AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

OPTIMIZATION OF CUTTING CONDITIONS FOR SUSTAINABLE MACHINING OF SINTERED POWDER METAL STEELS USING PCBN AND CARBIDE TOOLS

Powder metals are becoming a popular choice in the automotive and other manufacturing industries because of their ability to meet wide ranging product functional requirements without compromising the performance of the product. They offer various advantages, including weight reduction, near net-shape processing capability, and their ability to be sintered to achieve desired properties in the end-product. However, in order to satisfy the product design requirements during manufacturing, they need to be machined to the required tolerances. Machining of powder metals is quite different to machining of traditional metals because of their specific properties, including porosity. This thesis work deals with the finish machining of powder metal steels in automotive applications, for increased tool-life/reduced tool-wear. Tool-life is affected by a variety of factors such as tool grade selection, tool coating, cutting conditions and tool geometry including cutting edge geometry. This work involves optimization of cutting conditions for plunge cutting and boring operations of automotive powder metal components using PCBN and carbide tools. The cycle time of the process introduces an additional constraint for the optimization model along with the tool-wear criterion. Optimized cutting conditions are achieved for maximum tool-life

Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel

Surface roughness is an important quality in manufacturing, as it affects the product’s tribological, frictional and assembly characteristics. Turning stainless steel at low cutting speeds may result in a rougher surface due to built up edge formation, where as speed increases the surface roughness improves, due to the low contact time between the chip and the tool to allow bonding to occur.However, this increase in cutting speed produces higher tool wear rates, which increases the machining costs. Previous studies have indicated that savings in cost and manufacturing time are obtained when predicting the surface roughness, prior to the machining process. In this paper, experimental data are used to develop prediction models using Multiple Linear Regression and Artificial Neural Network methodologies. Results show that the neural network outperforms the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural Network model has been integrated with an optimisation algorithm, known as Simulated Annealing (SA),this is done in order to obtain a set of cutting parameters that result in low surface roughness. A low value of surface roughness and the set of parameters resulting on it, are successfully yielded by the SA algorithm

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Analysis of Vehicle Use Patterns during Military Field Exercises to Identify Potential Roads

Military training is an intensive land use and can cause negative environmental effects. Many studies conducted under Integrated Training Area Management (ITAM) for quantifying the impact resulted from the military training exercise found that off-road vehicular activities during training exercises cause the major impact to the training land. Vehicle land use patterns at a certain location affect the impact severity: concentrated and repeated traffic create more serious damage to the land compared to the dispersed offroad vehicle movements. Those areas heavily disturbed by off-road traffic may require a longer period of time or special treatments for the land to return to its pre-disturbed status. Based on the impact severity and the shape of the disturbed area, some areas can be considered as potential roads, defined as the roads newly formed by concentrated offroad traffic during the military training exercises, or the roads currently exist but have not been mapped. Potential roads need to be rehabilitated, have traffic dispersed to return the land to its natural status, or to be included in the established road construction and maintenance programs. As Global Positioning System (GPS) has been used for monitoring vehicles\u27 activities during military training exercises; it enables the analysis of vehicle movement patterns. The vehicle movement patterns are characterized as the percentage of vehicle travel every day, vehicles\u27 on and off road travel, the frequencies of vehicle\u27s off-road velocity and turning radius. GPS vehicle tracking data collected during an eight-day reconnaissance training exercises in Yakima Training Center (YTC) in October 2001 were analyzed for vehicle movement patterns. Comparison of the on-road and off-road movement patterns indicates that potential roads may exist on the locations where the concentrated traffic or a high speed movement occurred. Based on the analysis of the movement patterns, factors were extracted to characterize the special movement patterns that indicate the vehicles moved on a potential road. The YTC was divided into small study units, and a multicriteria method was developed to determine if a study unit is a portion of a potential road. The multicriteria method was evaluated by comparing the predictions to the site visit results on 34 selected road segments that met different criteria levels. Results show that locations met higher criteria levels have higher possibilities to be roads: the location met all five criteria has an approximately 91% possibility for road existence; those met four criteria has an approximately 55% possibility; and for those met criteria level two or three, there is an approximately 14% probability for road existence. The analysis of updated off-road shows the percentage of vehicle off-road movement drops from 20.0% to 15.8% after excluding the potential road moving data. As an alternative method, a neural network approach for identifying the potential roads was introduced and compared to the multicriteria method. The neural network method obtained an approximately 85% accuracy when tested by on-road grids, successfully identified the high-way segment as road, and predicted approximately 31% off-road grids as potential road grids. Results show that the neural network method, although emphasized in factors different from the multicriteria method, has approximately 78% accuracy for identifying the potential road locations. The prediction from the neural network method was found highly correlated to the one of the criterion: vehicles travel in different directions. Simplified methods were also developed to identify potential roads by investigating the GPS point density, vehicle velocity, and the number of passes within a study unit. A simple linear relationship was found between the number of passes and the possibility for road existence. Although using vehicle velocity for identifying the potential roads may not be the best choose, velocity is still considered as one of the most important features to characterize vehicle movements and to locate special movement patterns. Considering the discrete situation in the predicted potential road areas, a kernel smoothing technique was introduced and applied to smooth the results to improve the continuity of the potential roads. The application found the kernel smoothing technique was able to obtain continuous potential road grids by selecting reasonable bandwidth

A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions

Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. It implies that the proposed data-driven framework can lead to similar manufacturing decisions with reduced costs and time