Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management. A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of waste produced. The ML models had greater predictive capacity than a linear model with stepwise parameter selection. Then, uncertainty is included in the predictions using a normal distribution based on the residuals of the model. Furthermore, we also demonstrate that ML models can be used as a tool to identify possible production anomalies. This research shows innovative ways to deal with uncertainty in production planning using modern methods in the field of operation research. These tools improve classical methods and provide production managers with valuable information to assess the economic benefits of improved machinery or process controls. As a consequence, accurate predictive models can potentially improve the profitability of food companies, also reducing their environmental impact.</p

NASA Tech Briefs, July 2006

Topics covered include: Airport Remote Tower Sensor Systems; Implantable Wireless MEMS Sensors for Medical Uses; Embedded Sensors for Measuring Surface Regression; Coordinating an Autonomous Earth-Observing Sensorweb; Range-Measuring Video Sensors; Stability Enhancement of Polymeric Sensing Films Using Fillers; Sensors for Using Times of Flight to Measure Flow Velocities; Receiver Would Control Phasing of a Phased-Array Antenna; Modern Design of Resonant Edge-Slot Array Antennas; Carbon-Nanotube Schottky Diodes; Simplified Optics and Controls for Laser Communications; Coherent Detection of High-Rate Optical PPM Signals; Multichannel Phase and Power Detector; Using Satellite Data in Weather Forecasting: I; Using Dissimilarity Metrics to Identify Interesting Designs; X-Windows PVT Widget Class; Shuttle Data Center File-Processing Tool in Java; Statistical Evaluation of Utilization of the ISS; Nanotube Dispersions Made With Charged Surfactant; Aerogels for Thermal Insulation of Thermoelectric Devices; Low-Density, Creep-Resistant Single-Crystal Superalloys; Excitations for Rapidly Estimating Flight-Control Parameters; Estimation of Stability and Control Derivatives of an F-15; Tool for Coupling a Torque Wrench to a Round Cable Connector; Ultrasonically Actuated Tools for Abrading Rock Surfaces; Active Struts With Variable Spring Stiffness and Damping; Multiaxis, Lightweight, Computer-Controlled Exercise System; Dehydrating and Sterilizing Wastes Using Supercritical CO2; Alpha-Voltaic Sources Using Liquid Ga as Conversion Medium; Ice-Borehole Probe; Alpha-Voltaic Sources Using Diamond as Conversion Medium; White-Light Whispering-Gallery-Mode Optical Resonators; Controlling Attitude of a Solar-Sail Spacecraft Using Vanes; and Wire-Mesh-Based Sorber for Removing Contaminants from Air

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

On Characterization and Optimization of Engineering Surfaces

Swedish manufacturing industry in collaboration with academia is exploring innovative ways to manufacture eco-efficient and resource efficient products. Consequently, improving manufacturing efficiency and quality has become the priority for the manufacturing sector to remain competitive in a sustainable way. To achieve this, control and optimization of manufacturing process and product’s performance are necessary. This has led to increase in demand for functional surfaces, which are engineering surfaces tailored to different applications. With new advancements in manufacturing and surface metrology, investigations are steadily progressing towards re-defining quality and meeting dynamic customer demands. In this thesis, surfaces produced by different manufacturing systems are investigated, and methods are proposed to improve specification and optimization.The definition and interpretation of surface roughness vary across the manufacturing industry and academia. It is well known that surface characterization helps to understand the manufacturing process and its influence on surface functional properties such as wear, friction, adhesivity, wettability, fluid retention and aesthetic properties such as gloss. Manufactured surfaces consist of features that are relevant and features that are not of interest. To be able to produce the intended function, it is important to identify and quantify the features of relevance. Use of surface texture parameters helps in quantifying these surface features with respect to type, region, spacing and distribution. Currently, surface parameters Ra or Sa that represent average roughness are widely used in the industry, but they may not provide adequate information on the surface. In this thesis, a general methodology, based on the standard surface parameters and statistical approach, is proposed to improve the specification for surface roughness and identify the combination of significant surface texture parameters that best describe the surface and extract valuable surface information.Surface topography generated by additive, subtractive and formative processes is investigated with the developed research approach. The roughness profile parameters and areal surface parameters defined in ISO, along with power spectral density and scale sensitive fractal analysis, are used for surface characterization and analysis. In this thesis, the application of regression statistics to identify the set of significant surface parameters that improve the specification for surface roughness is shown. These surface parameters are used to discriminate between the surfaces produced by multiple process variables at multiple levels. By analyzing the influence of process variables on the surface topography, the research methodology helps to understand the underlying physical phenomenon and enhance the domain-specific knowledge with respect to surface topography. Subsequently, it helps to interpret processing conditions for process and surface function optimization.The research methods employed in this study are valid and applicable for different manufacturing processes. This thesis can support the guidelines for manufacturing industry focusing on process and functional optimization through surface analysis. With increase in use of machine learning and artificial intelligence in automation, methodologies such as the one proposed in this thesis are vital in exploring and extracting new possibilities in functional surfaces

The doctoral research abstracts. Vol:12 2017 / Institute of Graduate Studies, UiTM

Foreword: Congratulation to IGS on your continuous efforts to publish the 12th issue of the Doctoral Research Abstracts which highlights research in various disciplines from science and technology, business and administration to social sciences and humanities. This research abstract features the abstracts from 71 PhD doctorates who will receive their scrolls in this 87th UiTM momentous convocation ceremony. To the 71 doctorates, you have most certainly done UiTM proud by journeying through the scholarly world with its endless challenges and obstacles, and by persevering right till the very end. Graduands, your success in achieving the highest academic qualification has demonstrated that you have indeed engineered your destiny well. The action of registering for a PhD program was not by chance but by choice. It was a choice made to realise your self-actualization level that is the highest level in Maslow’s Hierarchy of Needs, while at the same time unleashing your potential in scholarly research. Again, congratulations to all PhD graduates. As you leave the university as alumni we hope a new relationship will be fostered between you and the faculty in soaring UiTM to greater heights. I wish you all the best in your future endeavor. Keep UiTM close to your heart and be our ambassador wherever you go. / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR

Some Critical Issues for Injection Molding

This book is composed of different chapters which are related to the subject of injection molding and written by leading international academic experts in the field. It contains introduction on polymer PVT measurements and two main application areas of polymer PVT data in injection molding, optimization for injection molding process, Powder Injection Molding which comprises Ceramic Injection Molding and Metal Injection Molding, ans some special techniques or applications in injection molding. It provides some clear presentation of injection molding process and equipment to direct people in plastics manufacturing to solve problems and avoid costly errors. With useful, fundamental information for knowing and optimizing the injection molding operation, the readers could gain some working knowledge of the injection molding