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In this paper, a new slip-line model approach for modeling the orthogonal cutting process with rounded-edge tools and its associated hodograph are proposed. This model consists of eight regions, which include a dead region in front of the rake face of tool. Dewhurst and Collins’s matrix technique for numerically solving the slip-line problem is employed in the mathematical formulation of the new model. The experimental results show that a small dead region is seen in front of the rake face of tool during cutting with a rounded-edge cutting tool. The unknown slip-line angle pair was solved depending on the force data obtained experimentally and variation of the subregions with cutting edge radius was determined. Cutting force, thrust force, and dead zone grow as cutting edge radius increases in cutting edge-radiused tools.

Electric discharge machining is one of the widely used nontraditional methods in manufacturing industry. In the present study, the effects of discharge energy density on the material removal rate, electrode wear rate, surface average roughness of the workpiece and the tool electrode were investigated. AISI 1040 steel as workpiece material and electrolytic copper as tool electrode were selected. Experiments were designed and conducted using one-factor-at-a-time approach. During the experiments, current and voltage were kept constant, whereas pulse on time and pulse off time were varied at two levels. In order to investigate and analyze the effects of discharge energy density on the responses, different diameters of the tool electrode were used. It has been observed that the discharge energy density could be considered as a significant factor for appropriate selection of process parameters for better machining characteristics.

Polymer composite products can be obtained by primary manufacturing processes such as contact molding, vacuum bag molding, resin transfer molding, or sheet molding compound and secondary processes such as drilling and saw cutting. Drilling is generally employed to make bolted or riveted assembles in composite structures. In drilling, some defects like delamination and crack are seen, and also undesired hole surface roughness related to tool wear is an another problem frequently encountered. In this study, tool wear in drilling of sheet molding compound (SMC) composite, consisted of 30 wt.% glass fiber, 25 wt.% polyester, and 45 wt.% calcium carbonate, was investigated. SMC composite was drilled under different cutting speeds, feeds, and drill point angles. Taguchi design of experiments and analysis of variance were utilized to determine the optimal cutting parameters and to analyze the effects of them on the tool wear. The feed followed by the drill point angle were found to be the important factors while cutting speed was the least effective parameter. Chip volume was accepted as a criterion to compare obtained data. Increasing feed and decreasing drill point angle reduced the tool wear. Multivariable linear regression analysis was also employed to determine the correlations between the factors and the tool wear. Finally, a model was generated and a good approximation was achieved in the comparison of the experimental data and the predicted data obtained from the model.

In interrupted cutting, chips occur as intermittent and cutting tool may be damaged under severe variable loading. And it is known that the cutting forces cause transverse cracks on the rake face, and cutting tool may be damaged during interrupted cutting under mechanical and thermal fatigue in a short period of time. In this work, carbide inserts and forged SAE 4340 workpiece with axial slots were used in interrupted turning. The hardness values of some points on rake face near the cutting edge were measured, and cracks and failures on cutting tool were displayed by scanning electron microscopy (SEM). Additionally, milling experiments were conducted with carbide inserts on C45 medium carbon steel. In the interrupted cutting, the effect of work hardening of cobalt in sintered carbide cutting tool on tool failure was investigated. According to the results, micro hardness of sintered carbide cutting tool increased due to work hardening of cobalt, and this caused the micro crack formation on cutting tool.

Improvement of chip control is a necessity for automated machining. Chip control is closely related to chip flow and it plays also a predominant role in the effective control of chip formation and chip breaking for the easy and safe disposal of chips, as well as for protecting the surface-integrity of the workpiece. Although several ways to predict the chip flow angle (CFA) have been subjected in some researches, a good approximation has not been achieved yet. In this study, using different indexable inserts and cutting conditions for turning of mild steel, the chip flow angles were measured and some of the collected data from this experimental study were used for training with a two hidden layered backpropagation neural network algorithm. A group was formed from randomly selected data for testing. The chip flow angle values found from multiple regression, neural network (NN) and studies of previous researchers under the same turning conditions of the present study were compared. It has been seen that the best prediction was obtained by neural network approach.

A two-sided vectoral relationship exists between nachschrift and the quotations it contains, nachschrift is thus not only an intertextual work but also an intertextual plan of action, whereby the work has a retroactive effect on the interpretation of its source texts, raising questions about how to treat the Holocaust. Contrary to doubts about the representability of history, attention is called here to the literary aspect of historical representation.

Purpose The purpose of this paper is to propose a new RFM model called length, recency, frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail industry; and to identify different customer segments in this industry based on the proposed model. Design/methodology/approach This study combines the LRFMP model and clustering for customer segmentation. Real-life data from a grocery chain operating in Turkey is used. Three cluster validation indices are used for optimizing the number of groups of customers and K-means algorithm is employed to cluster customers. First, attributes of the LRFMP model are extracted for each customer, and then based on LRFMP model features, customers are segmented into different customer groups. Finally, identified customer segments are profiled based on LRFMP characteristics and for each customer profile, unique CRM and marketing strategies are recommended. Findings The results show that there are five different customer groups and based on LRFMP characteristics, they are profiled as: “high-contribution loyal customers,” “low-contribution loyal customers,” “uncertain customers,” “high-spending lost customers” and “low-spending lost customers.” Practical implications This research may provide researchers and practitioners with a systematic guideline for effectively identifying different customer profiles based on the LRFMP model, give grocery companies useful insights about different customer profiles, and assist decision makers in developing effective customer relationships and unique marketing strategies, and further allocating resources efficiently. Originality/value This study contributes to prior literature by proposing a new RFM model, called LRFMP for the customer segmentation and providing useful insights about behaviors of different customer types in the Turkish grocery industry. It is also precious from the point of view that it is one of the first attempts in the literature which investigates the customer segmentation in the grocery retail industry.

PurposeThe purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.Design/methodology/approachThis paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.FindingsIt was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.Originality/valueThis paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

Purpose Predicting customers’ purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers’ individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them. Design/methodology/approach The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior. Practical implications The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies. Originality/value This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers’ individual purchase behaviors.