energy consumption and thus make the process more efficient and sustainable.

Multivariate control charts are useful tools for monitoring the product quality in which the process outcome is expressed by several correlated variables. The multivariate homogeneously weighted moving average (MHWMA) control chart is one of the most efficient extensions of the multivariate exponentially weighted moving average (MEWMA) procedure. However, this control chart has been proposed under the assumption of precise measurements. This paper explores the impact of the measurement system inability on sensitivity of MHWMA control chart in Phase II monitoring of the mean vector of multivariate normally distributed quality characteristics. Through simulation studies, the run length (RL) properties of MHWMA chart in the presence of measurement errors are investigated and compared to without-error scenario. The results indicate that the imprecise measurements degrade the detection ability of MHWMA control chart in detecting different process disturbances. The undesired impact of gauge measurement errors increases as the error variance increases. Moreover, multiple measurements on each sampled item are utilized to compensate for the undesired effect of the measurement errors on run length characteristics of the MHWMA control chart. It is also concluded that, as the number of measurements on each sampled point increases, the detection capability of MHWMA chart reduces and approaches the without-error case. Finally, a realistic illustrative example from a healthcare system is utilized to elaborate on the impact of imprecise measurements on chart performance.

A combined model of a 2^k design of experiment (DOE) and goal programming (GP) approaches is presented to determine optimum levels of input variables and analyze their robustness for a multiobjective performance of a flexible manufacturing cell (FMC) in this study. Two main performance metrics, namely, manufacturing lead time (MLT) and surface roughness (SR), are considered performance outputs for the FMC. Machine sequence, robot speed, tool type, and material type are selected as the four input variables on the input side of the proposed model. The study shows that even with a limited number of experiments, one can determine optimum input levels for the multiobjective performance of the FMC and determine their robustness.

To ensure the smoothness of working flow on the shop floor, workers need excellent and healthy physical conditions. However, some people with disabilities, especially those who have lost their limbs, will have difficulty carrying out the production-related activities. These problems become the background of this paper to design a prosthetic hand made by 3D printing technique. Nine servo motor units actuate all the joints and Arduino is applied as a microcontroller. This paper mainly discusses the design of the kinematics model in the hand, electrical system, and basic movements produced by this prosthetic hand. In addition, patterns of gripping are analyzed and compared with the 3D design. The use of servo motors in each joint of the prosthetic hand can be an advantage to increase the variety of motion in it compared with the design using a pull-string force system. Finally, this paper proposes the idea of future development of the hand design, to achieve a more versatile motion of each finger.

With the arrival of the 5G era, centralized cloud computing is gradually shifting to edge computing. However, due to the characteristics of distributed multi-deployment of edge computing, it is difficult to ensure that all edge-node clusters have enough resources. Faced with the limited resources of edge-node cluster, how to carry out reasonable resource scheduling and provide users with high-quality services (high quality and low delay) has become the focus of current research. Aiming at this problem, in this paper, the edge-node resource scheduling based on linear programming is the key technology being studied. It is compared with conventional algorithm-based edge-node resource scheduling system. In the five deployments of the edge node resource scheduling system under the conventional algorithm, 8 virtual machines are used. The edge node resource scheduling system under the linear programming algorithm uses 7 virtual machines for 3 deployments and 8 virtual machines for 2 deployments. It can be seen that the results affirm the feasibility of edge node resource scheduling based on linear programming, and make due contributions to current research.

In recent years, environmental pollution and resource waste have become the focus of heated discussion around the world. In order to realize green and sustainable development, the development of green supply chain (GSC) has attracted the attention of many scholars. The research object of this paper is the dual-channel green supply chain (DGSC) composed of online channels and offline channels. The supplier is the leader of the entire DGSC, and it needs to optimize the wholesale price of the product and the level of green investment to maximize its own benefit. In addition, the supplier also needs to establish contracts with network sales platform (NSP) and store sales platform (SSP) to increase the benefit of NSP and SSP, and ultimately increase the benefit level of the entire DGSC. Among them, we constructed a centralized decision (CD) model and a decentralized decision (DD) model, and obtained the optimal pricing, optimal greenness and optimal benefit of the supplier, NSP and SSP under the two models. We found that the benefits of SC members under the DD model are generally lower than those of the CC model. Therefore, we built contracts between the supplier, NSP and SSP to coordinate. Finally, we substituted specific parameters to verify the model. The following conclusions are drawn: (i) When consumers prefer online sales channels, it will positively affect the online sales price, online sales volume and greenness level of DGSC. At the same time, it negatively affects offline sales price and sales volume. (ii) The benefit of DGSC and the benefit of NSP, SSP, and the supplier show a phenomenon of decline first and then increase with respect to consumers’ preference for online sales channels. (iii) Under the coordination contract, the subsidy factor positively affects the benefits of the supplier and online and offline wholesale prices, and negatively affects the benefits of online platforms and physical stores.

Businesses are on a perpetual quest to increase purchase intention to increase product sales. Currently, this study is also an effort in a similar context where consumers’ perception of product assortment, internet of things, order fulfillment, green packaging, and green design are comprehended. Besides, the research entails understating the assistance given to the customers for product purchases, followed by the signaling effect of these factors on the brand’s shopping assistance. The impact of shopping assistance is empirically analyzed on purchase intention to grasp the phenomena. Data were gathered using an online survey form, and analysis was done using PLS-SEM. The empirical testing suggests that product assortment, internet of things, order fulfillment, green packaging, and green design on shopping assistance support purchase intention. The result also confirms meditation of shopping assistance in the proposed model.

This paper proposed a modified Oxley’s model using the nonequipartition parallel shear-zone theory. By introducing a strain and strain rate distribution model in the first shear band, the assumption that primary deformation zone was divided equally by the main shear plane was eliminated. Then the expressions of shear-zone thickness and resultant angle were derived according to Oxley’s cutting theory. Finally, a force prediction model for orthogonal cutting was established by analyzing the mechanical-thermal characteristics in the deformation zone. The thin-walled cylinder made of AISI 304 was used to carry out the orthogonal turning experiment. A variety of cutting force prediction models were used to compare with the experimental results. The calculation results from the proposed model show that the prediction accuracy of cutting force in both cutting and feed directions is improved.

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.

The problems of parallel two-sided assembly lines balancing (TALB) are nonpolynomial (NP-hard) problems that belong to the field of combinatorial optimization. In most cases, these problems can be resolved with algorithms. To this end, this research adopts colored Petri nets (CPN) and a minimal spanning tree (MST) to address the type I problems for two-sided assembly line balancing problem (TALBP). This research proposes a three-step method. In the first step, a reachability tree chart is built to show potential tasks order assignment. In the second step, the color firing mechanism is employed to determine if the colored conditions are satisfied. The final step uses a MST and a least-time prioritized rule to select the proper tasks for assignment efficiently. These steps will be performed repetitively until all the tasks are properly assigned. Since the whole assignment process is based on graphical and paradigm methods, it can be adopted to lessen the burdens of complex calculations. The end of the research will present the comparison results between the examples from the literature and the method proposed in this research. As indicated by the research findings, employing CPN and an MST for TALBPs (type-I) can obtain even favorable and consistent results over legacy methods, and further optimize workstation design and enhance balance efficiency.

Magnetic field-assisted electrochemical spark drilling (MA-ECSD) is a cost-effective triplex hybrid machining technique that has been developed to enhance the machining depth and surface roughness of insulated and hard-to-scribe materials. The study presented adopts a reformed approach for creation of dynamic magnetic field during drilling of Sodalime glass where a 34 AWG copper wire coiled electromagnet has been installed in the in-house designed and fabricated setup of MA-ECSD. The experimental plan is based on Box–Behnken design (BBD) of Response Surface Methodology (RSM) and significance of parameters is determined using ANOVA. Multi-objective optimization (MOO) is performed by applying Grey Relational Analysis (GRA). A noncontact optical profilometer measures the machining depths and surface roughness of drilled holes. The installed electromagnet generated dynamic magnetic field intensity (MFI) ranging between 0.00 and 0.18 Tesla. Preliminary experiments were conducted to select and set the range of input parameters. Significant effect of voltage, NaOH concentration and MFI on machining depth and surface roughness is found and optimal parameter settings obtained are 24V, 30wt% and 0.09 Tesla. Machining depth increased by about 13.03% with rise in voltage-NaOH concentration and surface roughness improved by 25.3% with elevation in voltage-MFI. Dynamic MFI generated from electromagnet helped in smooth motion of electrolyte in the fine space amidst cathode and glass slide due to magnetohydrodynamic effect (MHD) which resulted in enhanced machining depth and surface roughness. The experimental and predicted results obtained after confirmatory test are appreciable which is evident from SEM images and images obtained from Optical profilometer.

The multitude of nodes, sensors and other devices in internet of things enables manufacturing systems to create security challenges for engineers and researchers. In particular, the nodes’ mobility, the mobile robots in the internet of things-based nodes increase the vulnerability of the device and sensors in the manufacturing network. In this paper, the encryption scheme is implemented to secure an internet of things-based additive manufacturing nodes against the network layer attack. To this end, the paper considers the black hole attack as a network layer attack and modifies the routing protocol to mitigate the black hole attack. The RSA encryption technique is used to encrypt and decrypt the routing messages between the manufacturing nodes using the public and private keys. The public key is used for encrypting the route request messages from manufacturing nodes while the private key is utilized for decrypting the messages at the receiver node. The proposed scheme is simulated using network simulator NS-2 and its performances are compared.