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Ti_49.4Ni_50.6 (at. %) shape memory alloy (SMA) is a unique class of smart materials because of unbeatable property which given a wide variety of their applications across a broad range of fields including an orthopedic implant. It plays a very important role in the constructions of novel orthopedic implants application (like dynamic compression plate) because of lower Young’s modulus compared to other biomedical implant materials, high mechanical strength, excellent corrosion resistance and unique property like shape memory effect. Conventional machining of Ti-Ni yields poor surface finish and low dimensional accuracy of the machined components. Hence, wire electro-discharge machining (WDEM) of Ti-Ni has been performed. The purpose of this paper is to investigate the effect of variation of five process parameters, namely, a pulse-on time, pulse-off time, spark gap set voltage (SV), wire feed and wire tension on the material removal rate, surface roughness (SR), kerf width (KW) and dimensional deviation (DD), in the WDEM of Ti_49.5Ni_50.6 SMA.

The effect of machining parameters on Ti_49.4Ni_50.6 has been fully explored using WEDM with zinc coated brass wire as an electrode. In this work, L18 orthogonal array based on Taguchi method has been used to conduct a series of experiments and statically evaluate the experimental data by the use of the method of analysis of variance. Scanning electron microscope images of the machined surface, at the highest and lowest pulse-on time, have been taken to evaluate the quality of surface in terms of their SR values.

For the highest pulse-on time, it is observed that blow holes, cracks, melted droplets and craters have been formed on the machined surface with an SR of 2.744 µm, while for the lowest pulse-on time, these are not formed with an SR of 0.862 µm. It is seen that the pulse-on time is the most significant process parameter for MRR, SR and KW, while the DD is significantly affected by spark gap SV. The optimal values of the process parameters have been obtained by the method of analysis of mean and the confirmatory experiments have been carried out to validate results of optimization. Energy dispersive spectroscopy analysis of the machined surface of Ti_49.4Ni_50.6 has shown a certain amount of deposition of material on the machined surface.

This is an original paper.

Warranty service plays a critical role in sustainability and service continuity and influences customer satisfaction. Considering the role of social networks in customer feedback channels, one of the essential sources to examine the reflection of a product/service is social media mining. This paper aims to identify the frequent product failures through social network mining. Focusing on social media data as a comprehensive and online source to detect warranty issues reveals opportunities for improvement, such as user problems and necessities. This model will detect the causes of defects and prioritize improving components in a product-service system based on FMEA results.

Ontology-based methods, text mining and sentiment analysis with machine learning methods are performed on social media data to investigate product defects, symptoms and the relationship between warranty plans and customer behaviour. Also, the authors have incorporated multi-source data collection to cover all the possibilities. Then the authors promote a decision support system to help the decision-makers using the FMEA process have a more comprehensive insight through customer feedback. Finally, to validate the accuracy and reliability of the results, the authors used the operational data of a LENOVO laptop from a warranty service centre and classifier performance metrics to compare the authors’ results.

This study confirms the validity of social media data in detecting customer sentiments and discovering the most defective components and failures of the products/services. In other words, the informative threads are derived through a data preparation process and then are based on analyzing the different features of a failure (issues, symptoms, causes, components, solutions). Using social media data helps gain more accurate online information due to the limitation of warranty periods. In other words, using social media data broadens the scope of data gathering and lets in all feedback from different sources to recognize improvement opportunities.

This work contributes a DSS model using multi-channel social media mining through supervised machine learning for warranty-service improvement based on defect-related discovery to unravel the potential aspects of social networks analysis to predict the most vulnerable components of a product and the main causes of failures that lead to the inputs for the FMEA process and then, a cost optimization. The authors have used social media channels like Twitter, Facebook, Reddit, LENOVO Forums, GitHub, Quora and XDA-Developers to gather data about the LENOVO laptop failures as a case study.