Search results

The knowledge over the performance of cutting fluids when applied under different machining conditions (such as distinct work material and cutting parameters) is critical in order to improve the efficiency of most machining operations. This paper is concerned with the performance of cutting fluids employed under two distinct machining operations involving aluminium alloys: drilling of AA 1050‐O aluminium applying cutting fluid as a mist and turning of AA 6262‐T6 aluminium alloy using cutting fluids (as a flood) with distinct extreme pressure additives (chlorine, sulphur and phosphor).

This work reports on a experimental study of the performance of cutting fluids when machining aluminium alloys.

The results indicated an increase in the flow rate of the mist led to lower feed forces but higher torque, power consumption and specific cutting pressure in the drilling operation (AA 1050‐O aluminium). The surface finish was not drastically affected by the cutting fluid flow rate. When turning AA 6162‐T6 aluminium alloy, in general, best results were observed using 10 per cent fluid concentration applied at the tool‐workpiece interface. The cutting fluid containing chlorine as extreme pressure additive produced lower cutting forces and better surface finish at high cutting speed and low feed rate and depth of cut.

The novel element of this paper is the use of minimal lubrication (drilling) and cutting fluids with distinct extreme pressure (turning).

This paper presents a study of the development of surface roughness model when turning the mild steel hardened up to 484 HV with mixed alumina ceramic (KY1615) and coated alumina ceramic cutting tools (KY4400). The model was developed in terms of main cutting parameters such as cutting speed, feed rate and depth of cut, using response surface methodology. The established equation indicated that the feed rate affected the surface roughness the most, but other parametres remined stable for arithmetic average height parametre (Ra). However, it decreased with increasing the cutting speed, and with the starting and finishing point of cut for ten point height parametre (Rz). The cutting speed and the depth of cut had a slight effect on surface roughness values of Ra, Rz when using KY4400 cutting tools. Furthermore, the average surface roughness value of Ra was about 0.926 um, 1.089 um for KY1615, KY4400 cutting tools, respectively. The predicted surface roughness was found to be very close to experimentally observed ones at 95% confidence level. Moreover, analysis of variance indicated that squares terms were significant but interaction terms were insignificant for both cutting tools.

The purpose of this paper is to investigate the performance of four cutting oils, two different vegetable‐based cutting fluids developed from refined sunflower oil and two commercial types (semi‐synthetic and mineral), for surface roughness during drilling of AISI 304 austenitic stainless steel with HSSE tool.

L₉ (3³) orthogonal array was used for the experiment plan. Spindle speed, feed rate and drilling depth were considered as machining parameters.

Results were evaluated statistically. Mathematical models based on cutting parameters were obtained from regression analyses to predict surface roughness. ANOVA was used to determine the effect of the cutting parameters on the surface roughness. The performance results were found to be better for vegetable‐based cutting oils than that of commercial ones.

The paper reports on the use of refined sunflower oil in drilling stainless steel.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

The Green Revolution was a singular event in world history; because of the Green Revolution, world prices for all crops declined. The agricultural mechanization issue was also driven by intellectual property rights (i.e., the right to patent products), as was the agricultural chemical revolution. The livestock industrialization revolution sharply lowered the prices for all livestock products. The Gene Revolution (i.e., the recombinant DNA revolution) further lowered the cost of producing farm products. The Gene Revolution was based on three events. The first was the discovery that DNA (Delbrook) was the carrier of genetic information. The second was the discovery by Watson and Crick of the double helix structure of DNA. The third was the method of stable insertion of DNA into a host genome (Cohen and Boyer). The future of agricultural research depends on the capacity of countries to invent and imitate.

This study aims to identify and analyze how Internet of things (IoT) technology affects supply chain management (SCM) performance.

A systematic literature review was conducted (using Scopus, JSTOR, Emerald, ProQuest, Science Direct and Web of Science) covering a 20-year timeframe (2000–2020). Out of 2,572 papers identified, 171 peer-reviewed papers from the most important journals were selected. Content analysis was used following the Global Supply Chain Forum (GSCF) SCM framework.

Regarding the GSCF SCM framework processes, most IoT-based studies have addressed improving order fulfilment, manufacturing flow management and demand management processes. However, no studies addressing the Supplier relationship management process were identified, suggesting that IoT-based applications are perceived to add more value in downstream than upstream SCM processes. The importance of using enabling technologies to realize the potential of value generation of IoT was also revealed. Findings suggest new research avenues related to product development and commercialization process, the supplier relationship management process, the returns management process, servitization strategies, new SCM models and new business models.

The review encompasses only academic papers from journals considered the most relevant (retrieved from specific databases), using the impact factor as the quality criterion.

The findings can help business managers better understand the potential of IoT technology, such as the main applications identified in the literature and their impacts on SCM processes. Their importance in enabling technologies to leverage SCM performance is identified and the emerging SCM models/business models that IoT deployment can enable are highlighted.

This study contributes to filling a gap in the literature using a systematic literature review of how IoT technology affects SCM performance through content analysis, using an SCM framework to clarify which SCM processes are affected. Academic articles from the most important journals from 2000 to 2020 are identified.

Little research has been done on the impact of realistic job information on the salesperson′s decision to terminate a sales position. Using propensity to leave as a surrogate for salesforce turnover, presents the results of an investigative study of realistic job information in sales interviewing. Breaks down the construct of realistic job information into four dimensions, namely volume; personal relevance; depth; and accuracy. Finds that salespersons who display a higher tendency to leave their jobs believed their job preview to have been less realistic than those who exhibit a propensity to stay in their positions. Also finds, however, that while relevance, depth and accuracy of job information are strong predictors of propensity to leave a sales job, volume of information is not, suggesting that the problem may be one of interview quality, rather than information quantity.

Examines turnover functionality (high‐ and low‐performing quitters and stayers) in a business‐to‐business sales setting. Prior research indicates that antecedents of turnover frequency and turnover functionality are different. However, this may be an artifact of the way in which turnover has been measured. This study develops a new criterion of turnover functionality. Additionally, common antecedents of turnover frequency were examined to see if they could differentiate between high‐ and low‐performing quitters and stayers. The results indicate that several antecedents associated with turnover frequency are able to discriminate among different groups of high‐ and low‐performing quitters and stayers. Specifically, satisfaction with promotion, supervision, work, and global satisfaction contribute to our understanding of turnover functionality. Additionally, role conflict, role ambiguity, anxiety, evaluation of job alternatives, and intention to quit are also good discriminators of turnover functionality.

This paper aims to presents a counterexample that points to an inconsistency generated by the first- and second-order approximation methods, FORM and SORM, respectively, procedures for elliptical problems.

The classical results of theory measure and functional analysis were used.

The FORM and SORM are known to find solutions in a Gaussian space. This procedure does not satisfy the conditions of the Lax–Milgram theorem and does not assure the existence and uniqueness of the solution.

This paper alerts the engineering research community that uses these methods, initiating discussion and improvement of FORM and SORM procedures.

This paper puts in check the feasibility of using FORM and SORM in engineering problems.

From the moment they were introduced to the engineering and scientific communities, the FORM and SORM were taken as the bases for solving various problems found in the literature and indifferent documents scattered throughout the world over the past 50 years, for FORM, and 40 years, for SORM. Even though it was a very serious fault, at least for elliptical problems, pointed out in this work, it went unnoticed all those years by the research community. Therefore, the contribution of this paper is to present the engineering community that uses FORM and SORM in elliptical problems an unnoticed failure since the introduction of these methods.