Motility of the reticulum and rumen of sheep given juice-extracted pasture

1. Sheep were fed on different diets of juice-extracted herbage to determine what effect juice-extraction had on reticulo-rumen motility. 2. The frequency of A and B sequences of contraction of the reticulo-rumen were recorded during eating, rumination and inactivity for continuous periods of 24–72 h by using integrated electromyograms obtained from electrodes implanted in the musculature of the reticulum and cranial dorsal rumen. 3. Animals were fed on herbage in which approximately 200 g/kg dry matter had been removed in juice extracted from ryegrass (Lolium perenne), white clover (Trifolium repens), mixed ryegrass–white clover and lucerne (Medicago saliva). 4. Over all the frequency of A sequences of contraction did not differ in animals fed on pressed herbage or the unpressed material from which it was derived, although it was slower during rumination on some of the pressed material. In contrast, the frequency of B sequences was higher on the pressed material. The frequencies of contraction of A and B sequences in animals fed on pressed herbage was related to the activity of the animals in the order eating > rumination > inactivity. 5. Changes in reticulo-rumen motility due to juice extraction were small and the frequencies of A and B sequences of contraction in sheep fed on pressed herbage were in the range encountered in ruminants consuming more conventional foods

Effect of previous handling experiences on responses of dairy calves to routine husbandry procedures

The nature of human–animal interactions is an important factor contributing to animal welfare and productivity. Reducing stress during routine husbandry procedures is likely to improve animal welfare. We examined how the type of early handling of calves affected responses to two common husbandry procedures, ear-tagging and disbudding. Forty Holstein–Friesian calves (n = 20/treatment) were exposed to one of two handling treatments daily from 1 to 5 weeks of age: (1) positive (n = 20), involving gentle handling (soft voices, slow movements, patting), and (2) negative (n = 20), involving rough handling (rough voices, rapid movements, pushing). Heart rate (HR), respiration rate (RR) and behaviour (activity, tail flicking) were measured before and after ear-tagging and disbudding (2 days apart). Cortisol was measured at −20 (baseline), 20 and 40 min relative to disbudding time. There were no significant treatment differences in HR, RR or behaviour in response to either procedure. However, the following changes occurred across both treatment groups. HR increased after disbudding (by 14.7 ± 4.0 and 18.6 ± 3.8 bpm, positive and negative, respectively; mean ± s.e.m.) and ear-tagging (by 8.7 ± 3.1 and 10.3 ± 3.0 bpm, positive and negative, respectively). After disbudding, there was an increase in RR (by 8.2 ± 3.4 and 9.3 ± 3.4 breaths/min, positive and negative, respectively), overall activity (by 9.4 ± 1.2 and 9.9 ± 1.3 frequency/min, positive and negative, respectively) and tail flicking (by 13.2 ± 2.8 and 11.2 ± 3.0 frequency/min, positive and negative, respectively), and cortisol increased from baseline at 20 min post procedure (by 10.3 ± 1.1 and 12.3 ± 1.1 nmol/l positive and negative, respectively). Although we recorded significant changes in calf responses during ear-tagging and disbudding, the type of prior handling had no effect on responses. The effects of handling may have been overridden by the degree of pain and/or stress associated with the procedures. Further research is warranted to understand the welfare impact and interaction between previous handling and responses to husbandry procedures

Inspection by exception: a new machine learning-based approach for multistage manufacturing

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively

Development of a new machine learning-based informatics system for product health monitoring

Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications

A two-step machining and active learning approach for right-first-time robotic countersinking through in-process error compensation and prediction of depth of cuts

Robotic machining processes are characterised by errors arising from the limitations of the industrial robots. These robot-related errors can compromise the overall manufacturing process performance, resulting in final products with dimensions different from the nominal specifications. To avoid accumulation of errors through several manufacturing stages, a quality inspection step is usually performed after the cutting operation. This work presents an innovative two-step manufacturing method for achieving right-first-time characteristics in robotic machining operations through in-process inspection and compensation of the systematic errors, whilst collecting suitable training data for building predictive models. The key idea behind the proposed method is based on the observation that under certain conditions, the robotic machining errors remain largely consistent, and therefore by splitting the process into two similar steps and having an inspection step in between, a prediction and then compensation of the systematic errors would be possible. A Gaussian Process Regression (GPR) framework is applied for the creation of robust process models that predict the post-process inspection result from in-process signal features, with the associated confidence intervals. An active learning algorithm that makes online decisions on the inspection task based on the current confidence of the models, is also proposed. The two-step machining method and the active learning approach were both tested on a robotic countersinking process experiment. The results showed that the in-process inspection and error compensation of the proposed two-step machining method was able to achieve final countersink depths very close to the desired target, confirming the potential for right-first-time robotic machining. In addition, the active learning results highlighted the ability of the algorithm to reduce the number of required post-process inspections, thus saving both time and costs, whilst also identifying novel data relevant for the model training

An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP

A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing

Manufacturing is usually performed as a sequence of operations such as forming, machining, inspection, and assembly. A new challenge in manufacturing is to move towards Industry 4.0 (the fourth Industrial revolution) concerning the full integration of machines and production systems with machine learning methods to enable for intelligent multistage manufacturing. This paper discusses Multistage Manufacturing Processes (MMPs) and develops a probabilistic model based on Bayesian linear regression to estimate the results of final inspection associated with comparative coordinate measurement given in-process measured coordinates. The results of two case studies for flatness tolerance evaluation demonstrate the effectiveness of the probabilistic model which aims at being part of a larger metrology informatics system to be developed for predictive analytics and agent-based advanced control in multistage manufacturing. This solution relying on accurate models can minimise post-process inspection in mass production with independent measurements

A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes

The emergence of highly instrumented manufacturing systems has enabled the paradigm of smart manufacturing that provides high levels of prognostics functionality. Of particular interest is to precisely determine geometric conformance or non-conformance of workpieces during manufacturing. This paper presents a new dimensional product health monitoring system that learns from in-process sensor data and updates the prediction of the product quality as the product is manufactured. The system uses data from multiple manufacturing stages, unlike from a single stage at a time, to predict the dimensional quality of the finished product that is updated with subsequent measurements such as On-Machine Measurements (OMMs), in on-line incremental learning fashion. It is based on self-supervised neural networks for dimensionality reduction, Gaussian Process Regression (GPR) models for probabilistic prediction about the end product condition and the associated uncertainty, and Bayesian information fusion for updating the conditional probability distribution of the end product quality in the light of new information. The monitoring approach was tested on the prediction of diameter deviations with validation results showing its ability to achieve an average accuracy better than 5 μm in terms of the Root Mean Squared Error (RMSE). Having obtained a Probability Density Function (PDF) for the measurand of interest, the conformance and non-conformance probabilities given the tolerance specifications are computed to support the principle of inspection by exception. This ability to construct a conformance probability-based product quality monitoring system using probabilistic machine learning methods constitute a step change to manufacturing prognostics

An interpretable machine learning based approach for process to areal surface metrology informatics

Surface metrology parameters represent an important class of design variables, which can be controlled because they represent the DNA or fingerprint of the whole manufacturing chain as well as form important predictors of the manufactured component's function(s). Existing approaches of analysing these parameters are applicable to only a small subset of the parameters and, as such, tend to provide a narrow characterisation of the manufacturing environment.This paper presents a new machine learning approach for modelling the surface metrology parameters of the manufactured components. Such a modelling approach can allow one to understand better and, as a result, control the manufacturing process so that the desired surface property can be achieved whilst manipulating the process conditions. The newly proposed approach utilises a fuzzy logic based-learning algorithm to map the extracted process features to the areal surface metrology parameters. It is fully transparent since it employs IF...THEN statements to describe the relationships between the input space (in-process monitoring variables) and the output space (areal surface metrology parameters). Furthermore, the algorithm includes a ridge penalty based mechanism that allows the learning to be accurate while avoiding over-fitting. This new machine-learning framework was tested on a real-life industrial case-study where it is required to predict the areal parameters of a manufacturing (machining) process from in-process data. Specifically, the case study involves a full factorial experimental design to manufacture seventeen (17) steel bearing housing parts which are fabricated from heat-treated EN24 steel bars. Validation results showed the ability of this new framework not only to predict accurately but also to generalise across different types of areal surface metrology parameters

Perfect weddings abroad

Approximately 16% of UK couples are currently married abroad. However, academic or practitioner focused research that explores the complex nature of a couple’s buying preferences or the development of innovative marketing strategies by businesses operating within the weddings abroad niche sector, is almost non-existent. This exploratory paper examines the role and relevance of marketing within the weddings abroad sector. The complex nature of customer needs in this high emotional and involvement experience, are identified and explored. A case study of Perfect Weddings Abroad Ltd highlights distinctive features and characteristics. Social networking and the use of home-workers, with a focus on reassurance and handholding are important tools used to develop relationships with customers. These tools and techniques help increase the tangibility of a weddings abroad package. Clusters of complementary services that are synergistic and provide sources of competitive advantage are identified and an agenda for future research is developed