Selection of optimal cutting conditions for pocket milling using genetic algorithm

Pocket milling is the most known machining operation in the domains of aerospace, die, and mold manufacturing. In the present work, GA-OptMill, a genetic algorithm (GA)-based optimization system for the minimization of pocket milling time, is developed. A wide range of cutting conditions, spindle speed, feed rate, and axial and radial depth of cut, are processed and optimized while respecting the important constraints during high-speed milling. Operational constraints of the machine tool system, such as spindle speed and feed limits, available spindle power and torque, acceptable limits of bending stress and deflection of the cutting tool, and clamping load limits of the workpiece system, are respected. Chatter vibration limits due to the dynamic interaction between cutting tool and workpiece are also embedded in the developed GA-OptMill system. Enhanced capabilities of the system in terms of encoded GA design variables and operators, targeted cutting conditions, and constraints are demonstrated for different pocket sizes. The automatically identified optimal cutting conditions are also verified experimentally. The developed optimization system is very appealing for industrial implementation to automate the selection of optimal cutting conditions to achieve high productivit

Cutting torque and tangential cutting force coefficient identification from spindle motor current

This article presents an enhanced methodology for cutting torque prediction from the spindle motor current, readily available in modern machine tool controllers. This methodology includes the development of the spindle power model which takes into account all mechanical and electrical power losses in a spindle motor for high-speed milling. The predicted cutting torque is further used to identify tangential cutting force coefficients in order to predict accurately the cutting forces and chatter-free regions for milling process planning purposes. The developed model is compared with other studies available in the literature, and it demonstrates significant improvements in terms of the completeness and accuracy achieved. The developed model is also validated experimentally, and the obtained results show good compliance between the predicted and the measured cutting torque. The developed enhanced procedure is very appealing for industrial implementation for cutting torque/force monitoring and tangential cutting force coefficient identificatio

Content Distribution in Social Groups

We study Social Groups consisting of self-interested inter-connected nodes looking for common content. We can observe Social Groups in various socio-technological networks, such as Cellular Network assisted Device-to-Device communications, Cloud assisted Peer-to-Peer Networks, hybrid Peer-to-Peer Content Distribution Networks and Direct Connect Networks. Each node wants to acquire a universe of segments at least cost. Nodes can either access an expensive link to the content distributor for downloading data segments, or use the well-connected low cost inter-node network for exchanging segments among themselves. Activation of an inter-node link requires cooperation among the participating nodes and reduces the cost of downloading for the nodes. However, due to uploading costs, Non-Reciprocating Nodes are reluctant to upload segments, in spite of their interest in downloading segments from others. We define the Give-and-Take (GT) criterion, which prohibits non-reciprocating behaviour in Social Groups for all nodes at all instants. In the “Full Exchange” case studied, two nodes can exchange copies of their entire segment sets, if each node gains at least one new segment from the other. Incorporating the GT criterion in the Social Group, we study the problem of downloading the universe at least cost, from the perspective of a new node having no data segments. We analyze this NP-hard problem, and propose algorithms for choosing the initial segments to be downloaded from the content distributor and the sequence of nodes for exchange. We compare the performance of these algorithms with a few existing P2P downloading strategies in terms of cost and running time. In the second problem, we attempt to reduce the load on the content distributor by choosing a schedule of inter-node link activations such that the number of nodes with the universe is maximized. Link activation decisions are taken by a central entity, the facilitator, for achieving the social optimum. We present the asymptotically optimal Randomized algorithm. We also present other algorithms, such as the Greedy Links algorithm and the Polygon algorithm, which are optimal under special scenarios of interest. We compare the performances of all proposed algorithms with the optimal value of the objective. We observe that computationally intensive algorithms exhibit better performance. Further, we consider the problem of decentralized scheduling of links. The decisions of link activations are made by the participating nodes in a distributed manner. While conforming to the GT criterion for inter-node exchanges, each node's objective is to maximize its utility. Each node tries to find a pairing partner by preferentially exploring nodes for link formation. Unpaired nodes choose to download a segment using the expensive link with Segment Aggressiveness Probability (SAP). We present linear complexity decentralized algorithms for nodes to choose their best strategy. We present a decentralized randomized algorithm that works in the absence of the facilitator and performs close to optimal for large number of nodes. We define the Price of Choice to benchmark performance of Social Groups (consisting of non-aggressive nodes) with the optimal. We evaluate the performance of various algorithms and characterize the behavioural regime that will yield best results for node and Social Group as well

Multimodal Sentiment Analysis: Perceived vs Induced Sentiments

Social media has created a global network where people can easily access and exchange vast information. This information gives rise to a variety of opinions, reflecting both positive and negative viewpoints. GIFs stand out as a multimedia format offering a visually engaging way for users to communicate. In this research, we propose a multimodal framework that integrates visual and textual features to predict the GIF sentiment. It also incorporates attributes including face emotion detection and OCR generated captions to capture the semantic aspects of the GIF. The developed classifier achieves an accuracy of 82.7% on Twitter GIFs, which is an improvement over state-of-the-art models. Moreover, we have based our research on the ReactionGIF dataset, analysing the variance in sentiment perceived by the author and sentiment induced in the reade

Medication compliance in schizophrenic out patients with psychoactive substance use co-morbidity: a cross-sectional study

Background: Medication adherence is an important issue in the treatment and management of persons with psychiatric disorders including schizophrenia. Apart from side effects and inefficient outcomes of psychiatric medications, substance abuse also complicates the adherence pattern to the prescribed medications. Present study was designed to estimate the magnitude of medication non-adherence and its correlates in patients with schizophrenia having co-morbid psychoactive substance use.Method: The 60 schizophrenic patients with active substance use were taken from OPD of institute of mental health and hospital, Agra. Positive and negative syndrome scale (PANSS), alcohol, smoking and substance involvement screening test (ASSIST), medication adherence rating scale (MARS) and Morisky 8-item medication adherence questionnaire (MMAQ-8) were used to gather relevant clinical data along with a proforma for recording socio-demographic characteristics.Results: The results revealed an alarming level of medication adherence. The 91.7% sample (55 patients) met the criteria for medication non-adherence. Majority of the patients were using alcohol (58.3%) and cannabis (51.7%). Conclusions: Given the high rate of medication non-compliance it is suggested that specific intervention aimed at compliance to prescribed medication is needed in this population