Fracture mechanisms of hybrid adhesive bonded joints:effects of the stiffness of constituents

In this study, different single-lap hybrid joints are used to analyse the effects of the stiffness of the adherends and the adhesive on the failure mechanism. The hybrid joints include a combination of (a) different adherends: aluminium (6082 T6) and PolyPhtalamide (PPA) reinforced with 50% of glass fibre (grade HTV-5H1 from Grivory) and (b) different adhesives: epoxy-based adhesive (Loctite EA 9497) and silane-modified polymer-based adhesive (Teroson MS 9399). Six different single-lap joints are fabricated and analysed. The cohesive parameters of different adhesives against different adherends are determined respectively using single-mode coupons and validated with finite element modelling. Single-lap shear tests are conducted to understand different fracture mechanisms of the joints. Finite element (FE) models using the Cohesive Zone Method (CZM) are developed to simulate the failure of the joints and validated by the testing results. Different failure processes obtained from different hybrid joints combinations are discussed further by analysing the stress distributions along the interfaces of the joints. Finally, the relationship between the stiffness of the constituents of a hybrid adhesive joint and its failure mechanism is summarised. The load vs displacement behaviour of the single-lap joints demonstrate that the stiffness of adherends affects the maximum failure load of the joints with rigid adhesive (epoxy). However, the joint with flexible adhesive (polyurethane) is not sensitive to the stiffness of the adherends. In addition, higher shear stress distribution occurs in the interface adjacent to the adherend with lower stiffness, leading to the failure initiation at the PPA side regardless of adhesive types

Sexbots: sex slaves, vulnerable others or perfect partners?

This article describes how sexbots: sentient, self-aware, feeling artificial moral agents created soon as customised potential sexual/intimate partners provoke crucial questions for technoethics. Coeckelbergh's model of human/robotic relations as co-evolving to their mutual benefit through mutual vulnerability is applied to sexbots. As sexbots have a sustainable claim to moral standing, benefits and vulnerabilities inherent in human/sexbots relations must be identified and addressed for both parties. Humans' and sexbots' vulnerabilities are explored, drawing on the philosophy and social science of dehumanisation and inclusion/exclusion. This article argues humans as creators owe a duty of care to sentient beings they create. Responsible innovation practices involving stakeholders debating ethicolegal conundrums pertaining to human duties to sexbots, and sexbots' putative interests, rights and responsibilities are essential. These validate the legal recognition of sexbots, the protection of their interests through regulatory oversight and ethical limitations on customisation which must be put in place

PENGANTAR BISNIS EDISI 11

vii,; 800 hlm,; 28 c

A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods:Deep neuron networks and genetic programming

The aerospace, automotive and marine industries have witnessed a rapid increase of using adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering materials. Joint strength is the key property in evaluating the capability of the adhesive joint. In this paper, developments of black-box and grey-box machine learning (ML) models are presented to allow accurate predictions of the failure load of single lap joints by considering a mix of continuous and discrete design (geometry and material) variables. Firstly, the failure loads of 300 single lap joint samples with different geometry/material parameters are calculated by FE models to generate a data set of which accuracy is validated by experimental results. Then, a deep neuron network (black-box) and a genetic programming (grey-box) model are developed for accurately predicting the failure load of the joint. Based on both ML models, a case study is conducted to explore the relationships between specific design variables and overall mechanical performances of the single lap adhesive joint, and optimal designs of structure and material can be obtained