Sustainable Manufacturing and Remanufacturing Management

In this chapter, introduction to sustainable manufacturing and remanufacturing management is given. For sustainable manufacturing, characteristics and technical trends for modern manufacturing processes, sustainable trend for product development lifecycle, and process planning for sustainable manufacturing are briefly discussed. For remanufacturing manufacturing, technologies of recycling and disassembly process planning for Waste Electric and Electronic Equipment (WEEE) are introduced. This chapter is the base for the following chapters of the book for detailed technology development.

Process planning that is based on environmental consciousness and energy-efficient scheduling currently plays a critical role in sustainable manufacturing processes. Despite their interrelationship, these two topics have often been considered to be independent of each other. It, therefore, would be beneficial to integrate process planning and scheduling for an integrated energy-efficient optimization of product design and manufacturing in a sustainable manufacturing system. This chapter proposes an energy-aware mathematical model for job shops that integrates process planning and scheduling. First, a mixed integrated programming model with performance indicators such as energy consumption and scheduling makespan is established to describe a multi-objective optimization problem. Because the problem is strongly non-deterministic polynomial-time hard (NP-hard), a modified genetic algorithm is adopted to explore the optimal solution (Pareto solution) between energy consumption and makespan. Finally, case studies of energy-aware integrated process planning and scheduling are performed, and the proposed algorithm is compared with other methods. The approach is shown to generate interesting results and can be used to improve the energy efficiency of manufacturing processes at the process planning and scheduling levels.

Process planning and scheduling are important stages in manufacturing, and good strategies can significantly improve the energy performance of manufacturing to achieve sustainability. In this paper, an innovative optimization approach has been developed to facilitate sustainable process planning and scheduling. In the approach, honeybee mating and annealing processes are simulated to optimize multi-objectives including energy consumption, makespan, and the balanced machine utilization. Experiments on practical cases show that the optimization results from this approach are promising in comparison with those from a genetic algorithm, a honeybee mating optimization algorithm, ant colony optimization, and a simulated annealing algorithm, respectively.

The implementation of sustainability in manufacturing companies, whose activities are usually characterized by high variety and low volume, has been crippled by the lack of effective process planning and scheduling solutions for sustainable management of manufacturing shop floors. To address the challenge, an innovative and systematic approach for machining process planning and scheduling optimization has been developed. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for machining a part are optimized adaptively to meet multiple objectives and constraints, i.e., energy efficiency of the machining process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features, operations and scheduling for producing multiple parts on different machines are optimized. Artificial neural networks are used for establishing the complex nonlinear relationships between the key process parameters and measured data sets of energy consumption and surface quality. Intelligent algorithms, including pattern search, genetic algorithm, and simulated annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potentials of research applicability in practice.

In the past, milling operations have been mainly considered from the economic and technological perspectives, while the environmental consideration has been becoming highly imperative nowadays. In this study, a systemic optimization approach is presented to identify the Pareto-optimal values of some key process parameters for low-carbon milling operation. The approach consists of the following stages. Firstly, regression models are established to characterize the relationship between milling parameters and several important performance indicators, i.e., material removal rate, carbon emission, and surface roughness. Then, a multi-objective optimization model is further constructed for identifying the optimal process parameters, and a hybrid NSGA-II algorithm is proposed to obtain the Pareto frontier of the non-dominated solutions. Based on the Taguchi design method, dry milling experiments on aluminum are performed to verify the proposed regression and optimization models. The experimental results show that a higher spindle speed and feed rate are more advantageous for achieving the performance indicators, and the depth of cut is the most critical process parameter because the increase of the depth of cut results in the decrease of the specific carbon emission but the increase of the material removal rate and surface roughness. Finally, based on the regression models and the optimization approach, an online platform is developed to obtain in-process information of energy consumption and carbon emission for real-time decision making, and a simulation case is conducted in three different scenarios to verify the proposed approach.

Modern manufacturing is challenged by customized, high-variety and low-volume job orders, dynamic ambient working conditions in shop floors, and stricter requirements on sustainability. Data-driven approach for manufacturing planning, control, and management will be a new research trend to tackle the challenge. In this research, based on in-process monitoring on energy consumption of machining processes and data analytics technologies, an innovative Cyber-physical system (CPS) and Big Data-enabled scheduling optimization system for sustainable computer numerical control (CNC) machining has been developed. This system is augmented with intelligent mechanisms for enhancing adaptability to condition dynamics in machining shop floors. The system consists of scheduling and re-scheduling functions. For scheduling, an artificial neural networks (ANNs)-based algorithm has been designed to establish energy models of components machined in a shop floor according to current working conditions of CNC machines. Based on the energy models, a fruit fly optimization (FFO) algorithm has been applied to generate a multi-objective optimized schedule. For re-scheduling, another ANNs-based algorithm has been developed to monitor the energy consumption of components during machining in real time. Scheduling optimization will be triggered to generate an updated schedule if there are significantly varying working conditions and re-scheduling adjustments are necessary. The system has been validated through deployment into a European machining company and industrial case studies to demonstrate technical innovations and the great potential of applicability in practice.

Computer numerical control (CNC) machining is one of the major manufacturing activities. It is imperative to develop energy-efficient CNC machining processes to achieve the overall goal of sustainable manufacturing. Due to the complexity of machining parameters, it is challenging to develop effective energy consumption modelling and optimization approaches to implement energy-efficient CNC machining. In this chapter, via experiments and qualitative analysis, the impact that key cutting parameters generates on energy consumption of milling processes on BS EN24T alloy (AISI 4340) has been conducted in detail. This facilitates machining process planners to choose suitable scopes of machining parameters, (e.g. cutting speed, feed per tooth, engagement depth) to improve energy efficiency. Based on the above, optimization has been carried out to formulate a multi-objective optimization model, and a novel-improved multi-swarm fruit fly optimization algorithm (iMFOA) has been developed to identify optimal solutions. Case studies and algorithm benchmarking have been conducted to validate the effectiveness of the optimization approach. The characteristics and novelty of the research include: (1) the relationships between energy consumption and key machining parameters have been analysed to support process planners in implementing energy saving measures efficiently; and (2) the optimization approach is effective in fine-tuning the key parameters for enhancing energy efficiency while meeting the dynamic production requirements.

NC programs are widely developed and applied to various machining processes. However, the lack of effective NC program optimization strategy for the machining energy efficiency has been crippling the implementation of sustainability in companies. To address this issue, a multi-granularity NC program optimization approach for energy efficient machining has been developed and presented in this paper. This approach consists of two levels of granularities: the granularity of a group of NC programs for a setup where the features are machined on a single CNC machine with the same fixture and the granularity of a NC program. On the former level of granularity, the execution sequence of the NC programs for the setup of a part is optimized to reduce the energy consumed by the cutting tool change among the NC programs. On the latter level of granularity, the execution sequence of the features in the same NC program is optimized to reduce the energy consumed by the cutting tool’s traveling among the machining features. Experiments on the practical cases show that the optimization results from this approach are promising and the approach has significant potential of applicability in practice.

Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine, and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives: energy efficiency, robustness, and makespan, and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exist a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

Sustainable management ofWaste Electrical and Electronic Equipment (WEEE) has attracted escalating concerns of researchers and industries. Closer information linking among the participants in the products’ lifecycle should take place. How to interoperate among the distributed and heterogeneous information systems of various participants is a challenge faced. Targeting thecloud-based remanufacturing, this article aims to develop a semantic information services framework for sustainable WEEE management. In the proposed framework, an ontology-based approach is developed to integrate and represent the lifecycle information from multiple local data sources within an information services provider. Meanwhile, a semantic information services management platform is introduced for the advertisement, matchmaking, and retrieval of semantic information services. Some relevant techniques used to build the framework are introduced extensively. A demonstration case study on waste LCD TV is used to illustrate the effectiveness and significance of the proposed framework.

Waste Electrical and Electronic Equipment (WEEE) is one of the most significant waste streams in modern societies. In the past decade, disassembly of WEEE to support remanufacturing and recycling has been growingly adopted by industries. With the increasing customization and diversity of Electrical and Electronic Equipment (EEE) and more complex assembly processes, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritizes operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important but still challenging research topic in recent years. In order to address the issue effectively, in this chapter, a particle swarm optimization (PSO)-based selective disassembly planning method embedded with customizable decision-making models and a novel generic constraint handling algorithm has been developed. With multi-criteria and adaptive decision-making models, the developed method is flexible to handle WEEE to meet the various requirements of stakeholders. Based on the generic constraint handling and intelligent optimization algorithms, the developed research is capable to process complex constraints and achieve optimized selective disassembly plans. Industrial cases on liquid crystal display (LCD) televisions have been used to verify and demonstrate the effectiveness and robustness of the research in different application scenarios.

Waste Electrical and Electronic Equipment (WEEE) is one of the major waste streams in terms of quantity and toxicity, and a critical step in WEEE end-of-life (EOL) processing is through disassembly. Compared with full disassembly, which is a sub-optimal solution due to its high operational cost, selective disassembly is more economic and practical as only selected parts with recycling potential are considered. In this paper, a systematic selective disassembly approach for handling WEEE with a maximum disassembly profit in accordance with the WEEE and Restriction of Hazardous Substances (ROHS) Directives has been developed. Firstly, a space interference matrix is generated based on the interference relationship between individual components in the 3D CAD model of WEEE. A matrix analysis algorithm is then applied to obtain all the feasible disassembly sequences. Secondly, an evaluation and decision-making method is developed to find out an optimal selective disassembly sequence from the obtained feasible disassembly sequences. The evaluation takes into account the disassembly profit and requirements of the WEEE and ROHS Directives, which regulate on recycling rates of different types of products and removal requirements of (i) hazardous, (ii) heavy, and (iii) high-value components. Thus, an optimal solution is a selective disassembly sequence that can achieve the maximum disassembly profit, while complying with the WEEE and ROHS restrictions based on a brute-force search method. Finally, an industrial case on Changhong Liquid Crystal Display Televisions (LCD-TVs) of the type LC24F4 is used to demonstrate the effectiveness of the developed approach.

Disassembly sequence planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, teaching-learning-based optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This chapter presents a simplified teaching-learning-based optimization (STLBO) algorithm to solve DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching-learning-based evolutionary mechanism from the TLBO algorithm, while the realization method of the evolutionary mechanism and the adaptation methods of the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a feasible solution generator (FSG) used to generate a feasible disassembly sequence, a teaching phase operator (TPO), and a learning phase operator (LPO) used to learn and evolve the solutions toward better ones by applying the method of precedence preservation crossover operation. Numerical experiments and case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks.