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Aluminium alloys can be used as lightweight and high-strength materials in combination with the technology of laser beam welding, an efficient joining method, in the manufacturing of automotive parts. The purposes of this paper are to conduct laser welding experiments with Al2024 in the lap joint configuration, model the laser welding process parameters of Al2024 alloys and use propounded models to optimize the process parameters.

Laser welding of Al2024 alloy has been conducted in the lap joint configuration. Then, the influences of explanatory variables (laser peak power, scanning speed and frequency) on outcome variables (weld width [WW], throat length [TL] and breaking load [BL]) have been investigated with Poisson regression analysis of the data set derived from experimentation. Thereafter, a multi-objective genetic algorithm (MOGA) has been used using MATLAB to find the optimum solutions. The effects of various input process parameters on the responses have also been analysed using response surface plots.

The promulgated statistical models, derived with Poisson regression analysis, are evinced to be well-fit ones using the analysis of deviance approach. Pareto fronts have been used to demonstrate the optimization results, and the maximized load-bearing capacity is computed to be 1,263 N, whereas the compromised WW and TL are 714 µm and 760 µm, respectively.

This work of conducting laser welding of lap joint of Al2024 alloy incorporating the Taguchi method and optimizing the input process parameters with the promulgated statistical models proffers a neoteric perspective that can be useful to the manufacturing industry.

The increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.

Preferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.

The proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.

This method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.