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This paper aims to optimize the single-point incremental forming process variables for realizing higher formability in Inconel 625 components and to plot the forming limit diagram for Inconel 625 aviation-grade superalloy.

The formability of Inconel 625 components has been measured in terms of major strain, minor strain and minimum sheet thickness. Response surface methodology with desirability function analysis has been used to achieve maximum formability. The finite element analysis has been conducted at optimal parametric setting.

The derived forming limit diagram proves that the maximum forming limit for Inconel 625 is 57.5° at the optimal parametric setting, achieved with desirability of 0.995. The outcomes of finite element analysis conducted at optimal parametric setting show excellent agreement with confirmation experiment results.

Inconel 625 superalloy is frequently used in aircraft and other high-performance applications for its superior strength.

It has been suggested that to enhance formability, higher tool rotation speed, minimum step-size, larger tooltip diameter and higher wall angle must be used. Wall angle is the governing parameter among all the parameters.

The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

This paper aims to propose a framework model of belief consistency on the confirmatory bias theory, trying to explore the interactions between cues of credence-label structure and different controversial types of health foods, as well as the intermediary mechanism of belief consistency.

This paper presents a conceptual framework of belief consistency based on confirmation bias theory. The interactions between cues of credence-label structure and different controversial types of health foods, as well as the intermediary mechanism of belief consistency.

Consumers’ willingness to purchase varies under interactions between cues of credence-label structure (product-level and ingredient-level credence-label cues) and different controversial types of health foods (noncontroversial health foods and controversial health foods). In the consumption context of noncontroversial health foods, the presence of product-level credence-label cues causes confirmation bias, greater perception of health belief consistency and higher willingness to purchase healthy foods. In the consumption context of controversial health foods, the presence of ingredient-level credence-label cues results in the prevention of confirmation bias, lower perception of unhealthy belief consistency and higher willingness to purchase health foods.

This paper offers a significant tool for researchers to enrich relevant theories in the field of the conceptual framework of cues of credence-label structure. It also discusses practical implications for enterprise marketing and for the health and welfare of consumers.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

This paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO₂ hybrid composite fabricated by friction stir processing.

An empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.

The relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO₂) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO₂ resulted in a decrease in ductility.

AZ91D/AgNPs/TiO₂ hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.

In terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This study explores the differences between digital immigrants (DIs) and digital natives (DNs) in the continuance of routine and innovative information system use.

A quantitative survey was conducted with two different samples comprising 100 DIs and 152 DNs in mandatory information system use contexts. Data were analyzed with structural equation modeling to examine the hypothesized relationships in the research model.

Results revealed differences among digital nativity groups. The effect of confirmation of expectations about system use on satisfaction is stronger for DNs whereas the effect on task–technology fit (TTF) is similar in both digital groups. Interestingly, significant differences between digital nativity groups occur in routine use. For DIs, TTF and habit are significant while for DNs, satisfaction significantly affects routine use. The results show no difference between digital native groups regarding innovative use.

This study extends the concept of digital nativity to routine and innovative system use, contributing to an enhanced understanding about the differences in information systems continuance (ISC) based on digital nativity. It also provides a fine-grained discussion of how to classify digital nativity and its impact in working contexts and extends the IS continuance model by considering two types of IS usage.

This study aims to identify the significant factors of the multi-dimpling process, determine the most influential parameters of multi-dimpling to increase the dimple sheet strength and make a low-cost model of the multi-dimpling for sheet metal industries. To create an empirical expression linking process performance to different input factors, the percentage contribution of these elements is also calculated.

Taguchi grey relational analysis is used to apply a new effective strategy to experimental data in order to optimize the dimpling process parameters while taking into account several performance factors and low-cost model. In addition, a statistical method called ANOVA is used to ensure that the results are adequate. The optimal process parameters that generate improved mechanical properties are determined via grey relational analysis (GRA). Every level of the process variables, a response table and a grey relational grade (GRG) has been established.

The factors created for experiment number 2 with 0.5 mm as the sheet thickness, 2 mm dimple diameter, 0.5 mm dimple depth, 8 mm dimples spacing and the material of SS 304 were allotted rank one, which belonged to the optimal parameter values giving the greatest value of GRG.

The study demonstrates that the process parameters of any dimple sheet manufacturing industry can be optimized, and the effect of process parameters can be identified.

The proposed low-cost model is relatively economical and readily implementable to small- and large-scale industries using newly developed multi-dimpling multi-punch and die.

Firms are increasingly required to make ethical choices when selecting suppliers for their supply chains, and the decisions often rest on individual purchasing managers within the firm. This study builds on the literature on ethical decision-making and the concept of decision frames to investigate the decision-making process of purchasing managers in financially distressed firms. Codes of Conduct (CoC) and how they are enforced (financial rewards and codified procedures for oversight) are studied in terms of their effectiveness in informing and guiding purchasing managers in their supplier selection decisions.

Four sequential experiments were conducted with a total of 648 purchasing managers from manufacturing firms.

The results indicate that purchasing managers in firms facing financial distress are more than four times more likely than purchasing managers in the control groups to select the less ethical supplier in favor of better operational performance. As a potential remedy, it is found that enforcing the firm's CoC help to counteract this tendency and increase ethical supplier selection decisions by 2.1- to 2.6-fold. However, CoC enforcement that invokes multiple conflicting decision frames simultaneously is more likely to impair than promote ethical supplier selection decisions, compared to situations where only one enforcement method is present.

These findings develop an improved understanding of purchasers' decision-making processes and shed light on how to effectively use CoCs to guide these decisions.