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

The fuzzy-nets based approach in predicting the cutting power of end milling operations

Process planning is a major determinant of manufacturing cost. The selection of machining parameters is an important element of process planning. The development of a utility to show the cutting power on-line would be helpful to programmers and process planners in selecting machining parameters. The relationship between the cutting power and the machining parameters is nonlinear. Presently there is no accurate or simple algorithm to calculate the required cutting power for a selected set of parameters. Although machining data handbooks, machinability data systems, and machining databases have been developed to recommend machining parameters for efficient machining, they are basically for general reference and hard to use as well;In this research, a self-organizing fuzzy-nets optimization system was developed to generate a knowledge bank that can show the required cutting power on-line for a short length of time in an NC verifier. The fuzzy-nets system (FNS) utilizes a five-step self-learning procedure. A generic FNS program consisting of fuzzification and defuzzification modules was implemented in the C++ programming language to perform the procedure. The FNS was assessed before an actual experiment was set up to collect data;The performance of the FNS was then examined for end milling operations on a Fadal VMC40 vertical machining center. The cutting force signals were measured by a three-component dynamometer mounted on the table of the Fadal CNC machine with the workpiece mounted on it. Amplified signals were collected by a personal computer on which an Omega DAS-1401 analog-to-digital (A/D) converter was installed to sample the data on-line. Data sets were collected to train and test the system. The results showed that the FNS possessed a satisfactory range of accuracy with the intended applications of the model. The values of cutting power predicted by the FNS were more accurate than the formula values. Compared to the FNS system, dynamometers and amplifiers are very expensive. Thus, most of them could be replaced with the FNS

Optimization of Manufacturing Production and Process

This chapter mainly introduces production processing optimization, especially for machining processing optimization on CNC. The sensor collects the original vibration data in time domain and converts them to the main feature vector using signal processing technologies, such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet packet in the 5G AI edge computing. Subsequently, the main feature will be sent for cloud computing using genetic programming, Space Vector Machine (SVM), etc. to obtain optimization results. The optimization parameters in this work include machining spindle rotation velocity, cutting speed, and cutting depth, while, the result is the optimized main spindle rotation speed range of CNC, which met machining roughness requirements. Finally, the relationship between vibration velocity and machining quality is further studied to optimize the three operational parameters

Monitoring a diagnosis for control of an intelligent machining process

A multi-level modular control scheme to realize integrated process monitoring, diagnosis and control for intelligent machining is proposed and implemented. PC-based hardware architecture to manipulate machining process cutting parameters, using a PMAC interface card as well as sensing processes performance parameters through sampling, and processing by means of DSP interface cards is presented. Controller hardware, to interface the PC-based PMAC interface card to a machining process for the direct control of speed, feed and depth of cut, is described. Sensors to directly measure on-line process performance parameters, including cutting forces, cutting sound, tool-workpiece vibration, cutting temperature and spindle current are described. The indirect measurement of performance parameter surface roughness and tool wear monitoring, through the use of NF sensor fusion modeling, is described and verified. An object based software architecture, with corresponding user interfaces (using Microsoft Visual C++ Foundation Classes and implemented C++ classes for sending motion control commands to the PMAC and receiving processed on-line sensor data from the DSP) is explained. The software structure indicates all the components necessary for integrating the monitoring, diagnosis and control scheme. C-based software code executed on the DSP for real-time sampling, filtering and FFT processing of sensor signals, is explained. Making use of experimental data and regression analysis, analytical relationships between cutting parameters (independent) and each of the performance parameters (dependent) are obtained and used to simulate the machining process. A fuzzy relation that contains values determined from statistical data (indicating the strength of connection between the independent and dependent variables) is proposed. The fuzzy relation forms the basis of a diagnostic scheme that is able to intelligently determine which independent variable to change when a machining performance parameter exceeds control limits. The intelligent diagnosis scheme is extensively tested using the machining process simulation

Application of ”ART SIMULATOR” for Manufacturing Similarity Identification in Group Technology Design - Chapter 10

This chapter 10 carried out the exceptional implementation of ART-1 neural network in the analysis of the manufacturing similarity of the cylindrical parts within the group technology design. Established concept of the group technology design begins from the complex part of the group or the group representative. Group representative has all the geometrical elements of the parts in group, and manufacturing procedure may be applied to the machining of any part in the group. The complex part may be realistic or a hypothetical one. The ART-1 artificial neural network provided manufacturing classification according to the geometrical similarities of work-pieces for the group of cylindrical parts. For the manufacturing similarity identification within the group technology design, software package "ART Simulator" is developed and presented in this chapter

DIG-MAN: Integration of digital tools into product development and manufacturing education

General objectives of PRODEM education. Teaching of product development requires various digital tools. Nowadays, the digital tools usually use computers, which have become a standard element of manufacturing and teaching environments. In this context, an integration of computer-based technologies in manufacturing environments plays the crucial and main role, allowing to enrich, accelerate and integrate different production phases such as product development, design, manufacturing and inspection. Moreover, the digital tools play important role in management of production. According to Wdowik and Ratnayake (2019 paper: Open Access Digital Tool’s Application Potential in Technological Process Planning: SMMEs Perspective, https://doi.org/10.1007/978-3-030-29996-5_36), the digital tools can be divided into several main groups such as: machine tools and technological equipment (MTE), devices (D), internet(intranet)-based tools (I), software (S). The groups are presented in Fig. 1.1. Machine tools and technological equipment group contains all existing machines and devices which are commonly used in manufacturing and inspection phase. The group is used in physical shaping of manufactured products, measurement tasks regarding tools and products, etc. The next group of devices (D) is proposed to separate the newest trends of using mobile and computer-based technologies such as smartphones or tablets and indicate the necessity of increased mobility within production sites. The similar need of separation is in the case of internet(intranet)-based tools which indicate the growing interest in network-based solutions. Hence, D and I groups are proposed in order to underline the significance of mobility and networking. These two groups of the digital tools should also be supported in the nearest future by the use of 5G networks. The last group of software (S) concerns computer software produced for the aims of manufacturing environments. There is also a possibility to assign the defined solutions (e.g. computer programs) to more than one group (e.g. program can be assigned to software and internet-based tools). The main role of tools allocated inside separate groups is to support employees, managers and customers of manufacturing firms focused on abovementioned production phases. The digital tools are being developed in order to increase efficiency of production, quality of manufactured products and accelerate innovation process as well as comfort of work. Nowadays, digital also means mobile. Universities (especially technical), which are focused on higher education and research, have been continuously developing their teaching programmes since the beginning of industry 3.0 era. They need to prepare their alumni for changing environments of manufacturing enterprises and new challenges such as Industry 4.0 era, digitalization, networking, remote work, etc. Most of the teaching environments nowadays, especially those in manufacturing engineering area, are equipped with many digital tools and meet various challenges regarding an adaptation, a maintenance and a final usage of the digital tools. The application of these tools in teaching needs a space, staff and supporting infrastructures. Universities adapt their equipment and infrastructures to local or national needs of enterprises and the teaching content is usually focused on currently used technologies. Furthermore, research activities support teaching process by newly developed innovations. Figure 1.2 presents how different digital tools are used in teaching environments. Teaching environments are divided into four groups: lecture rooms, computer laboratories, manufacturing laboratories and industrial environments. The three groups are characteristic in the case of universities’ infrastructure whilst the fourth one is used for the aims of internships of students or researchers. Nowadays lecture rooms are mainly used for lectures and presentations which require the direct communication and interaction between teachers and students. However, such teaching method could also be replaced by the use of remote teaching (e.g. by the use of e-learning platforms or internet communicators). Unfortunately, remote teaching leads to limited interaction between people. Nonverbal communication is hence limited. Computer laboratories (CLs) usually gather students who solve different problems by the use of software. Most of the CLs enable teachers to display instructions by using projectors. Physical gathering in one room enables verbal and nonverbal communication between teachers and students. Manufacturing laboratories are usually used as the demonstrators of real industrial environments. They are also perfect places for performing of experiments and building the proficiency in using of infrastructure. The role of manufacturing labs can be divided as: • places which demonstrate the real industrial environments, • research sites where new ideas can be developed, improved and tested. Industrial environment has a crucial role in teaching. It enables an enriched student experience by providing real industrial challenges and problems

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed